d3.2.1 guidelines for enhancing communication networks for...

SUNSEED, Grant agreement No. 619437 of 96

D3.2.1 Guidelines for enhancing

communication networks for real-time smart grid control

Deliverable report


NOTICE The research leading to the results presented in the document has received funding from the European Community's Seventh Framework Programme under Grant agreement number 619437. The content of this document reflects only the authors’ views. The European Commission is not liable for any use that may be made of the information contained herein. The contents of this document are the copyright of the SUNSEED consortium.


Document Information

1 PU Public

RP Restricted to other programme participants (including the Commission Services)

RE Restricted to a group specified by the consortium (including the Commission Services)

CO Confidential, only for members of the consortium (including the Commission Services)

Call identifier FP7-ICT-2013-11

Project acronym SUNSEED

Project full title Sustainable and robust networking for smart electricity distribution

Grant agreement number 619437

Deliverable number D3.2.1

WP / Task WP3 / T3.2

Type (distribution level)1 PU

Due date of deliverable 31.08.2015 (Month 19)

Date of delivery 28.08.2015

Status, Version Final, V1.0

Number of pages 96 pages

Responsible person, Affiliation Jimmy Jessen Nielsen, AAU

Authors Jimmy Jessen Nielsen, Germán Corrales Madueño, AAU

Ljupco Jorguseski, Haibin Zhang, Onno Mantel, Michal Golinski, TNO

Manolis Chrysallos, TNO

Ziming Zhu, TREL

Reviewers Marko Pesko, TS

Richter Christian, Gemalto M2M

Guidelines for enhancing communication networks for real-time smart grid control. Version 1.0


Revision history Version Date Author(s) Notes Status

1.0 26.8.2015 Jimmy Nielsen, AAU All changes accepted, final check and fixing of cross-references.

Final

0.42 25.8.2015 Jimmy Nielsen, Germán Madueño, AAU, Ljupco Jorguseski, TNO

Update of section 3.2. Fixing broken references. Moving citations to back of deliverable. Updated sections 2.1 and 2.2 with conclusions and new results. Summary of findings added to main conclusions.

Draft

0.41 19.8.2015 Jimmy Nielsen, AAU Update of section 5.1 to fix broken references and general improvements to text. Added conclusion text. Still missing TNO input for conclusion.

Draft

0.40 18.8.2015 Jimmy Nielsen, AAU, Ziming Zhu, TREL.

Merged TREL updates. Draft

0.39 18.8.2015 Jimmy Nielsen, AAU Finished introduction, added more details to LTE ARP model.

Draft

0.38 14.8.2015 Jimmy Nielsen, AAU Ljupco Jorguseski, TNO

Reorganized content in chapters. Added introductory content. Fixed broken references and citations. Merged TNO’s updates.

Draft

0.36 5.8.2015 Jimmy Nielsen, AAU Reorganized content in chapters, fixed broken references in 2 of AAU’s contributions.

Draft

0.34 14.7.2015 Ljupco Jorguseski, TNO, Jimmy Nielsen, AAU, Ziming Zhu, TREL

Added capacity vs coverage trade-off section 3.4, updated section LTE Delay-based capacity section, and added 802.11ah section

Draft

0.33 19.6.2015 Jimmy Nielsen, AAU Added Multi-interface transmissions draft. Draft

0.32 15.6.2015 Ljupco Jorguseski, TNO Chapter 3 LTE Coverage and Chapter 4 LTE Capacity added

Draft

0.3 10.6.2015 Jimmy Nielsen, AAU Draft input added for AAU parts Draft

0.2 29.5.2015 Jimmy Nielsen, AAU Adding ToC contents Draft

0.1 18.5.2015 Jimmy Nielsen, AAU Document outline notes Draft



Table of Contents

Table of Figures.......................................................................................................................................... 6

Table of Acronyms ..................................................................................................................................... 8

SUNSEED project ...................................................................................................................................... 10

Executive Summary .................................................................................................................................. 11

1 Introduction ...................................................................................................................................... 13

1.1 SUNSEED WAN/NAN Network Architectures ......................................................................... 13

1.2 Approach and Relation to the Rest of the Project ................................................................... 14

1.3 Outline of This Report .............................................................................................................. 14

1.4 Random Access in LTE networks ............................................................................................ 15

2 Analysing and Enhancing the Capacity of LTE ............................................................................... 18

2.1 LTE Coverage Assessment and Enhancements ..................................................................... 18

2.2 LTE Delay-Based Capacity Assessment ................................................................................. 31

2.3 GPRS and LTE Random Access Capacity Assessment ......................................................... 41

2.4 Model Based Estimation of LTE Random Access Capacity .................................................... 49

3 Co-existence of M2M and non-M2M devices in Cellular Networks ................................................ 55

3.1 Cellular M2M Network Access Congestion: Performance Analysis and Solutions ................. 55

3.2 Massive M2M Access with Reliability Guarantees in LTE Systems ........................................ 60

4 Analysis and Enhancement of Neighbourhood Area Networks ...................................................... 66

4.1 Performance study of IEEE 802.11ah Wi-Fi network for smart grid applications .................... 66

5 Ultra-Reliable Smart Grid Communication ...................................................................................... 70

5.1 Reliable Reporting in LTE with Periodic Resource Pooling ..................................................... 70

5.2 Multi-Interface Transmissions for Ultra-High Reliability ........................................................... 74

6 Conclusions ..................................................................................................................................... 84

7 References ...................................................................................................................................... 86

Appendix A: Relation between capacity and coverage improvement ................................................... 92

Appendix B: Additional LTE simulation results ...................................................................................... 94



Table of Figures

Figure 1: Cellular smart grid network. .................................................................................................................. 13 Figure 2: Neighborhood Area Network. ................................................................................................................ 14 Figure 3: Message exchange between a device and the eNodeB during the LTE random access procedure. ..... 15 Figure 4: Flow diagram of LTE access reservation protocol: (a) one-shot transmission model; (b) m-Retransmissions model (dashed lines). ................................................................................................................. 16 Figure 5: Schematic illustration of the repetition technique. ............................................................................... 19 Figure 6: BLER of system information blocks for different repetitions [42] ......................................................... 20 Figure 7: TTI bundling with 4 consecutive TTIs ..................................................................................................... 21 Figure 8: Example of PSD boosting for PBCH signal in a bandwidth of 10 MHz. Reproduction from [45] ............ 22 Figure 9: PBCH coverage extension using PSD boosting in a 10 MHz bandwidth, from [45] ................................ 23 Figure 10: PSD boosting & repetition, from [40] ................................................................................................... 23 Figure 11: Example of inter-subframe frequency hopping. Different colors represent different UEs. ................ 24 Figure 12: Effect of coverage enhancement options on cell radius for three environment types. ...................... 26 Figure 13: Relation between optimal link budget increase and ratio max/node. ................................................. 29 Figure 14 Illustration of the ADNMSP approach in the electricity distribution grid, reproduction from [60] ...... 32 Figure 15 Illustration of the uplink transmission in LTE and the two delay components dradio and dcore. The user equipment (UE) is the 3GPP term for end terminals ............................................................................................ 34 Figure 16: HARQ process, from [72] ...................................................................................................................... 36 Figure 17: CDFs of WAMS delay performance for 6 PRBs: (a) the influence of number of users per cell on the delay CDFs;), (b) the influence of random and TTI based scheduling; (c) the influence of the radio propagation environment; (d) the influence of different number of PRBs per UE ................................................................... 39 Figure 18: CDFs of SM delay performance for 6 PRBs (a) the influence of number of users per cell on the delay CDFs; (b) the influence of random and TTI based scheduling; (c) the influence of the environment; (d) the influence of different number of PRBs per UE ...................................................................................................... 40 Figure 19: Classification of OpenSmartGrid traffic originating from an SM. λ-values show the number of generated messages per day per device. Use case short names: Demand Response - Direct Load Control (DR-DLC), Premise Network Administration (PNA), Firmware and Software updates (FW/SW upd.), Real-Time Price (RTP), Islanded Distributed Customer Storage (IDCS). .......................................................................................... 43 Figure 20: GPRS outage evaluation for increasing number of SM with different report interval values and RS = 300 bytes for residential and RS = 600 bytes for commercial/industrial, where ARP+D denotes the access reservation protocol plus data phase, while D denotes only data phase. ............................................................ 45 Figure 21: LTE and GPRS outage evaluation for increasing penetration of WAMS-SPMs, where ARP+D denotes the access reservation protocol plus data phase, while D denotes only data phase. .......................................... 46 Figure 22: Markov Chain backoff model to estimate number of required transmissions. The equilibrium states in the red dashed box are used to calculate the mean number of required transmissions. ................................ 51 Figure 23: Plots for RACH configuration with 2 RAOs per frame (δRAO = 5). Ericsson model refers to [27]...... 53 Figure 24: Plots for RACH configuration with 10 RAOs per frame (δRAO = 1). Ericsson model refers to [27].... 53 Figure 25: Non-M2M throughput vs. number of M2M users ............................................................................... 57 Figure 26: Random access with transmission probability control ........................................................................ 58 Figure 27: A two-hop M2M network .................................................................................................................... 59 Figure 28: (a) Delay vs. number of users (b) Throughput per opportunity vs. number of users .................. 60 Figure 29: Proposed access frame consisting of an estimation RAO followed by S≤L-1 serving RAOs. ................ 61 Figure 30: Proposed estimator performance when the expected arrival rate is not a priori known. Through exhaustive numerical search it was found that for a dynamic range between N=[1,30000] the optimal values of the estimator parameters are p0=0.001 andα=1.056. .......................................................................................... 62 Figure 31: Proposed access scheme for two traffic classes TC1 and TC2, where TC1 has priority over TC2. ....... 63 Figure 32: Achievable transient R(1) within the access frame by the legacy and access frame solutions, with N2 = 10k and Rreq(1) = Rreq(2) = 0.99. ........................................................................................................ 64 Figure 33: Network topologies. (Left: 1 AP, 30 STAs; Right: 1 AP, 1080 STAs) ...................................................... 68 Figure 34: Collision and failure rates vs number of STAs per group ..................................................................... 69



Figure 35: Representation of the LTE uplink resource structure, where a set of RBs has been reserved for M2M purposes. ............................................................................................................................................................... 71 Figure 36: a) Periodically occurring M2M resource pool. b) Division of M2M resource pool in the pre-allocated and common pool. ................................................................................................................................................ 72 Figure 37: Comparison of simulated and analytical PDF and CDF of R when N = 100 and L = 10. ................... 73 Figure 38: Fraction of system capacity used for M2M services, when P[Φ] ≤ 10-3, RI of 1 minute, RS of 100

bytes, bandwidth of 5 MHz and pe = 10-1. ......................................................................................................... 73 Figure 39: Fraction of system capacity used for M2M services, when P[Φ] ≤ 10-3, RI of 1 minute, bandwidth of

5 MHz, 64-QAM and pe = 10-1. .......................................................................................................................... 74 Figure 40: Example of latency-reliability CDF of a network interface (blue curve). The red dash-dotted line and the black dashed line indicate the obtainable reliability for latency requirements 25 ms and 50 ms, respectively. .............................................................................................................................................................................. 76 Figure 41: State-transition diagram of the continuous time Markov chain that represents the case of dependent LTE (4G) and GPRS (2G) connection options. The color of a state indicates the level of degraded service. ........ 77 Figure 42: Considered network architecture for independent and partially dependent technologies. ............... 78 Figure 43: Latency-reliability curves of considered technologies. ........................................................................ 78 Figure 44: State-transition diagram of triple-redundant system with dependencies. The color of a state indicates the level of degraded service. ................................................................................................................ 80 Figure 45: Latency-reliability curves for the considered multi-interface transmission methods. ........................ 82 Figure 46: Original cell radius and radius after the coverage enhancement. ....................................................... 92 Figure 47: CDFs of WAMS delay performance for 50 PRBs: (a) the influence of number of users per cell on the delay CDFs; (b) the influence of random and TTI based scheduling; (c) the influence of the environment; (d) the influence of different number of PRBs per UE ...................................................................................................... 94 Figure 48: CDFs of SM delay performance for 50 PRBs: (a) the influence of number of users per cell on the delay CDFs; (b) the influence of random and TTI based scheduling; (c) the influence of the environment; (d) the influence of different number of PRBs per UE ...................................................................................................... 95 Figure 49: CDFs of WAMS and SM delay performance for 1/6 WAMS to SM ratio: (a) the influence of number of users per cell on the WAMS delay CDFs; (b) the influence of number of users per cell on the WAMS delay CDFs; (c) the influence of number of users per cell on the SM delay CDFs; (d) the influence of number of users per cell on the SM delay CDFs ........................................................................................................................................... 96



Table of Acronyms

Acronym Description

3GPP 3rd Generation Partnership Project

ACB Access Class Barring

ACK/NACK Acknowledgement / Negative Acknowledgement

ADNMSP Advanced Distribution Network Management System Platform

AG Access Granting

AP Access Point

API Application Programming Interface

ARP Access Reservation Protocol

BLER Block Error Rate

BPSK Binary Phase Shift Keying

CDF Cumulative Density Function

CIM Common Information Model

CRC Cyclic Redundancy Check

CSMA Carrier Sense Multiple Access

CSMA/CA Carrier Sense Multiple Access Collision Avoidance

dB Decibel

DER Distributed Energy Resources

DSO Distribution System Operator

DTX Disrupted Transmission

EAB Extended Access Barring

FFA Fair Fixed Assignment

H2H Human-to-human

H2x Human-type communications

HAN Home Area Network

HARQ Hybrid Automatic Repeat Request

IoT Internet of Things

M2M Machine-to-Machine

MAC Medium Access Control

MCS Modulation and Coding Scheme

MIMO Multiple Input Multiple Output

MTC Machine Type communication

MTD Machine Type Device

MTTR Mean Time to Restoration

NAN Neighbourhood Area Network

OFDM Orthogonal Frequency Division Multiplexing

PDF Probability Density Function

PRACH Physical Random Access Channel

PSD Power Spectral Density

PDSCH Physical Downlink Shared Channel



PHICH Physical Hybrid-Automated-Repeat-Request Indication Channel

PHY Physical

PLC Power Line Communications

PRACH Physical Random Access Channel

PRAW Periodic Restricted Access Window

PRB Physical Resource Block

PSS Primary Synchronization Signal

PUSCH Physical Uplink Shared Channel

QoS Quality of Service

RAO Random Access Opportunity

RAR Random Access Response

RAW Restricted Access Window

RB Resource Block

RI Reporting Interval

RLC Radio Link Control

RV Redundancy Version

SI System information

SIB System information Message

SM Smart Meter

SSS Secondary Synchronization Signal

STA Station

TIM Traffic Indication Map

TPC Transmission Probability Control

TTI Transmit Time Interval

TXOP Transmit Opportunity

UE User Equipment

VPP Virtual Power Plant

W3C World Wide Web Consortium

WAMS Wide Area Measurement System

WAMS-SPM WAMS with Synchro-Phasor Measurement capability

WAN Wide Area Network

WP Work Package

XML Extensible Markup Language



SUNSEED project

SUNSEED proposes an evolutionary approach to utilisation of already present communication networks from

both energy and telecom operators. These can be suitably connected to form a converged communication

infrastructure for future smart energy grids offering open services. Life cycle of such communication network

solutions consists of six steps: overlap, interconnect, interoperate, manage, plan and open. Joint

communication networking operations steps start with analysis of regional overlap of energy and

telecommunications operator infrastructures. Geographical overlap of energy and communications

infrastructures identifies vital DSO energy and support grid locations (e.g. distributed energy generators,

transformer substations, cabling, ducts) that are covered by both energy and telecom communication

networks. Coverage can be realised with known wireline (e.g. copper, fiber) or wireless and mobile (e.g. Wi-Fi,

4G) technologies. Interconnection assures end-2-end secure communication on the physical layer between

energy and telecom, whereas interoperation provides network visibility and reach of smart grid nodes from

both operator (utility) sides. Monitoring, control and management gathers measurement data from wide area

of sensors and smart meters and assures stable distributed energy grid operation by using novel intelligent real

time analytical knowledge discovery methods. For full utilisation of future network planning, we will integrate

various public databases. Applications build on open standards (W3C) with exposed application programming

interfaces (API) to 3rd parties enable creation of new businesses related to energy and communication sectors

(e.g. virtual power plant operators, energy services providers for optimizing home energy use) or enable public

wireless access points (APs) (e.g. Wi-Fi nodes at distributed energy generator locations). SUNSEED life cycle

steps promise much lower investments and total cost of ownership for future smart energy grids with dense

distributed energy generation and prosumer involvement.

Project Partners

1. TELEKOM SLOVENIJE, D.D.; TS; Slovenia

2. AALBORG UNIVERSITET; AAU; Denmark

3. ELEKTRO PRIMORSKA, PODJETJE ZA DISTRIBUCIJO ELEKTRICNE ENERGIJE, D.D.; EP; Slovenia

4. ELEKTROSERVISI, ENERGETIKA, MERILNI LABORATORIJ IN NEPREMICNINE, D.D.; ES; Slovenia

5. INSTITUT JOZEF STEFAN; JSI; Slovenia

6. GEMALTO SA; GTOSA; France

7. GEMALTO M2M GMBH; GTOM2M; Germany

8. NEDERLANDSE ORGANISATIE VOOR TOEGEPAST NATUURWETENSCHAPPELIJK ONDERZOEK-TNO; TNO; The

Netherlands

9. TOSHIBA RESEARCH EUROPE LIMITED; TREL; United Kingdom

Project webpage http://www.sunseed-fp7.eu/

http://www.sunseed-fp7.eu/



Executive Summary

In SUNSEED, the primary communication technology to be used in the wide area and neighbourhood area network domains is LTE. Therefore, most of the guidelines for enhancing communication networks for real-time smart grid control considered in this deliverable are targeting LTE. Initially, in Chapter 2, we focus on the capacity of LTE when it is being used for M2M communication such as real-time smart grid communication. A key insight of the researches is that the capacity of an LTE cell covers many different aspects. For example: How do the propagation conditions of a specific scenario and the used scheduling algorithm influence the experienced transmission delay? The delay requirements of, e.g. WAMS-SPM nodes thereby puts a limit on the capacity. An analysis of coverage enhancement techniques discussed in 3GPP indicates a potential to increase the link budget by 10-20 dB, leading to a factor 2-4 increase in cell radius. However, it is important to also consider the capacity effects of coverage enhancement techniques since the effect of these techniques is that a) on average more radio resources are needed per device and b) more devices must be supported in the larger cells. An analytical study shows that the highest gains (up to 10 dB) of LTE coverage enhancement techniques are expected for higher frequencies and urban areas, while in rural areas the gain will be limited due to the capacity constraints. The delay of the LTE uplink transmission for the WAMS-SPM and smart meter (SM) nodes is mainly determined by the number of nodes in the cell, the allocated bandwidth, and (especially for higher number of nodes) the scheduling approach. The radio propagation environment (e.g. urban, sub-urban, rural) and the bandwidth (in PRBs) allocated per node do not have significant impact on the delay performance. For a given desired 95% delay requirement of e.g. 1 s the LTE cell capacity is in the range of 500 nodes (6 PRBs available) to 5000 nodes (50 PRBs available). The TTI based scheduling is preferred as it improves the 95% delay statistics for WAMS-SPM nodes without large impact on the delay for the SM nodes. Future delay based capacity investigations will focus on other radio access technologies (e.g. GSM, UMTS, etc.) and also heterogeneous deployments of e.g. RF mesh with gateways towards a cellular network. Other researches reveal that the access reservation protocol used in LTE for sporadic uplink transmissions, e.g. for smart meter and WAMS-SPM node reporting, is often the limiting factor in M2M communication long before the data resources are exhausted. Specifically we show show that even GPRS can support traditional SM traffic, as well as more frequent measurements down to 5 min report intervals. Further, it is shown that LTE can support WAMS-SPM nodes, however with potentially large use of bandwidth (up to 10 MHz), unless the report payloads are appropriately dimensioned by measurement down-selection and/or applying compression techniques compared to traditional PMU measurement traffic characteristics. In addition to this specific study, we have proposed a mathematical model that allows evaluating the capacity of an LTE network at click-speed, in terms of access reservation protocol limitations for a given network configuration. In Chapter 3 we consider the co-existence of machine-to-machine (M2M) and human-type communications in cellular networks. This co-existence can be problematic, since intermittent bursts in human-type traffic can lead to overload situations where the strict real-time and reliability requirements of M2M traffic cannot be satisfied. We have addressed this challenge first by studying the network access congestion problem. Two methods, namely transmission probability control and two-hop access Aloha, have been investigated as means to improve co-existence. Performance evaluation shows the proposed solutions can significantly improve the access delay and throughput of M2M applications. Second, we propose a proactive approach, based on dedicated access resources for the M2M traffic, combined with a novel frame based serving scheme composed by an estimation and a serving phase. In the estimation phase the volume of arrivals is estimated and then used to dimension the amount of resources in the serving phase, such that reliable service



guarantees are provided. The provided framework can be extended for more than two traffic classes, which is one of the future work directions. Following in Chapter 4 we bring a preliminary study on the upcoming long-range Wi-Fi protocol IEEE 802.11ah. This protocol is envisioned as a potential choice for complimenting cellular networks in areas with limited cellular coverage. The study presents initial simulation results that show the effectiveness of the station grouping method for supporting numerous transmission requests in a CSMA/CA based network. Finally, in Chapter 5 we study different methods to ensure high reliability in smart grid communication. First we consider how reliability guarantees can be given to uncoordinated transmissions from M2M devices, by introducing a contention-free allocation method for M2M that relies on a pool of resources reoccurring periodically in time. Promising results in the context of LTE show that the proposed resource pool based scheme uses much less resources than legacy random access in LTE. The proposed method can be applied to other systems, such as 802.11ah. Thereafter we consider the general problem of using multiple transmission interfaces and communication paths to enable redundant and highly reliable communication. We propose a method to evaluating the reliability of both independent (parallel) communication paths and partially dependent communication paths, e.g., in the case of shared base station tower for 4G and 2G technologies. The latter approach is modelled using Markov chains that describe the relations between the different system states. For the considered system model, which is based on reliability figures provided by TS, we find that the (naïve) parallel systems model, which assumes communication paths to be independent, results in an overly optimistic reliability result, which is one order of magnitude higher than when using the more realistic model that accounts for component dependencies.



1 Introduction

One of the big challenges in SUNSEED is to enable reliable real-time communication to smart meters and especially WAMS nodes being located in remote DSO and/or customer premises where wired connectivity is not already established or a backup connection is needed. As mentioned in D3.1, the first choice of communication technology for these devices in the Wide Area Network (WAN) /Neighbourhood Area Network (NAN) domain is LTE, since it is already widely rolled out and is expected to be capable of supporting the traffic requirements of smart meters and WAMS nodes, as shown in D3.1.

1.1 SUNSEED WAN/NAN Network Architectures

With LTE being the first choice in communication technology for the WAN/NAN domain, the majority of contributions in this deliverable are concerning cellular access using LTE. Specifically, we are targeting the type of network architecture outlined in Figure 1, where base stations (eNodeBs) are covering different areas/neighborhoods. Within the coverage area of each eNodeB, there is a number of customer premises (residential, commercial, or industrial) with smart meters (SMs) and in some cases WAMS nodes with synchro-phasor measurement capabilities (WAMS-SPM). The WAMS-SPMs are typically installed in premises with Distributed Energy Resources (DERs) such as photo-voltaics, electric vehicles, and wind turbines. The SMs and WAMS nodes are using the cellular network to communicate to the DSO back-end facilities. As shown in D3.1, the traffic requirements of SMs are very modest and can easily be handled by LTE and in typical configurations also by GPRS, whereas WAMS-SPM nodes are expected to be more challenging to support, even for LTE.

Figure 1: Cellular smart grid network.

In addition to studying cellular networks, this task also has a work item on the upcoming long-range Wi-Fi technology, denoted IEEE 802.11ah, which is intended for use in the NAN domain. This technology is investigated as an alternative to LTE, especially in areas where LTE cellular networks are not yet rolled out. A typical NAN architecture is shown in Figure 2.



Figure 2: Neighborhood Area Network.

1.2 Approach and Relation to the Rest of the Project

In this deliverable we address how different communication technologies can be enhanced to support real-time monitoring and control in smart grids with high concentration of Distributed Energy Resources (DER). The work takes a starting point in analysing the shortcomings and potential areas of improvement. Where applicable, the analyses of current and enhanced technologies will use the developed use cases from WP2 as well as the traffic models defined in WP3, as documented in D3.1. In some cases, the present deliverable D3.2.1 also presents the proposed enhancements, but since this deliverable is the intermediate deliverable of T3.2, some of the work is not yet completed and will therefore not be presented in full until the final deliverable D3.2.2. The presented analyses are conducted using the partners' own simulation tools and mathematical models, which are then parametrized according to the defined SUNSEED use cases and traffic models. Ideally, we would integrate all of the proposed enhancements in the field trial setup in WP5; however this is not practically feasible for the following reasons: 1) modified communications cannot be implemented and tested in TS' running public mobile network, since the operation of existing costumers may be disrupted; 2) in relation to the ultra-reliable communication, where the aim is to make the communication resilient to network failures that do not occur very often, simulations running faster than real-time or mathematical modeling may be the only feasible ways to ensure statistically sound evaluation results. Instead, selected proposed enhancements may be tried out in limited lab setups. A continuous dialog with WP4 ensures that the assumed traffic models reflect the insights gained on the requirements of especially the WAMS-SPM nodes, in terms of reporting frequency and payload sizes.

1.3 Outline of This Report

Several of the contributions in this deliverable are concerning the random access procedure in LTE. Therefore a common introduction to the principles of random access in LTE is given first, in section 1.4. Since machine-to-machine (M2M) applications, of which Smart Grid is a prime example, have very different traffic characteristics than human-oriented applications, conventional tools and



analysis techniques do not necessarily apply directly to Smart Grid systems. In chapter 2, we therefore analyse the capacity of cellular networks in terms of different performance aspects that are important to Smart Grid systems. Several performance enhancement techniques are also considered. Furthermore, since Smart Grid applications are intended to co-exist with human-type applications, i.e., share the same cellular networks, we investigate in chapter 3 the potential problems of this and propose solutions to avoid adverse affects of co-existence on the Smart Grid applications. In chapter 4 we consider the upcoming long-range Wi-Fi technology 802.11ah, which may be useful to support Smart Grid applications in areas with limited cellular coverage. The last technical contributions that focus on methods to improve reliability of communications are presented in chapter 5. The deliverable wraps up with conclusions in chapter 6.

1.4 Random Access in LTE networks

Machine-Type Communication (MTC) is commonly characterized by a large number of cellular devices that are active sporadically, where a large number of devices may activate in a correlated way due to a sensed physical phenomenon (e.g., a power outage in the smart grid). In more traditional human-centric traffic where the associated payloads are relatively large, a small number of active devices can cause the network to become in outage mainly due to the lack of available resources for data transmission. In contrast, the associated payloads are relatively small in MTC such that the division of the aggregate available data rate with the small data rate required by each Machine-Type Devices (MTDs) leads to the conclusion that the system can support a vast number of MTDs. Recent studies such as [22] have shown that such a conclusion is misleading: the network still becomes in outage, not being able to provide access to the MTDs, despite plenty of available resources to support a massive number of MTDs. Here the culprit in the limited number of supported devices, is not the available resources as in human-centric traffic, but instead the bottlenecks in the access reservation protocol [22]. Specifically in LTE, the access reservation protocol that is outlined in Figure 3 has two limitations that unveil with MTC. The first is in MSG 1, where only a limited number of preambles can be used to signal a sporadic request for uplink resources to the eNodeB, in the RACH phase. The second is in MSG 2, where a bottleneck may be caused by the limited amount of feedback resources in the access-granting (AG) phase.

Figure 3: Message exchange between a device and the eNodeB during the LTE random access procedure.



Figure 4: Flow diagram of LTE access reservation protocol: (a) one-shot transmission model; (b) 𝑚-Retransmissions model (dashed lines).

1.4.1 System Model To explain the concept of random access in LTE, we focus on a single LTE cell, with 𝑁 MTDs, also called machine-type User Equipment (UE). We assume that the MTC applications associated with these MTDs, generate new uplink transmissions with an aggregate rate 𝜆I, as depicted in Figure 4.

That is, 𝜆I = ∑ 𝜆𝑖app𝐾

𝑖=1 , where 𝜆𝑖app

is the transmission generation rate of the ith out of K MTC

applications running on the UEs. We assume this aggregate rate follows a Poisson distribution with rate 𝜆I. For each new data transmission, up to 𝑚 retransmissions are allowed, resulting in a maximum of 𝑚 + 1 allowed transmissions. When these transmissions fail and retransmission occurs, then an additional stress is put on the access reservation protocol, since the retransmissions 𝜆R add to the total rate 𝜆T. Even though our model assumes Poisson arrivals, here for analytical simplification, it can also be used to model massive arrivals. The justification for this is that although the massive arrivals occur in burst, they do so within an interval [31], within which the process can be assumed to be stationary. As shown in Figure 4, we split the access reservation model into two parts: (i) the one-shot transmission part in Figure 4 (a) that models the bottlenecks at each stage of the access reservation protocol; (ii) the 𝑚-retransmission part in Figure 4 (b), where finite number of retransmissions and backoffs are modelled. We focus our analysis on MTC, for which the traffic is characterized by having a very small payload. Therefore, in the one-shot transmission, depicted in Figure 4 (a), we assume that the RACH and access granting phases are the system bottlenecks. In other words, we assume that the network has enough data resources to deliver the serviced MTC traffic.

1.4.1.1 LTE Access Reservation Protocol

The uplink resources in LTE for frequency division duplexing are divided into time and frequency units denoted resource blocks (RBs) [32]. The time is divided in frames, where every frame has ten subframes, each subframe of duration 𝑡𝑠 = 1 ms. The LTE frequency band is organized in subcarriers, where 12 subcarriers of 15 KHz over a subframe constitute a PRB. The bandwidth of LTE ranges between 6 RBs (i.e., 1 MHz) and 100 RBs (20 MHz). The number of subframes between two consecutive random access opportunities (RAOs) denoted 𝛿RAO varies between 1 and 20. Every RAO occupies 6 RBs and up to 1 RAO per subframe is allowed. The System Information Blocks (SIBs), where all announcements including where each RAO occurs, are broadcasted periodically via the paging procedure that occurs from every 80 ms up to every 5.12 s [1]. The LTE random access follows the access reservation principle meaning that devices must contend for uplink transmission resources in a slotted ALOHA fashion within a RAO [33], [34]. As shown in Figure 3, the access procedure consists of the exchange of four different messages between a UE and the eNodeB. The first message (MSG 1) is a randomly selected preamble sent in the first coming RAO. In Figure 4 (a) the intensity of UE requests leading to preamble activations is represented by 𝜆T. LTE has 64 orthogonal preambles, where only 𝑑 = 54 are typically available for contention among



devices, since the rest are reserved for timing alignment. Commonly, the eNodeB can only detect which preambles have been activated but not if multiple activations (collisions) have occurred. This assumption holds in small cells [35], and refers to the worst-case scenario where the detected preamble does not reveal anything about how many users are simultaneously sending that preamble2. In other words, the preamble collision is not detected at MSG 1. Thereafter, in MSG 2, the eNodeB returns a random access response (RAR) to all detected preambles. The intensity of activated preambles is in Figure 4 (a) represented by 𝜆A, where 𝜆A ≤ 𝜆T since in a preamble collision only 1 preamble is activated. The contending devices listen to the downlink channel, expecting MSG 2 within 𝑡RAR. If no MSG 2 is received and the maximum of 𝑇 MSG 1 transmissions has not been reached, the device backs off and restarts the random access procedure after a randomly selected backoff time within the interval 𝑡𝑟 ∈ [0,𝑊c] ∩ ℤ+, where 𝑊c is the maximum backoff time. If received, MSG 2 includes uplink grant information that indicates the PRB in which the connection request with MSG 3 should be sent. The connection request specifies the requested service type, e.g., voice call, data transmission, measurement report, etc. In case of collision the devices receive the same MSG 2, resulting in their MSG 3s colliding in the PRB. In contrast to the collisions of MSG 1, the eNodeB is able to detect collisions of MSG 3. The eNodeB only replies to the MSG 3s that did not experience collision, by sending message MSG 4, with which the required RBs are allocated or the request is denied in case of insufficient resources. The latter is however unlikely in the case of MTC, due to the small payloads. If the MSG 4 is not received within 𝑡CRT since MSG 1 was sent, the random access procedure is restarted. Finally, if a device does not successfully finish all the steps of the random access procedure within 𝑚 + 1 MSG 1 transmissions, an outage is declared. When the number of devices attempting access is high, most of the RACH preambles are selected by multiple devices and end in collisions. Consequently, most devices are not granted access and therefore retry again. These reattempts coupled with the new arrivals lead to an even higher amount of attempted accesses, further overloading the RACH and with the end result of almost no device being granted access. The general load control mechanism in LTE is the access class barring (ACB), which works by assigning access probabilities to different access classes [4]. However, as the ACB does not distinguish between human-type traffic and M2M traffic, the Extended Access Barring (EAB) was defined in [1] to deal with potential burst of M2M traffic arrivals. EAB is used to explicitly restrict access from devices configured as delay tolerant. The core network can also trigger the admission control at the radio access network [5], via dynamic blocking according with the load. Another mechanism proposed to overcome the RACH overload is the dynamic allocation mechanism [2]. Here, whenever the eNodeB detects the occurrence of overload, it increases the number of RAOs per frame. Due to the system limitations, this increase is up to one RAO per subframe, announced to the devices via the paging procedure. This mechanism can be further enhanced through the expansion of the LTE contention space to the code domain [3]. The principles of random access in LTE, presented in this section, forms the basis for the contributions in sections 2.3, 2.4, 3.1, and 5.1.

2 When the cell size is more than twice the distance corresponding to the maximum delay spread, the eNodeB may be able to differentiate the preamble has been activated by two or more users, but only if the users are separable in terms of the Power Delay Profile [Sesia11],[Thomsen13].



2 Analysing and Enhancing the Capacity of LTE

The capacity of cellular networks can be expressed in different ways. The classical metric for characterizing the capacity of a cellular network is to consider the coverage and the achievable bit rates in different channel conditions. In real-time systems it may be more relevant to study the communication delay than just the achievable bit rate. Finally, in M2M systems with sporadic access, i.e., with several seconds or minutes between uplink transmissions, the LTE random access procedure poses an important constraint.

2.1 LTE Coverage Assessment and Enhancements

In the previous SUNSEED deliverable [38] estimates of the coverage cell radius of Long Term Evolution (LTE) cellular system were provided based on link budget calculations. It is noted that due to the type of the devices (smart meters and wide area monitoring and supervision-WAMS nodes) their placement will be mostly indoors, with protection from the weather conditions. Between the device and the outdoor environment we should expect any type of wall or construction and therefore significant loss of signal. Moreover, in a suburban setting, the density of households with smart meters/WAMS is typically low and the cell capacity is sufficient to support a large coverage radius. For these reasons, an increase in the link budget and therefore in the coverage cell radius is considered an important aspect that should be studied in the scope of SUNSEED.

Working towards newer releases of LTE, 3GPP has conducted a study to identify potential coverage issues and to investigate associated solutions, with a focus on low-complexity devices (M2M communication, which is the same scope as SUNSEED) [39][41]. A link budget analysis was presented per physical channel, taking into account that every channel has different properties e.g. spectrum occupancy, Block Error Rate (BLER) requirements. The impact of features existing in Rel.8/9/10, such as Hybrid Automatic Repeat Request (HARQ), Physical Uplink Shared Channel (PUSCH) hopping, Transmit Time Interval (TTI) bundling, beamforming, etc. were evaluated with link level simulations and included in the required Signal to Interference plus Noise Ratio (SINR) for the respective channel.

The analysis of 3GPP shows that:

Uplink is the limiting factor in terms of coverage

The link budget for PUSCH (medium data rate) is much poorer than for other channels

With strict performance targets, Physical Random Access Channel (PRACH) and MSG3 are potentially the limiting factors.

Typically the uplink is limiting for the coverage because the UE has lower transmission power than the eNodeB. Moreover, PRACH has very strict performance requirements, which in turn makes coverage an issue. The reason why specifically PUSCH seems to have poorer link budget than other uplink channels can be explained by the fact that it has higher data rate and less redundancy than signalling channels.

The targeted coverage improvement, as referred to in the following sections, implies an increase by 20 dB as a result of coverage enhancements, relative to the lowest PUSCH link budget of 140.7 dB without coverage enhancements [38]. Other uplink channels would therefore need relatively less coverage enhancement.

The following techniques for coverage improvement are mentioned by 3GPP:

HARQ retransmission

TTI bundling



Repetition

Power Spectral Density (PSD) boosting

Relaxed requirements

Frequency hopping

Code spreading in the time domain

Radio Link Control (RLC) segmentation into smaller packet

Very low rate coding, lower modulation order (Binary Phase Shift Keying, BPSK)

shorter length Cyclic Redundancy Check (CRC) (compromising Error rate)

New decoding techniques

From the documents of 3GPP (including contributions of the companies), as well as from a more general state-of-the art analysis it seems that the techniques marked in bold are the ones that are most widely discussed. This is mainly due to their promising effect in terms of coverage enhancement and because there has already been significant ongoing work and/or there is less need for standardization. In the following sections we will have a closer look at these techniques.

2.1.1 Description of coverage enhancement techniques

2.1.1.1 Repetition

Repetition refers to the concept of sending the same or different Redundancy Version (RV) of a packet multiple times [39]. In this way, we achieve higher probability of success per packet and thus increase the coverage probability for those devices that experience low SINR. A simple example is shown in Figure 5. In this example, a packet P1 is sent four times in four consecutive TTIs before packet P2 is sent, and sequentially P2 is sent 3 times in the next three consecutive TTIs.

Figure 5: Schematic illustration of the repetition technique.

Repetition follows a similar concept as HARQ, however without waiting for feedback but sending in advance multiple times the same (or different versions of the) packet. Repetition causes significant cell spectral efficiency degradation since multiple physical resources will be occupied for a single packet transmission. Moreover, repetition prolongs UE reception time and therefore increases latency. Depending on the number of repetitions different coverage enhancement gains should be expected. Repetition can be combined with techniques such as PSD boosting and frequency hopping. Such combination of techniques can help to lower the required number of repetitions.

For PUSCH it is expected that the repetition technique will be needed regardless of other techniques applied in order to achieve sufficient coverage enhancement. The coverage target for PUSCH can in fact be met by repetition alone. In Table 1 the results of simulations from different sources (companies) are presented, using different numbers of repetitions and different transport block sizes. Apparently, several hundreds of repetitions are required to support an SINR of around -20 dB.



Source Source 1 Source 2 Source 3 Source 4 Source 5 Source 6 Source 7 Source 8 Source 9

Repetitions/TBS/ achieved SINR

219/20/ -21.3dB 559/104/ -21.2dB

300/16/ -20.7dB

620/32/ -21.3dB 730/56/ -21.3dB 1160/104/ -21.3dB

300/16/ -22dB

1050/16/ -19.3dB

250/16/ -21.3dB

1000/160/-19dB

93/42/ -19.3dB

100/16/ -19.5dB 150/32/ -20.5dB 150/32/ -20dB 150/40/ -19.8dB

Table 1: Repetition times to achieve PUSCH coverage improvement target for FDD, from [39]

In [42], simulations were performed to analyse the impact of different numbers of repetitions on the coverage extension of the Physical Downlink Shared Channel (PDSCH) carrying System Information (SI). Although the transmit direction is different (downlink instead of uplink), the repetition concept is the same. An impression of the downlink coverage improvement can be seen in Figure 6. With a target BLER of 10-2 , we observe an improvement of about 3 dB between 100 and 200 repetitions, and another improvement of about 3 dB between 200 and 400 repetitions. The improvement in SINR directly translates into an increase of the link budget.

Figure 6: BLER of system information blocks for different repetitions [42]

In [43] simulations show that even when using many repetitions the limitation still lies in the coverage rather than the capacity of users. The reason for this is the small infrequent transmissions of the MTC-devices with relaxed delay constraints that are taken into account for the simulation.

To summarize, repetition can provide important coverage improvement depending on the number of repetitions. Simulation results vary depending on settings, but in all cases a large number of repetitions are required, leading to significant degradation of the spectrum efficiency. Typically, in order to achieve the target of 15 dB improvement only by repetition we need a factor of 200-1000 (depending on the source and simulation settings).

2.1.1.2 Transmission Time Interval (TTI) Bundling

TTI bundling is the grouping of several (typically four) 1 ms TTI into one physical uplink resource. Although the design is more complex than simply repetition, the improvement is based on a similar concept. Relying on incremental redundancy, HARQ transmissions are performed in consecutive TTIs without waiting for HARQ feedback. The HARQ receiver accumulates the received energy of all transmissions increasing the probability of correct reception [44]. This leads to an increase in the available link budget.



TTI bundling in general exists in Rel.8/9/10 and is specified as follows, see also Figure 7:

TTI bundling is activated through RRC.

A single transport block is channel coded and transmitted in a set of four consecutive TTIs.

The bundled TTIs are treated as a single UL resource assignment where a single UL grant and

a single Physical Hybrid-Automatic-Repeat-Request Indicator Channel (PHICH)

Acknowledgment/ Negative Acknowledgment (ACK/NACK ) are required.

HARQ RTT and HARQ process of TTI bundling are specified.

Figure 7: TTI bundling with 4 consecutive TTIs

The last 2 bullets show the main differences with repetition, which simply repeats the same packet as is without any other special specifications. With TTI bundling coding gain is exploited, diversity is improved, and overhead is reduced. Note that TTI bundling increases the latency of the actual HARQ process. Figure 7 shows the concept: The HARQ cycle duration is 8 ms, counting from the moment of reception of the last TTI of the bundle. Thus, the shortest HARQ RTT with the bundle size of 4 would be 11 ms (3 repeated packets plus 8 ms HARQ cycle duration). However, the bundle HARQ RTT of 11 ms cannot be synchronized with the 8 ms single-packet HARQ RTT, because this would lead to collision of the retransmitted bundle of TTIs 0-3 (and which would be scheduled for retransmission in TTIs 11-14) with single packets that are transmitted directly after the bundle in TTIs 4-6 (and which would be scheduled for retransmission in TTIs 12-14). For that reason, in 3GPP it has been agreed that the retransmission of the bundle is delayed until TTI 15 (and has a duration of 4 TTIs). Therefore, although TTI bundling seems more efficient than repetition, it is not possible to bundle a lot of TTIs since it increases significantly the latency.

In Release 8/9/10 specifications, the TTI bundling mechanism is restricted to bundles of 4 TTIs, QPSK modulation and to allocations up to 3 Physical Resource Blocks (PRBs). As a solution for enhancement of link budget it is proposed in [41] that more than 3 PRBs allocated per subframe should be allowed. The coverage gain when using more than 3 PRBs is expected to be in the order of 1 dB, based on simulations. No coverage gains are mentioned for other (non-adopted) suggested enhancements in that document.

As a coverage improvement for M2M communications, in [39] a further enhancement to TTI bundling is considered. Whereas the current maximum number of UL HARQ retransmissions is 28 and TTI bundling is up to 4 consecutive subframes, these values may be extended to achieve better performance. Already in [44] it was shown that bundling of 8 TTI gives 1-2 dB gain compared to bundling with 4 TTI (which is 4 dB). This leads to a total gain of 5-6 dB.

To summarize, TTI bundling is an important feature of LTE which is already specified from earlier releases and can lead up to 6 dB gain. The degradation of spectrum efficiency is much lower than repetition, since TTI bundling has a limitation on the number of repetitions/bundles for each packet.



2.1.1.3 Power Spectral Density (PSD) Boosting

The main concept of PSD boosting is increasing the transmitted PSD of a signal [45]. This is achieved by the eNodeB scheduler unloading some PRBs in a given sub-frame, concentrating the released power into other PRBs. This is illustrated in Figure 10 for the LTE downlink, where PDSCH PRBs are unloaded to boost the PSD of PBCH. According to [45], unloading 6 PRBs used by PDSCH will permit the PBCH to be boosted by 3 dB; and if all the PDSCH PRBs are unloaded a maximum PSD boost of ~9 dB is possible for a 10 MHz system bandwidth. In 20 MHz, up to ~12 dB PSD boost is available.

Figure 8: Example of PSD boosting for PBCH signal in a bandwidth of 10 MHz. Reproduction from [45]

In the case of uplink, where PUSCH is the critical channel, PSD boosting can be employed only in case of low or medium loads. Only in that case the power can be unloaded from some resources in order to boost other resources without causing congestion in the network. It is however interesting to investigate company contributions even for other channels, since some findings could be applicable to PUSCH.

In the work from Sony mentioned above, a return analysis for PBCH (downlink) was done through simulations. The BLER results in [45] show that there is no consistently diminishing return in the transfer of a given PSD boost to coverage extension: an incremental 3 dB PSD boost with channel estimation based on unboosted CRS gives an incremental 1.4–1.9 dB coverage extension at 1% BLER. However, since the PSD boost is in decibels, the quantity of RBs which needs to be unloaded, – the ‘pain’ traded the gain – increases exponentially, as shown in Table 2. The same therefore applies to the additional RBs which need to be unloaded for each 3 dB step.

A main observation derived is that for each incremental 3 dB PSD boost, the resource cost of PSD boosting doubles, but the additional coverage extension remains at 1.4–1.9 dB. The gap between the PSD boost level and the coverage extension is due to using a channel estimator based only on CRS which, in this example, are not boosted. This is to avoid wasting power on CRS in RBs which are unloaded, and to avoid misleading legacy UEs. This way, even if the actual data are boosted and can achieve a lower BLER, the channel estimation (based on non-boosted CRS) chooses a more conservative MCS than the optimal one. Thus, the extra available power in the boosted PRBs is not fully exploited.



Figure 9: PBCH coverage extension using PSD boosting in a 10 MHz bandwidth, from [45]

PBCH coverage extension

PBCH PSD boost 0 dB 3 dB 6 dB 9 dB

Coverage extension 0 +1.9 dB +3.3 dB +4.7 dB

RBs unloaded 0 6 18 42

Incremental PRB cost +6 RBs +12 RBs +24 RBs

Table 2: Resource cost vs. coverage extension by PSD boosting on PBCH, from [45]

According to [39], for PDSCH 5dB gain can be provided from 9dB PSD boosting based on CRS without CRS power boosting and 8.4dB gain from 9dB PDSCH and DM-RS (reference symbols for the future releases of LTE) boosting can be derived. All the results are based on simulations

This concept is shown in Figure 10: UE3 is using half the bandwidth of UE1 and UE2 while the other half is unloaded. At the same time, repetition is used along 2 TTIs. UE4 on the other hand, is using less than 1 PRB (4 RE) and 3x repetition.

For PUSCH, PSD boosting may further reduce the number of repetitions. Initial evaluation results show that using boosted PRBs can save about 20-30% of repetitions.

Figure 10: PSD boosting & repetition, from [40]

2.1.1.4 Frequency hopping

Frequency hopping is a special transmission technique where the resources assigned to every user for data transmission change carrier frequency in a certain pattern through time. If the users were assigned statically the same resources in frequency and some impairment happened at a specific frequency region then the performance of some specific users would degrade irreversibly. With frequency hopping we exploit frequency diversity (frequency diversity gains) where every user gets equal chances to transmit in a channel without impairment (interference averaging). In LTE frequency hopping is only supported in uplink, since downlink has other techniques to combat the impairments in the link.

0.1 %

1.0 %

10.0 %

100.0 %

-14 -13 -12 -11 -10 -9 -8 -7 -6 -5

PB

CH

BL

ER

SNR [dB]

0 dB PSD boost

3 dB PSD boost (6 RBs unloaded)





Currently the frequency hopping as has been standardized can be between subframes (inter-subframe) or within a subframe (intra-subframe). 3GPP specifies two types of frequency hopping for the LTE uplink:

hopping based on explicit hopping information in the scheduling grant

sub-band based hopping according to cell-specific hopping and mirroring patterns [46].

A schematic illustration of inter-subframe frequency hopping for two users is shown in Figure 11 (note that the frequency does not change within the two slots of a subframe).

Figure 11: Example of inter-subframe frequency hopping. Different colors represent different UEs.

Recently, as an machine type communication (MTC) coverage enhancement, an enhanced scheme of frequency hopping has been discussed. This new concept could allow the UE to hop not only within its allocated frequencies but within the whole system bandwidth. According to [47], frequency-hopping on every M subframes can be designed in such a way that it can co-exist with the currently standardized frequency-hopping (i.e. no collision between UEs using different hopping schemes) and also allows cross-subframe channel estimation at the eNodeB. Co-existence is achieved by allocating separate frequency/time resources for the new frequency-hopping scheme. In this case, the frequency hopping can be realized on every M consecutive subframes (M = 4/8/16) in which corresponding resources are swapped to provide some kind of frequency diversity for MTC PUSCH and possibly for PUCCH transmission.

In [48], a more detailed analysis of this frequency hopping was done by means of simulations. The simulation results show that the performance difference for various hopping periods (M=1/4/8/16 subframes) depends on the retuning time, which is the time during which the UE tunes to the new frequency and cannot be used for data transmission. The performance difference is marginal when the retuning time is not taken into account. However, when retuning time is considered, subframe-by-subframe hopping increases the time it takes to complete the required number of repetitions (in the case when the repetition technique is used) resulting in some performance loss. This performance loss depends on the hopping period and is larger for short hopping periods due to the more frequent retuning that is required. Therefore it seems reasonable to consider a longer hopping period in order to achieve further performance gains from cross-subframe channel estimation and also avoid very frequent retuning time.

In [49] the gain is quantified as an extra 1 dB in the link budget calculation. This is not far from the 0.3-0.7 dB that are mentioned in an older work [50]. A bit higher values, however in the same range



(1.2-2 dB) are mentioned in [51] where intelligent methods are used. Although the gain is rather small, it is important to mention that the cost comes with some extra complexity while the cost in terms of lost spectrum efficiency is marginal.

2.1.1.5 Relaxed requirements

In LTE some control signals have specific (highly robust) modulation and coding schemes, and specific requirements in terms of detection probability. A way to increase the coverage is to relax these requirements.

Relaxation of performance requirements can be applied on Primary/Secondary Synchronization Signals (PSS/SSS) and PRACH signals. For the synchronization signal it can be done for example by allowing longer acquisition time (e.g. for reading system information or for performing synchronization) and for the PRACH by tolerating a higher error rate (e.g. through improved message design such that with a higher error rate, it is still possible to decode the message in the correct way).

For PRACH, according to [39], relaxing the detection miss probability from 1% to 10% and with about 32 sequence repetitions, 17dB coverage enhancement target can be achieved, and with about 10 sequence repetitions and 4 sequence repetitions 14 dB and 11dB coverage enhancement target can be achieved, respectively.

It seems however that this coverage improvement cannot be applied to PUSCH, since PUSCH can adapt to different modulation and coding schemes, and does not have a pre-defined BLER.

2.1.2 Effect on link budget and coverage Coverage improvement techniques can improve coverage for delay-tolerant MTC. The larger the required improvement to coverage, the larger is the negative spectrum efficiency impact will be. Also, the more advanced coverage enhancement techniques require more complex specification. The coverage enhancement target should therefore be balanced with the required capacity and the specification impact. The initial target for coverage improvement set by 3GPP was 20 dB. Considering the spectrum efficiency, specification impact and standardization effort, a possible target of coverage enhancement in terms of trade off could be 15 dB at least for FDD.

A summary of the coverage enhancements of this section, together with their expected gains, is given in Table 3. The technique of relaxed requirements is not included in this table, since it applies to the synchronization channels and RACH rather than the PUSCH.

Coverage enhancement Expected gain

Repetition up to 15 dB

TTI bundling ~4-6 dB (4 TTI)

PSD boosting up to 12dB or 30% less repetition

Frequency hopping ~ 1 dB

Table 3: Coverage enhancements, and their gains

Finally, we analyse the expected effect of coverage enhancement options on cell radius. To this end, the link budget described in [38] is increased with the coverage enhancement value in the range between 0-20 dB, as this range seems feasible by accumulating the gains listed in Table 3. The cell radius is then derived from the new link budget value using the empirical Okumura-Hata (at 800 MHz) and COST-231 Hata (at 1800 MHz) path loss models.

The result of this analysis is shown in Figure 12 for three environment types and for two LTE frequency bands. The plots show that the cell radius can potentially be increased by about a factor



two and four if 10 dB or 20 dB coverage enhancement can be realized, respectively. Before such larger coverage areas can indeed be realized, a capacity analysis should however confirm that the available radio resources in the cell are sufficient to support the increased traffic load in the new, larger area.

Figure 12: Effect of coverage enhancement options on cell radius for three environment types.

2.1.3 Coverage/capacity trade-off for the SUNSEED use case

2.1.3.1 Introduction

In the previous section we saw that several techniques can be deployed in order to improve the link budget of an uplink channel in LTE. This improvement can be used in the framework of SUNSEED to increase the coverage radius of each base station, or to combat a higher penetration loss of buildings and constructions where the smart meters and WAMS are located. The improvement of the link budget however, in most cases comes with a cost in terms of capacity: some resources have to be either unloaded or loaded with redundant data, which reduces the spectrum efficiency. In this section we analyse in more detail the trade-off between coverage and capacity for the SUNSEED use case.

2.1.3.2 Trade-offs per technique

Repetition: According to the previous section, a 3dB improvement in link budget requires a

double amount of repetitions; therefore we have 50% decreased capacity.

TTI Bundling: According to the previous section, a bundling of 4 TTIs provides 4 dB

improvement, while a bundling of 8 TTI provides 6 dB improvement. Therefore we can use an

approximation of 50% decreased capacity for every 2 dB improvement in coverage (up to

maximum coverage improvement of 6 dB, since only a bundling up to 8 TTI is currently being

discussed)

PSD Boosting: According to the previous section, a gain of 1.4 to 1.9 dB can be achieved if we

double the resources that are reserved for transmitting a signal. We approximate this as 1.5

dB.

Frequency hopping: According to the previous section, frequency hopping can provide 1 dB

gain with negligible costs in capacity.



Table 4 below summarizes the coverage gains and capacity results per technique. The summary suggests that repetition is the most efficient technique, although it should be noted that in reality e.g. overhead can actually decrease its efficiency. For the purpose of this analysis we will consider repetition as the preferred coverage enhancement technique.

Coverage enhancement Expected gain (dB) results in capacity

Repetition 3 -50%

TTI bundling (up to 3 times) 2 -50%

PSD boosting ~1.5 -50%

Frequency hopping ~1 -

Table 4: Trade-off between coverage enhancement gain and capacity for different coverage enhancement techniques.

In the remainder we assume that users inside the initial cell coverage are in good radio conditions, and therefore the coverage enhancement will apply only to the users who are outside the initial coverage, in the ring between the initial cell radius and the enhanced cell radius Those users will benefit from the excess capacity of supported users and actual users inside the initial cell. This excess capacity will be decreased with the coverage enhancement. More details can be found in Appendix A.

2.1.3.3 Effect on the nodes capacity per cell

In this section we will derive a trade-off between capacity and coverage resulting in an optimal value for the link budget increase. To this end we will use results from [38] for the coverage (see Section 4.2.2 and Appendix D in [38]) and for the capacity (see Section 4.2.3 in [38]). In Table 5 below we summarize the maximum cell radius per scenario investigated in [38], together with its corresponding capacity. The amount of households was derived from the number of total nodes taking into account the ratio of WAMS to SM, where we assume that every household has one SM and some of them additionally have one WAMS node. These numbers will be referred to later as ρmax, the maximum theoretical household density.

Frequency Environment Max.

Cell range (km) Households per km2 WAMS to SM = 1/3

Households per km2 WAMS to SM = 1/6

800 MHz

Urban 1.03 2081.8 3943.6

Suburban 2.4 338.4 642.0

Rural 10.22 21.2 40.1

1800 MHz

Urban 0.34 17896.6 34064.8

Suburban 0.83 3090.8 5868.0

Rural 3.68 162.4 307.8

Table 5: Summary of coverage and capacity results from [38]

In Table 6 we summarize the inputs on the household densities, as they were stated in [38], see there the case of Elektro Primorska, and the values in Table 2-3 and Figure 2-2. 3GPP also provides values for the maximum and minimum household density in urban environments [39], which are similar to the values in Table 6. These numbers will be referred to as ρnode , the actual household density.



Source Area type Max density

[Households /km2] Min density

[Households /km2]

Elektro Primorska

Urban 2200 (40003) 1000

Suburban 750 250

Rural 200 20

Table 6: Reference household densities ρnode

It is noted that increasing the link budget leads to a larger cell radius, which in turn increases the amount of users that needs to be served. However the increase of link budget at the same time decreases the supported capacity of the resources, according to the previous sections. The increase of required capacity and the decrease of supported capacity with link budget converge for one value of link budget increase for which the required capacity is equal to the supported capacity. This optimal link budget increase (Δ) is derived analytically in Appendix A and is given by (in linear units):

𝛥𝑛+20

𝑛 − 𝛥 − (𝜌𝑚𝑎𝑥−𝜌𝑛𝑜𝑑𝑒

𝜌𝑛𝑜𝑑𝑒) = 0 (1)

Where node and max are the actual node density and the maximum supported node density for the case without link budget respectively, and n is a path loss coefficient from the Okumura-Hata propagation model. We assume n= 35.7 dB for urban or suburban environment and n=34.8 dB for rural environment. The above equation is polynomial and can be solved numerically with one real

value. The relation between and max/node is visualized in Figure 13. For the purposes of this analysis we do not take frequency hopping into account. From Figure 13, the following can be concluded:

a) The relation between the link budget increase and the ratio max/node is almost the same for the three different radio environments urban, sub-urban, and rural.

b) As the ratio max/node is increased from 1 (i.e. the actual node density is equal to the maximum theoretical node density) to 100 (i.e. the actual node density is 100 times smaller than the maximum theoretical node density) the link budget can be improved by roughly 13 dB)

3 For large cities



Figure 13: Relation between optimal link budget increase and ratio max/node.

Table 7 shows the optimal link budget increase for the different environment types and frequency

bands, using the values for max and node specified in Table 5 and Table 6, respectively. Applying the new link budgets to the path-loss formula we obtain the corresponding values for the cell radius (Table 8). For the cases where the supported capacity is already insufficient without coverage enhancement, a minus (-) is indicated. For these cases, no further coverage enhancements are possible and the cell radius should actually be reduced in order to support the required capacity.

Frequency Environment Improvement

WAMS/SM = 1/3, max. density (dB)

Improvement WAMS/SM = 1/6, max. density (dB)

Improvement WAMS/SM =

1/3, min. density

(dB)

Improvement WAMS/SM =

1/6, min. density

(dB)

800 MHz

Urban - 2.7 3.2 5.1

Suburban - - 1.7 3.9

Rural - - 0.3 3.0

1800 MHz

Urban 6.9 8.6 8.9 10.6

Suburban 5.2 6.8 8.0 9.6

Rural - 2.1 6.8 8.4

Table 7: Possible improvements (in dB) per scenario. Minus sign (-) implies that an improvement is not possible due to capacity constraints.

-1

1

3

5

7

9

11

13

15

1 10 100

(d

B)

max/node

Optimal link budget increase (dB)

urban/suburban

rural



Frequency Environment Initial radius (km)

New radius WAMS/SM =

1/3, max. density

(km)


1/6, max. density

(km)


1/3, min. density

(km)


1/6, min. density

(km)

800 MHz

Urban 1.0 1.04 1.2 1.3 1.3

Suburban 2.4 2.44 2.44 2.7 3.1

Rural 10.2 10.24 10.24 10.5 12.5

1800 MHz

Urban 0.34 0.5 0.6 0.6 0.7

Suburban 0.83 1.2 1.3 1.4 1.5

Rural 3.7 3.74 4.2 5.8 6.4

Table 8: Possible improvements (in radius) per scenario. The results show the following:

The optimal coverage enhancement ranges from 0-10 dB depending on the node density to

be supported, environment type and frequency band. The obtained values are all lower than

the 15 dB coverage improvement proposed by 3GPP. This indicates that if the latter value of

15 dB would be applied, this would lead to insufficient capacity in the served cells.

The larger coverage enhancement gains are obtained for the scenarios with smaller initial

cell radiuses. The reason is that the initial number of nodes to be supported is relatively

limited for those scenarios, even for the urban case with higher node density. Thus there is

relatively much margin to increase the cell radius without running into capacity bottlenecks.

The largest effect of coverage enhancement should therefore be expected for the higher

frequency band.

For the rural scenarios the coverage enhancement to be achieved without violating the

capacity constraints is relatively modest. In some cases the supported capacity is already

insufficient to serve the initial cell sizes without coverage enhancement. This indicates that

for rural areas the cell size in the smart grid network is determined by the supported capacity

rather than the constraints on the link budget.

2.1.4 Conclusion In this section we have analysed the effect of coverage enhancement techniques on the cell radius in a LTE cellular network. In the context of 3GPP several coverage enhancement techniques are considered, of which repetition, TTI bundling, Power Spectral Density Boosting and frequency hopping seem to be the most promising for deployment in smart grids. The use of these techniques can lead to about 10-20 dB improvement in link budget, translating into a factor 2-4 increase in cell radius.

The use of coverage enhancement techniques can lead to capacity bottlenecks for two reasons. First, the required capacity is larger since in larger cells more devices need to be supported. Second, the available capacity is smaller since coverage enhancement techniques rely on redundancy meaning that on average more radio resources are used per data transmission. Coverage enhancement 4 Those are the initial cell radiuses corresponding to Δ=0, since an improvement is not possible for these cases: Already without coverage enhancement the available capacity is insufficient and the cell radius needs to be reduced.



techniques should thus only be used if sufficient excess capacity is available. An analytical study shows that the highest gain of coverage enhancement techniques (up to 10 dB) should be expected for higher frequency bands and urban areas, where the initial cell radii are small and capacity bottlenecks do not easily occur. In rural areas, on the other hand, the potential of coverage enhancement techniques is less, since the available capacity may be already insufficient to serve the initial cell sizes without coverage enhancement. In all cases, the expected coverage improvements are smaller than the 15 dB targeted by 3GPP.

2.2 LTE Delay-Based Capacity Assessment

Due to the proliferation of Distributed Energy Resources (DER), such as wind turbines and photovoltaics, the power flow in future smart grids migrates from traditional one way flow from major power plants to consumers towards power flow in both directions. Additionally, more electricity consumers are proactive via electricity demand-response approaches in order to optimize their monthly energy cost. Consequently, the voltage violations of the prescribed (or regulated) limits and to the lesser extend congestions in the electricity distribution network could occur. There are two approaches to solve these electricity grid operational issues. First, a traditional approach is to perform the necessary electricity grid reinforcements and limit with sufficient margin the power flow from DER in order to safeguard the necessary voltage levels. However, this increases operational costs and does not fully leverage the potential of the DER. The second approach [60], which avoids or delays grid reinforcements and fully utilizes the DER, is deployment of Advanced Distribution Network Management System Platform (ADNMSP) that is able to monitor voltage profiles, perform grid state estimations, congestion managements, and load forecasts. The grid management functions can be executed in control boxes installed in e.g. transformer stations, and use measurements from wide area measurement and supervision (WAMS) nodes. The whole concept is illustrated in Figure 14. The WAMS nodes should be installed in carefully selected locations that enable optimized state estimation of the electricity distribution grid with minimum number of installed WAMS nodes. In this chapter we address the communication network supporting the measurement reporting for WAMS and SM nodes via uplink transmission in LTE cellular networks.



Figure 14 Illustration of the ADNMSP approach in the electricity distribution grid, reproduction from [60]

The LTE system capability, especially delay/latency performance is investigated for smart grids [52]-[54] as well as in general (e.g. for supporting voice over IP services) [55][56]. The approach in these studies involves measurements or experiments complemented by theoretical analysis of the delay in LTE. The impact on the delay performance from the number of users active in the cell is not addressed, which is one of the main topics of this study. Further, there have been also studies which focused on the random access channel (RACH) performance in LTE when supporting smart grid applications [57], which is not in the scope of this section. It is assumed that WAMS nodes (or even SM if reporting with low period e.g. around 1s) would not perform the RACH procedure before sending individual measurements in uplink but rather have ongoing active sessions with intermediate Discontinuous Transmission (DTX) cycles. Additionally, the study also addresses the impact on the uplink transmission delay from the granularity of the allocated transmission resources for each measurement node as well as the environment type and scheduling policy for the WAMS nodes. We will always use prioritization in the WAMS nodes, since their payload is considered more time-critical that that of a SM.

The rest of the section is organized as follows. In Section 2.2.1 the communication approach and requirements are presented for facilitating the WAMS and SM node measurement reporting. Section 2.2.2 presents the analysis approach for the LTE delay followed by the numerical simulation results in Section 2.2.3. The chapter is finalized with the conclusions and recommendations in Section 2.2.4.

2.2.1 Smart grid communications and requirements The WAMS (and SM) are the basic facilitating nodes for the ADNMSP approach. In this section we aim at defining the communication requirements in terms of amount of generated measurement data and the end-to-end delay requirements for the transmitted measurement reports.

The installation of WAMS nodes in the medium and low voltage parts of the electricity distribution grid is beyond current state of the art. Each WAMS node has to perform and report similar measurements as the so-called phasor measurement unit (PMU) in the high voltage (transmission) electricity grid [58][59]. For the packet sizes we will use the assumptions used in [60]. Consequently, the size of the reported measurement from the WAMS nodes is 3628 per packet and for SM is 53 bytes per packet.



Next to the measurement report size it is important to know the delay requirement for the reported measurements from the WAMS and SM nodes. The literature gives the following indications:

PMU related latency requirements: 8-20 ms [61], 50ms [62], 10 ms [63], 100 ms [64] (MSG2)

SM related latency requirements (AMI): 1 s [64]

WAMS related latency requirements: 100-200 ms [63]

Smart grid application requirements in [65]: 1 s for state estimation applications (e.g. power flow, voltage assessment, etc.), 100 ms for transient stability applications (e.g. load trip, islanding, etc.), 1s for small signal stability applications (e.g. modes shape, damping, etc.), 1 to 5 s for voltage stability applications (e.g. capacity switching, load shedding, islanding).

Due to the fact that at the moment of writing of this deliverable a range of values for the latency requirement could be found in the literature and because the latency requirement also depends on the time criticality of the particular smart grid application (i.e. the time-variability of the application’s input and the required reaction time of the application) we use three values for the latency requirement: 0.1 s (high), 1 s (medium), and 10 s (low).

In this section the focus is on the radio part dradio of the LTE uplink transmission delay, as illustrated in Figure 15. The additional delay in the core network part dcore, i.e. from the eNodeB to the point where the transmitted packet enters the Internet domain outside the mobile operator network, is not taken into account. This is because dcore is typically smaller than dradio and it can be considered relatively constant for varying number of terminals communicating with the eNodeB (e.g. 10 ms are mentioned in [66] or 20 ms in [67]).

In LTE the radio transmission resources consist of Physical Resource Blocks (PRBs) with TTI duration of 1 ms, and in the frequency domain, in sets of 12 contiguous subcarriers with 180 kHz total width. The PRB allocation is done by the scheduler located in the eNodeB as illustrated in Figure 15. Depending on the radio conditions (e.g. SINR level) an appropriate modulation and coding scheme is applied to transfer the data within the allocated PRBs. This, in turn means that within the allocated PRBs the amount of data that can be transmitted within one (or more) TTIs differs for different WAMS and SM terminals depending on their respective SINR conditions. Consequently, for different packet sizes and terminal conditions, to transfer the whole measurement report different WAMS and SM nodes might use different number of PRBs and TTIs.

In our delay analysis in Section 2.2.2, in every TTI a number of nodes willing to transmit are scheduled and get assigned a fixed number of PRBs. This follows the scheme of Fair Fixed Assignment (FFA) as described in [67]. If the total required number of required PRBs for all users is higher than the number of PRBs in one TTI, then more than a single TTI is needed to serve them. This consequently increases the latency for the delivery of the packets.



Figure 15 Illustration of the uplink transmission in LTE and the two delay components dradio and dcore. The user equipment (UE) is the 3GPP term for end terminals

2.2.2 Analysis of LTE capacity impact on latency In this section, we first exploit the impact of number of nodes on the end-to-end latency performance of WAMS and SMs, by taking into account the communication requirements and assumption in Section 2.2.1.

In a certain cell of the LTE network, a number UE of (WAMS and SM) nodes are typically randomly placed within the coverage area of the cell. For an arbitrary i-th node, the achievable wideband signal-to-interference-and-noise ratio sinri in linear scale is calculated as follows:

𝑠𝑖𝑛𝑟𝑖 =𝑝𝑈𝐸𝐿𝑖.𝐺div

𝑛PRB.(𝑛𝑡ℎ_𝑖+𝑖𝑢𝑙_𝑖) (2)

Here,

pUE is the uplink transmission power of the node (e.g. 23 dBm)

Li is the propagation loss (incl. antenna gain, path-loss, shadowing, but no multipath fading) for the particular location of the i-th node

Gdiv is the environment-specific macro-diversity gain for all nodes.

nPRB denotes the number of PRB assigned per node, which is assumed fixed for all nodes.

nth_i is the thermal noise (including noise figure at the receiver)

iul_i is the inter-cell uplink interference experienced at the BS on the allocated PRBs for the i-th node

For the sake of simplicity, we assume a given uplink inter-cell interference level of iul_i = 3 dB for all the allocated PRBs. This is motivated by the fact that in the busy hours an operator typically plans and operates the LTE uplink with a desired uplink inter-cell interference target.

Based on the sinri, an appropriate modulation and coding scheme (MCSi) can be selected for the i-th node, such that the corresponding block error rate (BLER) is in a range of [0, 10%] . In our analysis we assume an average BLER of 5%. For the numerical evaluations in Section 2.2.3 the MCS selection is done via link-level SINR vs BLER performance results [70].



Further, from the selected MCSi and the assigned nPRB PRBs per node, we can derive the corresponding transport block size (TBSi) in bits for the i-th node, from the 3GPP specification [71]. Consequently, the number of TTIs for the i-th node nr_ttii needed to transmit the packet with size P [bits] in the case of no retransmission can be calculated as:

𝑛𝑟_𝑡𝑡𝑖𝑖 = ⌈𝑃

𝑇𝐵𝑆𝑖⌉ (3)

Here, the notation ⌈ ⌉ is the rounded up integer number. In order to assess the latency each node experiences, a scheduling simulation loop is executed advancing with a TTI step (i.e. 1 ms step) where at each scheduling turn the following steps are performed:

Step-1: Determine the number of PRBs which are free for initial transmission allocation, denoted as NPRB,free. Ideally, there will be the total number of PRBs of the cell free, e.g. 50 PRBs for a 10 MHz LTE carrier. However, an operator can also decide to reserve a fraction of the whole LTE carrier for supporting smart grid nodes. From the total free PRBs available for allocation the resources claimed by the following WAMS and SM users have to be extracted:

o Nodes that were transmitting in previous TTI (if applicable) but need additional TTI to finish the transmission.

o Nodes that need to re-transmit a packet (if applicable and originally transmitted 8 TTIs earlier), see also step-3a below.

Step-2: Select at most NPRB,free/nPRB nodes for initial transmission in the given TTI i.e. users that are not continuing their transmission from previous TTI or scheduled for retransmission. Note again that nPRB is the minimum number of PRBs that can be allocated for a single node. For each selected node, the following is performed:

o Reduce nr_ttii by one as this node is scheduled for initial transmission. If nr_ttii is larger than zero than this node is scheduled for transmission also in the next TTI.

o If the packet is erroneously received, assuming this will happen in average for 5% of the transmissions, schedule this node for retransmission at 8 TTIs further ahead. Otherwise set the delay to the current TTI.

Step-3a: Select from nodes, which are scheduled for retransmission. For each of these nodes, the following is performed (no reduction of nr_ttii as this is a retransmission):

o If the packet is erroneously received, as this will happen in average for 5% of the transmissions, schedule this node for retransmission at 8 TTIs further ahead and set the UE delay to the current TTI plus 8 ms. Otherwise set the UE delay to the current TTI.

Step-3b: Allocation of nPRB resources to nodes that are continuing its transmission from previous (if applicable) TTI. For each selected node, the following is performed:

o Reduce nr_ttii by one as this node is scheduled for transmission. If nr_ttii is larger than zero than this node is scheduled for transmission also in the next TTI.

o If the packet is erroneously received, assuming this will happen in average for 5% of the transmissions, schedule this node for retransmission at 8 TTIs further ahead. Otherwise set the delay to the current TTI.

The scheduling loop is stopped when all nodes have sent their packets including retransmissions.

One limitation of this algorithm is that during this scheduling loop (before all the nodes have finished transmission) no new arrival is allowed to be scheduled and the number of nodes that are scheduled within the simulation is pre-determined. This implies a deviation from reality where eventual extra nodes might request to be scheduled. However the purpose of this analysis is to provide a one-time



estimation of the delay experienced by WAMS and not to perform a full sized LTE simulation of continuous transmissions.

Moreover, note that the packet re-transmission in the scheduling loop requires at least 8 TTIs. As can be seen in Figure 16, 1 TTI is needed at the transmitter to send the original packet, the receiver needs 3 TTIs to decode the transmitted packet and decide if re-transmission is needed, 1 TTI is in receiver to send the NACK, and another 3 TTIs are needed for the transmitter to decode the NACK [72].

Figure 16: HARQ process, from [72]

Within each TTI of the scheduling loop packets from the WAMS and SM nodes are allocated for transmission with WAMS packets having higher priority than SM packets (e.g. strict WAMS priority over SM nodes). This is due to the stricter delay requirement for WAMS nodes relative to the SM nodes. Further within the group of WAMS or SM nodes active in the cell there are two options for scheduling the node:

a) Random scheduling of WAMS or SM node for transmission

b) TTI-based scheduling of WAMS or SM nodes based on the estimated time for the WAMS or SM node needed to transmit its measurement packet. The WAMS or SM nodes having longer estimated time (i.e. higher number of TTIs needed to transmit the packet) will be scheduled with higher priority.

The capacity is then defined as the number of WAMS and SM nodes within the LTE cell that can be served such that their respective 95% delay requirements are satisfied.

2.2.3 Numerical results In this section, we first present the used simulation parameters settings in our study. Urban, sub-urban, and rural propagation environments are considered in the analysis with the most important parameters as specified in Table I. Numerical results are then presented, following the approach in Section 2.2.2 above, that quantify the achievable latency versus capacity of an LTE network for the smart grid applications.

We assume each node is assigned nPRB = 0.5, 1 or 2 PRBs in order to investigate the impact on the delay from the resource allocation granularity. Note that 3GPP is considering to allow partial PRB allocation (e.g. 0.5) to increase the uplink coverage by increasing the transmit power spectral density [73].

For a more elaborate look on the results, we will assume a fixed number of 1 PRB per user. Further, we assume different packet size for SM and WAMS node of 53 Bytes and 3628 Bytes, respectively, as



presented already in Section 2.2.1. In the analysis we investigate a 10 MHz LTE system where either the total amount of 50 PRBs are available for usage or only 6 PRBs (e.g. a fraction of 12% percent) are available for allocation to SM and WAMS nodes. The results for 6 PRBs are presented here while the results for the full bandwidth are presented in Appendix B. This is motivated by the fact that an LTE telecom operator would probably like to only utilize a relatively small fraction of the total LTE carrier for support of the smart grid nodes.

Parameters Environment

Urban Suburban Rural

System bandwidth(MHz) 10 10 10

Inter-Site Distance (m) 510 3600 1530

Spectrum band (MHz) 1800 800 800

Propagation model COST231-Hata Hata-Okumura Hata-Okumura

Penetration loss (dB) 20 16 15

Shadowing standard deviation (dB)

12 8 6

Macro-diversity gain (dB) 5 4 3

Max. terminal transmit power (dBm)a

23 23 23

Thermal noise power density (dBm/Hz)

-174 -174 -174

Noise figure at eNodeB (dB) 2 2 2

Antenna gain at devices (dB) 0 0 0

Antenna gain at eNodeB (dB) 15 15 15

Receive diversity gain (dB)b 3 3 3

Cable and connector losses at eNodeB (dB)

1 1 1

Table 9 Simulation Parameters a. Subject to additional losses arising from antenna cables and body loss, which though for a typical communication node in a smart grid

deployment should be expected to be absent or negligible b. The theoretical maximum for a configuration with two antennas at the eNodeB.

The numerical results presented in this section are generated by ‘snap-shot’ simulations with static nodes as no node mobility is envisaged for the SM and WAMS nodes in smart grid applications. An LTE cell is populated with randomly placed SM and WAMS nodes e.g. from 50 to 2000 nodes per LTE cell. For each ‘snap-shot’ the scheduling procedure from Section 2.2.2 is executed and the delay in number of TTIs is determined for sending the packets generated by the individual SM and WAMS nodes. The ‘snap-shot’ realizations are repeated many times (e.g. 1000) and in each realization the individual packet delays are collected forming a statistical set that is used to derive the 95th-percentile delay performance metric from the CDF. It is important to stress here that all the results in this section should be interpreted for the case of simultaneously active SM and WAMS terminals.

First, we investigate the impact different radio environments, SM to WAMS ratios and scheduling policies, on the cumulative density function (CDF) for the SM and WAMS packet transmission delays. This analysis is presented in Figure 17 and Figure 18 for the case of the WAMS and SM delay CDF, respectively. Note that for this delay CDF analysis the x-axis in Figure 17 and Figure 18 is in the logarithmic scale in order to illustrate the wide range of delays as the number of users per cell is increased from 50 to 5000.

From Figure 17(a) and Figure 18(a) it can be seen that, as expected, the delay increases with the number of users in the network, for both WAMS and SM nodes. For low number of nodes (e.g. 50



nodes) the delay of WAMS nodes is even larger than the delay for SM nodes mainly due to the different packet size for SM (53 Bytes) and WAMS node (3628 Bytes). The drastically smaller packet size for SM nodes is also the reason for the smaller minimum delay for SM nodes when compared to the WAMS nodes. As the number of nodes increases and the system resources of 6 PRBs are increasingly utilized both WAMS and SM nodes experience similar and at the same time higher delays. If we increase the available spectrum for the WAMS and SM traffic from 6 PRBs to 50 PRBs , the delays are drastically reduced (e.g. upper limited by 3 seconds) and therefore the effect of the resource congestion becomes visible for higher number of nodes (only 2000 and 5000 nodes, see Figure 47(a) and Figure 48(a) in Appendix B.

Regarding the influence of the node scheduling approach, from Figure 17(b) we can see that TTI-based scheduling lowers the delay experienced by the WAMS nodes only for higher number of nodes per cell (e.g. from 1000 onwards). However, there is no effect from the random versus TTI based resource allocation on the SM delay as presented in Figure 18(b). This is because SM nodes send much smaller packets and are scheduled always after the WAMS transmissions. Compared to the case when full spectrum (50 PRBs) is available, we can see that different types of scheduling has the same effects as for 6 PRBs, where only WAMS delay is impacted and then also for higher number (e.g. 5000) of nodes per cell. This is illustrated in Figure 47(b) and Figure 48(b) in Appendix B.

From Figure 17(c) and Figure 18(c)(as well as Figure 47(c) and Figure 48(c) in Appendix B) we can clearly see that the type of radio propagation environment (including different inter-site distance) does not influence the delay statistics. The reason for that is that even though for suburban and rural environments we usually have larger inter-site distances, and therefore increased distance based radio attenuation, this is compensated with lower attenuation due to the use of lower frequency, less shadowing variation and lower penetration loss.

Figure 17(d) and Figure 18(d) illustrate the influence of the type of PRB allocation on the overall delay of WAMS and SM nodes. We can see that for low number of users both low and high delay CDF percentiles for WAMS nodes differ for different types of PRB allocation. The option of having 2 PRBs per user results in the lowest delays. For higher number of users (e.g. in a situation of high utilization of the available spectrum) the PRB allocation scheme influences only a small percentile of WAMS nodes having short delays while the e.g. 95% delay is not influenced. This is because the larger delays (e.g. 95% delay range) are predominantly result of WAMS nodes waiting for other WAMS nodes to finish their respective transmissions and not on the amount of allocated PRBs per WAMS node. This is also the reason why the amount of allocated PRBs per user does not have significant influence on the SM delay statistics presented in Figure 18(d) as the SM nodes are scheduled after the WAMS nodes start their uplink transmission.



(a)

(b)

(c)

(d)

Figure 17: CDFs of WAMS delay performance for 6 PRBs: (a) the influence of number of users per cell on the delay CDFs;), (b) the influence of random and TTI based scheduling; (c) the influence of the

radio propagation environment; (d) the influence of different number of PRBs per UE



(a)

(b)

(c)

(d)

Figure 18: CDFs of SM delay performance for 6 PRBs (a) the influence of number of users per cell on the delay CDFs; (b) the influence of random and TTI based scheduling; (c) the influence of the

environment; (d) the influence of different number of PRBs per UE

The capacity limiting factor of the LTE cell is naturally dependent on the delay requirement for the WAMS (and SM) nodes. The amount of WAMS (and SM) nodes that can be simultaneously active within a LTE cell for the different delay requirements of 0.1 s, 1 s, and 10 s is illustrated in Table 10. Note that this is a rough indication as the intention is to see the influence of the number of available PRBs and the WAMS to SM ratio. For the values in Table 10(b) the reader is also referred to the results illustrated in Figure 49 in Appendix B. Note that the 95% delay requirement of 0.1 s would be rather difficult to achieve with LTE also in the case of full 10 MHz available LTE carrier spectrum. For the 95% delay requirement of 1 s the LTE cell capacity ranges from 500 to 5000 nodes depending on the number of available PRBs and WAMS to SM ratio. If the 95% delay requirement is relaxed to 10 s the capacity of a LTE cell is from 2000 nodes and higher.



95% delay requirement Available PRBs

6 PRBs 50 PRBs

0.1 s < 50 < 50

1 s ~500 ~2000

10 s ~2000 > 5000

(a) WAMS to SM ratio 1/3

95% delay requirement Available PRBs

6 PRBs 50 PRBs

0.1 s < 50 < 50

1 s ~ 1000 ~5000

10 s ~5000 > 5000

(b) WAMS to SM ratio 1/6

Table 10 Indication of total number of nodes for different 95% delay requirements

2.2.4 Conclusions This study investigates the uplink delay performance of an LTE system supporting packet transmission from SM and WAMS nodes that form the basis for real-time management of future smart grids. The following main conclusions can be derived from the above analysis:

a) As expected, the delay values are statistically proportional to the load of the network, i.e. the number of users per cell (and also ratio of WAMS to SM nodes). The capacity of the LTE cell is largely dependent on the select 95% delay requirement, the WAMS to SM ratio, and the amount of available PRBs that can be utilized. For example, if we set the 95% delay requirement to 1 s the cell capacity ranges from 500 (6 PRBs available) to 5000 (50 PRBs available) nodes.

b) There are different ways of scheduling, and the scheme of “TTI based” scheduling, which prioritizes nodes with more data (remaining) to be transmitted, seems to improve the 95th percentile delay of WAMS nodes specifically for higher number of nodes per cell (e.g. 1000 or more), without affecting the delay CDF performance of SM nodes.

c) The radio propagation environments have little effect on the delay CDF results. d) The number of allocated PRBs per node (2, 1 or 0.5) has insignificant impact on the 95%

delay performance especially for higher number of users (e.g. higher than 50).

As a further study the delay analysis could be applied to 3G and 2G cellular system in order to evaluate their relative delay performance with respect to the achievable LTE delays and their suitability for real-time smart grid management. Additionally, other deployment scenarios will be evaluated where SM and WAMS measurements can be collected via other RF transmission (e.g. local Wi-Fi or RF-mesh) towards a gateway (e.g. an aggregator node) that is further communicating via a nearby LTE base station.

2.3 GPRS and LTE Random Access Capacity Assessment

The study presented in this section is an extension of the preliminary study presented in D3.1, section 4.2.1. This work is published in [6]. In this section we have the following four contributions: 1) extraction and classification of smart meter traffic models from relevant specifications, as well as predicted future traffic growth; 2) comprehensive simulation model of radio access systems that includes all phases in the access, in contrast to [10] and the NIST PAP2 guidelines for assessing wireless standards for smart grid



application v1.0 that use simplified models; 3) quantitative assessment of how many smart meter devices can be supported in cellular systems, comparing the simplified and comprehensive simulation model results; and 4) recommendations for standardization and future roadmap of the radio access technologies.

2.3.1 Smart Meter Traffic Model In the literature there are different examples of traffic models for traditional smart meters. Of these, the OpenSG Smart Grid Networks System Requirements Specification (described in [11]) from the Utilities Communications Architecture (UCA) user group is the most coherent and detailed network requirement specification, and it has therefore been used in this work as input for the SM traffic model. Through interactions with SM manufacturer in the SUNSEED project, we have found that the derived SM traffic model is in line with what is typically implemented in practice. The UCA OpenSG is a relevant consortium of 190 companies and the considered smart grid use cases are in line with those studied by other standardization organizations such as ETSI and USEF. Since there are differences in which use cases and applications are offered by the DSO or electricity retail company and which of those the individual customers are using, a one size fits all traffic model does not exist. In the following we consider a comprehensive configuration where all use cases that involve communication from the smart meters to the core network will be in operation and note that actual deployments with different configurations may lead to different results. For calculating the message frequency in the uplink SM traffic model the event occurrence frequencies listed in Table 11 have been used. Besides the values listed in Table 11 we assume that a commercial/industrial SM sends a 2400 bytes meter reading packet every hour, whereas a residential SM sends a 1200 bytes report every 4 hours.

Event Frequency [events per meter]

On-demand meter read requests 25/1000 per day

Meter capped energy mode request 5 per year

DR load management request to HAN devices 15/1000 per day

HAN device join/unjoin 5 per year

Real-time price (RTP) update 96 per day

Metrology firmware update 4 per year

Metrology program update 4 per year

NIC firmware update 4 per year

NIC program update 4 per year

Table 11: Assumptions for deriving traffic model.

The SM uplink traffic model, resulting from the above assumptions, is presented in Figure 19. The gray boxes represent the different use cases and the boxes span the latency and payload size requirements of the corresponding messages. The white box represents the WAMS-SPM traffic, which is defined in the following section. Nearly all use cases have reliability requirements of 98%, with the exceptions being two alarm messages in the IDCS use case requiring 99%, and the periodic meter reading, which has time-dependent reliability requirements ranging from 98% to 99.5%. In relation to the figure, Table 12 shows the average estimated uplink/downlink bandwidth for each use case.



Figure 19: Classification of OpenSmartGrid traffic originating from an SM. 𝜆-values show the number of generated messages per day per device. Use case short names: Demand Response - Direct Load

Control (DR-DLC), Premise Network Administration (PNA), Firmware and Software updates (FW/SW upd.), Real-Time Price (RTP), Islanded Distributed Customer Storage (IDCS).

The 𝜆-values in Figure 19 shows the number of generated messages per day per SM. The use cases grouped in the dash-dotted box transmit very infrequently with a combined rate of only ~0.5 messages per day. Further, they are relatively similar in terms of latency and payload size. In addition to this group, two other OpenSG use cases from the figure stand out, namely the real-time pricing (RTP) that causes 96 messages per day and the periodic meter reading on the top right. For periodic meter readings, a commercial/industrial (C/I) SM sends reports more often than a residential SM. Notice for the WAMS-SPM reporting that, in addition to the stricter latency requirement of ≤ 1 sec, the number of generated messages per day is many orders of magnitude higher than any of the SM use cases.

downlink uplink

Use case RI default default 5 min 1 min 30 sec 15 sec

Meter Reading 1.25 11K 95K 475K 950K 1.9M

Service Switch 3 6 6 6 6 6

PrePay 3.5 8 8 8 8 8

Meter Events 0 50 50 50 50 50

Islanded Distr. Cust. Storage 2 5 5 5 5 5

DR-DLC 400 0.5 0.5 0.5 0.5 0.5

Premise Network Admin 1 1 1 1 1 1

Price 10K 2.4K 2.4K 2.4K 2.4K 2.4K

Firmware / Program Update 30K 5 5 5 5 5

Total 40.4K 13.4K 97K 477K 952K 1.9M

Table 12: Average downlink/uplink raw data rate as [bytes/meter/day] for the considered use cases. Default value of RI is 4 hours for residential and 1 hour for commercial/industrial SMs.

Table 12 shows that the raw data rate requirements of SMs with default reporting interval (RI) are quite modest, with an average uplink data rate of appr. 13.4KB per day per SM and an average downlink data rate of appr. 40.4KB per day per SM. While the total downlink data rate is actually



higher than the uplink, it is constituted primarily of software updates, which are large low-priority data transfers that occur infrequently during the night, where it does not interfere with the day-to-day operation of the smart grid. Given the modest traffic requirements, it is expected that GPRS networks, that are deployed ubiquitously and offer a reliable coverage, but are gradually becoming less suitable for human-oriented traffic, can easily satisfy the default SM traffic requirements. Further, an option to increase observability in the power grid is to reduce the meter reading reporting interval. We investigate how capable the current cellular systems are to support this in section 2.3.4, when the report packet sizes are respectively 300 bytes and 600 bytes for residential and commercial/industrial and reporting intervals range from 5 min, 1 min, 30 sec, to 15 sec. As shown in Table 12, in case of these reduced RIs, the uplink data rate requirements become much larger than in the downlink.

2.3.2 Enhanced Smart Meter Traffic Model The WAMS-SPM is a PMU-like device for the distribution grid, which is able to measure voltage and current phasors. However, it has less strict real-time requirements than transmission grid PMUs, since it is used to increase observability rather than for protection purposes. Being deployed not only in DSO substations, but also in and close to prosumer homes, the WAMS-SPM reports measurements through cellular networks, since this allows a mobile network operator to prioritize and dedicate resources to WAMS-SPM traffic, thus achieving QoS, which may not be possible 3rd party consumer-grade wired Internet connections. Phasor measurements can be used on different time scales, ranging from a few milliseconds (e.g., for protective relays) up to several seconds (e.g., real-time monitoring and state estimation) [6]. The WAMS-SPMs are intended to improve observability and enable state estimation and real-time control [6], with the suggested lower bound of 1 second for the reporting interval [12]. Since the WAMS-SPMs features and requirements are not yet standardized, the WAMS-SPM traffic model considered in this study is based on the requirements of the transmission grid PMU and WAMS related standards, IEEE 1588, IEEE C37.118 and IEC 68150. Specifically, we assume that every second a WAMS-SPM sends a measurement report that consist of concatenated PMU measurements (50 Hz sample rate) from the preceding 1 second measurement interval. The samples are, as specified in PMU standards IEEE 1588 and C37.118, timestamped using GPS time precision. Assuming that the floating point PMU frame format from IEEE 1588 is used and that each sample covers 6 phasors, 1 analog value and 1 digital value, each PMU frame accounts to 76 bytes. Adding UDP header (8 bytes) and IPv6 header (40 bytes) to each report of 50 PMU samples, a WAMS-SPM packet is 3848 bytes, and a bit rate of 30.8 kbit/s. Since it may be an exaggeration to send all 50 PMU samples per measurement interval, we also consider in our performance analysis the case of WAMS-SPM reduced report sizes.

2.3.3 Cellular Systems Performance From the communications perspective, it is important to investigate which cellular technologies can support the current billing-only smart meter use cases, but also the use cases/services that go beyond the current ones. In [10] and [19] performance analyses were carried out to determine the number of smart grid devices supported by different wireless technologies, however, they only evaluated the data capacity of the systems and neglected to account for the bottlenecks in the access reservation protocol used in cellular systems. As it is shown in [13], the access reservation bottlenecks are particularly prone to exposure with M2M traffic such as smart grid traffic, meaning that a pure data capacity based analysis may lead to overly optimistic results. Therefore, our analysis will include all aspects of the access reservation procedure and compare those results to a data capacity only analysis. For the analysis we will consider the traffic patterns for SM and WAMS-SPM devices. From those traffic models it is clear that the communication requirements of these two device types are orders of magnitudes apart in terms of message frequency and bandwidth, meaning that for WAMS-SPM deployment a more capable technology than GPRS is needed. With its



integrated PMU unit, the WAMS-SPM is already a more complex and expensive device than the SM, and since fewer WAMS-SPMs than SM will be needed, a higher unit price can be better tolerated, and thus we will assume that the WAMS-SPM uses LTE.

2.3.3.1 Access Reservation Protocol Operation and Limitations

The access reservation protocol operation of LTE was already explained in great detail in section 1.4.1.1. GPRS works in much the same way, however there are the following differences. In GPRS there are 217 RAOs/s per carrier while in LTE there are 10.8k RAOs/s5. On the other hand, only 32 grants/s are offered in GPRS, compared to 3k grants/s for LTE [13], [14]. Therefore, when the random access stage is heavily loaded, the grant stage becomes a decisive limitation. Furthermore, in GPRS as in LTE the data stage is not only limited by the amount of the actual data resources, but also by the amount of the uplink identifiers used to coordinate transmissions from active devices, which limits the amount of simultaneously active M2M communication links.

2.3.4 Outage Performance Evaluation To evaluate the performance of the cellular access, we used the outage rate, i.e., the probability of a device failing to deliver a report before the report deadline expires, while accounting for the access reservation protocol. This can be regarded as a measure of the cellular access reliability, which is the paramount performance indicator for the wide-area distribution supervision and control applications [15]. To evaluate the outage we used event-driven simulators developed in MATLAB, which cover the complete access reservation protocol, as defined in 3GPP Release 12. Particularly, the GPRS simulator considers the amount of available access granted messages in the access granted channel (AGCH), with a typical configuration of 28 AGCH/s [13], the limited number of the identifiers used to coordinate the uplink transmissions, i.e., the uplink stage flag (USF), and the amount of data resources available. The LTE simulator considers the restricted amount of access grant messages (RAR messages) due to the physical downlink control channel (PDCCH) limitations [16], [17]. Finally, both in the GPRS and the LTE simulator, the data resources are shared with the signaling required in the access reservation procedure and the actual data transmissions.

Figure 20: GPRS outage evaluation for increasing number of SM with different report interval values and RS = 300 bytes for residential and RS = 600 bytes for commercial/industrial, where ARP+D

denotes the access reservation protocol plus data phase, while D denotes only data phase.

5 Assuming the contention resources occur every 5 ms, each with 54 contention preambles available.



The evaluation scenario is set in a single cell with 1000 m radius, which includes 4500 smart meters [13], from which 90% correspond to residential customers and the remaining 10% to commercial/industrial customers. In the case of GPRS, we consider a single carrier corresponding to a 200 KHz system. The considered LTE bandwidth is 1.4 MHz (6 PRBs), in line with the reduced capabilities for LTE devices [18]. In addition, the control channel and data channel probability of error, are respectively 10−2 and 10−1 [14]. In both systems, we assume the devices always transmit with the highest modulation scheme available, in order to focus the evaluation on the performance of the access reservation protocol. In these conditions, we observed that the SM traffic is supported by both GPRS and LTE with near 0% outage, as the total number of messages per hour from each SM only amounts to approximately 125. We start by considering for GPRS the scenario of reducing SM Report Intervals (RI). Figure 20 depicts the outage probability for increasing number of SMs and different RIs. Taking as reference a cell population of 4500 SMs, we can see that for RI > 5 min, GPRS can provide a significant increase on the distribution network observability from hourly intervals to every 5 minutes. For smaller report intervals to be supported in GPRS, then the options are either to reduce the cell size and/or increase the number of carriers.

Figure 21: LTE and GPRS outage evaluation for increasing penetration of WAMS-SPMs, where ARP+D denotes the access reservation protocol plus data phase, while D denotes only data phase.

We proceed by considering in Figure 21 the cellular network outage as a function of the WAMS-SPM penetration, i.e., of how many WAMS-SPMs are deployed per every 100 smart meter locations. As Mentioned earlier, each WAMS-SPM report contains 50 samples of the power phasors measured since the last report with an expected payload of 3848 bytes. Since this large payload has severe implications on the cellular network performance, we also consider the impact of smaller payloads on system performance, which can be motivated by the introduction of pre-processing to extract statistics, data compression and/or reduced number of samples. Specifically, we consider reduced report sizes (RS) of 3848, 400, and 115 bytes, where the last two values correspond respectively to a payload reduction of approximately 10% and 3% of the original payload size. The outage results for LTE and GPRS are shown in Figure 21. We note that GPRS is not able to support WAMS-SPM traffic irrespective of the chosen RS, while LTE for RS of 3848 bytes only supports up to 2% WAMS-SPM



penetration. When a 10 MHz bandwidth is completely dedicated in LTE to serve the WAMS-SPM traffic then it is possible to reach 30% of penetration with less than 10% of outage, which means a large amount of resources dedicated to a potentially low profit application. On the other hand, if we assume lower RS, already at 400 bytes LTE supports up to 20% of WAMS-SPMs. Further, when comparing the results that correspond to the case when only data phase is taken into account with the results obtained by considering the access reservation phase as well, it can be observed that the access reservation protocol impacts the number of supported WAMS-SPMs. Particularly, the limitations of the access reservation protocol become substantial as the report size decreases and it could shown that this is mainly due to the lack of access grant messages required to complete the access reservation procedure. Note that this effect has been overlooked in the previous works [10], [19]. The presented results allow us to conclude that the RS of the WAMS-SPM nodes must be small to support a high percentage of nodes. In addition, we emphasize that small data traffic cannot be analyzed only in terms of the system data capacity, but that the bottlenecks of the access reservation protocol itself must be considered, as observed in the gap between the two types of analysis depicted in Figure 21. We conclude by noting that in practice, when deploying WAMS-SPMs, due to the required communication reliability, good coverage should be ensured, e.g., by careful selection of the placement location and/or by adding an external antenna if needed. In the above presented study, it is assumed that all SMs and WAMS-SPMs are under a cellular coverage.

2.3.5 Standardization Outlook Although the traffic resulting from smart meters can be easily accommodated into current cellular systems, the same is not observed for the traffic generated by the WAMS-SPM. In the following, we discuss the challenges and possible solutions that need to be tackled by standardization bodies to ensure that the observability of the distribution network can be improved efficiently.

2.3.5.1 Smart Meter

The inclusion of additional phasor measurement units into the distribution grid, so as to increase its observability, is being discussed specifically at the last mile to the customer premises [8]. Currently, it is not yet clear if that will imply the same level of detail (in number of samples and report frequency) as in the transmission grid PMUs, where the reporting is done by SCADA over dedicated wired links. Based on the presented results it is clear that if the WAMS-SPMs generate the same amount of traffic as transmission grid PMUs, then the cellular networks will require an extensive overhaul to be able to support both WAMS-SPM and human centric traffic, leading to substantial investment in the cellular infrastructure. On the other hand, WAMS-SPMs will most likely be lighter versions of PMUs, both sampling and reporting less frequently. Therefore, if local processing and compression of the monitoring data is allowed and/or the required level of detail lowered, then the amount of generated traffic will be much lower. Another viable option is to increase the report frequency of current smart meters without introducing local PMU functionality. The generated small packets could then be handled by the network, as long as the bottlenecks at the access protocol level are addressed. It seems likely that the standardization for the WAMS-SPM’s PMU functionality falls within the scope of the IEEE C37.118 and IEC 68150 standards, since these specify the measurement and communications requirements for traditional PMU units. Therefore, it is of paramount importance that standardization bodies reach a consensus on the WAMS-SPM communication requirements allowing the affected stakeholders to take informed actions.



2.3.5.2 Cellular Network

In 3GPP, the standardization body responsible for the cellular air interface and core network functionality, there are two activities that will affect how the traffic from SM and WAMS-SPM will be handled [18]. We start by noting that, although GRPS is seen as an outdated communication technology [18], there is an ongoing effort to continue to reengineer GPRS to serve M2M applications, in which the SM traffic can be classified. One of the goals of this initiative is to achieve6 160 bit/s. Concurrently, there is a push from the industry (both utilities and vendors) to keep GPRS networks and their associated resources active, while facing the pressure to re-harvest the GPRS spectrum to be used in the next cellular network generation. A viable solution to keep the GPRS connectivity, is to virtualize its air interface into the next generation cellular systems. The second effort is to define a low complexity LTE user equipment category with respect to the cellular interface, which supports reduced bandwidth and transmit power while extending coverage operation [18]. Specifically, the goal of reduced bandwidth is to specify 1.4 MHz operation within any LTE system bandwidth, allowing operators to multiplex reduced bandwidth MTC devices and regular devices within their existing LTE deployments. In terms of extended coverage the goal is to improve the coverage of delay-tolerant MTC devices by 15 dB, thereby allowing operators to reach MTC devices in poor coverage conditions, such as smart meters located in basements [18]. To further improve the support of the traffic generated by SM and WAMS-SPM with very low duty cycle and latency requirements in the order of seconds, the inclusion of periodic reporting and discontinuous transmission functionality into cellular standards should be considered. In here, the network provides periodic communication resources so that devices can perform their short data transmission. This allows devices to go to sleep and save energy, since they have prior knowledge of when the next transmission time slot can occur. A solution based on this concept has been proposed through the reengineering of the LTE access protocol [14]. To cope with the WAMS-SPM traffic demands and increase the network capacity, localized aggregation of traffic should be considered. In this solution the traffic generated by multiple SMs and WAMS-SPMs in a geographical area could be aggregated, at WAMS-SPMs or cellular relays, and then trunked to the cellular network [20]. The use of aggregation and relaying would then allow to decrease the contention pressure at the base station, as well as to improve the single link connection, providing connectivity and coverage enhancements to SMs and WAMS-SPMs with poor propagation conditions. Finally, to support massive asynchronous access of small packet transmissions, access reservation protocols in cellular systems are just the first step of the asynchronous access to the network. After it has been completed, then the device starts exchanging signaling information via the higher layers with the entities in the core network, which leads to a high signaling overhead and possible air interface and core network congestion. Although there are already efforts to reduce the signaling exchanges with the core network [16], when the payloads are small enough, the facility to perform the data transmission already in the third step of the access reservation protocol should be in place.

2.3.6 Conclusions In this study we have evaluated two approaches to increase the observability of the network: (1) decreasing the report interval of the meter reading and (2) introduction of enhanced smart meters with phasor measurement units (PMUs). We provided details on the characteristics of the traffic generated by smart meters and enhanced smart meters and have highlighted the associated challenges in supporting it from a cellular network point of view. The obtained results show that GPRS can support traditional smart meter traffic, as well as more frequent measurements down to 5 min report intervals. Further, it is shown that LTE can support distribution grid PMUs, if the report

6 Considering the minimum SDU size, i.e. 80 bytes, with 4 seconds latency.



payloads are appropriately dimensioned. These results can be used as input for both smart meter and cellular system standardization bodies to enable the introduction of current and future smart grid devices into the cellular networks. The current main open issue is the uncertainty associated with the WAMS-SPM communication requirements, which will lead to different cellular systems optimizations.

2.4 Model Based Estimation of LTE Random Access Capacity

The study presented in this section is based on [21]. In this work we propose an analytical model of the transmission failure probability in an LTE cell for sporadic uplink transmissions carried over the LTE random access channel. The purpose of the proposed model is to be able to estimate the capacity in terms of the number of terminals or RACH arrival density that can be supported by LTE in a given configuration while accounting for retransmissions as well as modelling the bottlenecks that appear in the contention phase and the AG phase. This is a major contribution of the work, as the existing models do not capture these bottlenecks. Three other contributions are: 1) an iterative procedure to determine the impact of retransmissions using a Markov chain model of the retransmission and backoff procedure; 2) analytical derivations of the metrics based on a Markov chain, thereby achieving an analytical model that can be evaluated at click-speed; 3) analysis of the protocol breaking point using ever increasing access loads to the network. In the literature, analytical models of the preamble collision probability have already been considered in standardization documents [23], [24], and [25] and scientific papers [26] and [27]. In [28] the preamble collision probability is used to estimate the success probability of transmission attempts. However, we have found that existing models are incomplete and inaccurate and in this work we introduce a superior model that closely matches the system outage breaking point of the detailed simulation. The second limitation in the AG phase has been considered separate from collisions in [29] for burst arrivals following the Beta distribution, which is a valuable result for situations where many alarm messages are sent simultaneously. In [27] the authors present an approach to cell planning and adaptation of PRACH (Physical Random Access Channel) resources that only takes into account the preamble collisions. As we show in this section, the AG phase is a limiting factor before the amount of preamble collisions becomes an issue, since the impact of occasional collisions is effectively diminished with retransmissions. In [30] the authors present an analysis accounting for preamble collision and the AG phase, which however does not consider retransmissions.

2.4.1 Modeling the Access Reservation Protocol We now go to the analysis of the access reservation procedure. First, we model the One-shot transmission and then extend it to the 𝑚-Retransmissions model. The numerical results are from the complete model that accounts for both one-shot transmissions and retransmissions, as depicted in Figure 4.

2.4.1.1 One-Shot Transmission Model

We are interested in characterizing how often a transmission from a UE fails. This happens when the transmission is not successful in both the preamble contention and AG phases, i.e., a request from the UE must not experience a preamble collision and the uplink grant must not become stale and dropped. We model this as two independent events:

𝑝f(𝜆T) = 1 − (1 − 𝑃coll(𝜆T))(1 − 𝑃drop(𝜆A)), (1)

where 𝑃coll(𝜆T) is the collision probability in the preamble contention phase given UE request rate 𝜆T, while 𝑃drop(𝜆A) is the probability of the uplink grant being dropped from the AG queue given

preamble activation rate 𝜆A.



2.4.1.1.1 Preamble Contention Phase

We start by computing 𝑃coll(𝜆T). Let 𝑑 denote the number of available preambles (𝑑 = 54). Let the

probability of not selecting the same preamble as one other UE be 1 −1

𝑑. Then the probability of a

UE selecting a preamble that has been selected by at least one other UE given at 𝑁T contending UEs, is:

ℙ( 𝐶𝑜𝑙𝑙𝑖𝑠𝑖𝑜𝑛 |𝑁T) = 1 − (1 −1

𝑑)𝑁T−1

. (2)

Assuming Poisson arrivals with rate 𝜆T, then:

𝑃coll(𝜆T) = ∑+∞𝑖=1 [1 − (1 −

1

𝑑)𝑖−1

⋅ ℙ(𝑁T = 𝑖, 𝜆T ⋅ 𝛿RAO)] (3)

≤ 1 − (1 −1

𝑑)

𝜆T⋅𝛿RAO−1,

where ℙ(𝑁T = 𝑖, 𝜆T ⋅ 𝛿RAO) is the probability mass function of the Poisson distribution with arrival rate 𝜆T ⋅ 𝛿RAO. The inequality comes from applying Jensen’s inequality to the concave function 1 −(1 − 1/𝑑)𝑥, where 𝜆T is the total arrival rate (including retransmissions), and 𝛿RAO is the average number of subframes between RAOs.7 The resulting expression in (3) is thus an upper bound.

2.4.1.1.2 Access Granting Phase

The mean number of activated preambles in the contention phase per RAO, is given by 𝜆A. As discussed in section 1.4, we assume that the eNodeB is unable to discern between preambles that have been activated by a single user and multiple users, respectively. This will lead to a higher 𝜆A, than in the case where the eNodeB is able to detect the preamble collisions. The main impact of this assumption is that there will be an increased rate of AG requests, even though part of these correspond to collided preambles, which even if accepted will lead to retransmissions. The 𝜆A can be well approximated, while assuming that the selection of each preamble by the contending users is independent, by,

𝜆A = [1 − ℙ(𝑋 = 0) − ℙ(𝑋 = 1)] ⋅ 𝑑, (4) where ℙ(𝑋 = 𝑘) is the probability of k successes, which can be well approximated with a Poisson distribution with arrival rate 𝜆T/𝑑, i.e.:

ℙ(𝑋 = 𝑘) ≈(𝜆T/𝑑)𝑘𝑒−𝜆T/𝑑

𝑘!. (5)

To compute the outage probability due to the limitation in the AG phase, i.e., due to the maximum number of uplink grants per subframe and a maximum waiting time of 𝑡RAR subframes, we consider that this subsystem can be modeled as a queuing system. We assume that the loss probability 𝑃drop(𝜆A) can be seen as the long-run fraction of costumers that are lost in a queuing system with

impatient costumers [37]. In LTE, pending uplink grants are served with a deterministic time interval (1 subframe) between each serving slot. A straightforward approach would be to use an M/D/1 model structure, as presented in [37], in order to compute the drop probability. Unfortunately, the expression to compute 𝑃drop(𝜆A) for the M/D/1 queue does not have a closed form solution. However the

equivalent expression for the M/M/1 queue has a closed form solution [37]. We have compared the results of the two model types and found no noticeable difference in the computed outage numbers in practice. Thus, in the following we use the M/M/1 model to compute 𝑃drop(𝜆A):

𝑃drop(𝜆A) =(1−𝜌)⋅𝜌⋅Ω

1−𝜌2⋅Ω, withΩ = 𝑒−𝜇⋅(1−𝜌)⋅𝜏q . (6)

where 𝜌 =𝜆A

𝜇 is the queue load, 𝜇 is the number of uplink grants per RAR (𝜇 = 3), with 𝜏q = 𝑇d −

1

𝜇

and 𝑇d is the max waiting time (in terms of requests) in the uplink grant queue, i.e., 𝑇d = 𝜇 ⋅ 𝑡RAR.

7 E.g., 𝛿RAO = 1 if 10 RAOs per frame and 𝛿RAO = 5 if 2 RAOs per frame.



The fact that we are using an M/M/1 model instead of an M/D/1 model, may cause a discrepancy between the simulation and model results when the queue becomes congested (𝜌 > 1). However, we are interested in the switching point (𝜌 = 1) from which we then estimate accurately the outage breaking point, as shown in the results in section 2.4.2.

2.4.1.2 𝒎-Retransmissions Model

When UEs are allowed to make retransmissions the probability of an UE becoming in outage is the probability that none of the allowed 𝑚 + 1 transmissions attempts are successful. When retransmissions are allowed (𝑚 > 0), the total arrival rate 𝜆T must include the extra arrivals caused by the UE’s retransmissions. The number of retransmissions is however a result of the transmission error probability, which in turn depends on the number of retransmissions. This chicken and egg problem can be solved iteratively using a derivative of the Bianchi model [74] applied to our system model. Specifically, we are using a model adapted to LTE, with a structure similar to the one presented in [75]. The following derivations of the number of transmissions and outage probabilities have, to the best of our knowledge, not been presented previously.

Figure 22: Markov Chain backoff model to estimate number of required transmissions. The equilibrium states in the red dashed box are used to calculate the mean number of required

transmissions.

The mean number of required transmissions 𝑁TX and outage probability 𝑃outage, are computed with

help of the Markov chain model depicted in Figure 22. In the Markov chain model, the state index {𝑖, 𝑘} denotes the 𝑖th transmission attempt stage and 𝑘th backoff counter. The number of allowed retransmissions is given by 𝑚. In the following we present the derived results. See [21] for the detailed derivations. Since a transmission will eventually either finish successfully in the connect state or unsuccessfully in the drop state, the outage probability can be computed as:



𝑃outage = 𝑝f𝑚+1, (7)

The number of required transmissions is estimated as:

𝑁TX(𝜆T) =1−𝑝f

𝑚+1

1−𝑝f. (8)

From the number of transmissions, the value of 𝜆T can be solved iteratively using the fixed point equation:

𝜆T = 𝑁TX(𝜆T) ⋅ 𝜆I = 𝜆I1−𝑝f(𝜆T)𝑚+1

1−𝑝f(𝜆T). (9)

For the results presented in the following section we found that less than 20 iterations were needed to reach convergence (less than 1% change between consecutive iterations).

2.4.2 Evaluation results In the following we present the evaluation results from this work. We consider two different PRACH configurations, namely the typical configuration with 5 subframes between every RAO [76] and the most capable configuration with one RAO every subframe. Furthermore, we consider first the case where only a single transmission is allowed (one-shot, 𝑚 = 0) and then the more realistic configuration of 𝑚 = 9 allowed retransmissions. The results here presented are compared with a simulator than implements the full LTE access reservation protocol as defined in [33], [34] with the parameters in Table 13.

Table 13: LTE simulation and model parameters.

Parameter Value

Preambles per RAO (d) 54

Subframes between RAOs (𝛿RAO) 1 or 5

Max number of retransmissions (𝑚) 0 or 9

Uplink grants per RAR (𝜇) 3

System bandwidth 5 MHz

eNodeB processing time 3 ms

MSG 2 window (𝑡RAR) 5 ms or 10 ms

Contention time-out (𝑡CRT) 48 ms

Backoff limit (𝑊c) 20 ms

UE processing time 3 ms

2.4.2.1 One-shot Transmission (𝒎 = 𝟎)

In Figure 23(a) and Figure 24(a) the outage probabilities are depicted for 𝑚 = 0. There, the proposed model has a much better fit to the simulation results than the 3GPP TR 37.868 model [25] and the Ericsson model in [27]. Specifically, in Figure 23(a) where the preamble collisions are the main error cause, the TR 37.868 and Ericsson models are much worse than the proposed model. From Figure 24(a) it is clear that those models are not accounting for the AG limitation that starts to have an impact around 𝜆I = 2700 attempts/sec, causing an upward bend in the outage curve.

2.4.2.2 𝒎 = 𝟗 Retransmissions

In the typical configuration where retransmissions are allowed, a necessary feature of our model is that it is able to account for the feedback impact of retransmissions on the arrival rate 𝜆T. An intermediate metric that allows to study this is the number of transmissions per new data packet 𝑁TX. This is shown in Figure 23(b) and Figure 24(b). In Figure 23(b) the number of transmissions is estimated accurately leading to a well-fitting estimation of the outage in Figure 23(c). For the case of 10 RAOs per frame, the Markov chain model slightly overestimates the number of transmissions.



However, the breaking points in the curves are the same, meaning that the supported arrival rate in the simulation in Figure 24(c) closely matches the one in the model. Finally, the results show that the proposed model is superior to the existing models in the literature, as they do not capture the feedback impact of the retransmissions and are therefore not able to estimate the system outage capacity. The presented results also reveal an interesting insight in dimensioning the LTE access reservation parameters. Given that there is a 5 times difference in resource usage for RAOs (2 vs 10 RAOs per frame), the gain in supported arrival rate 𝜆I is quite modest, increasing from around 𝜆I = 2250 to around 𝜆I = 2800, i.e., a 25% increase. In order to further increase the capacity of the system, it is necessary to simultaneously increase the number of RARs per subframe.

(a) (b) (c)

[Outage probability, 𝑚 = 0] [Number of transmissions, 𝑚 = 9] [Outage probability, 𝑚 = 9]

Figure 23: Plots for RACH configuration with 2 RAOs per frame (𝛿RAO = 5). Ericsson model refers to [27].

(a) (b) (c)

[Outage probability, 𝑚 = 0] [Number of transmissions, 𝑚 = 9] [Outage probability, 𝑚 = 9]

Figure 24: Plots for RACH configuration with 10 RAOs per frame (𝛿RAO = 1). Ericsson model refers to [27].

2.4.3 Conclusions In this work we have presented a low-complexity, yet accurate model to estimate the outage capacity of the LTE access reservation protocol for machine-type communications, where the small payload sizes means that the system resources are typically not the limiting factor. The model accounts for both contention preamble collisions and the limited number of uplink grants in the random access response message, as well as the feedback impact that the resulting retransmissions has on the random access load. For the considered typical LTE configurations, the model is able to very accurately estimate the system outage capacity. This puts it forward as a useful tool in system dimensioning, as it allows replacing time-consuming simulations with click-speed calculations.



Future work should look into how diverse channel conditions and diverse traffic patterns of users can be efficiently included in the model. While the outage metric is very important from a planning perspective, other metrics such as access delay or transmission time would be very relevant to be able to estimate accurately when considering real-time machine-type communications.



3 Co-existence of M2M and non-M2M devices in Cellular Networks

Since the smart grid communication in SUNSEED is envisioned to be served through the public LTE networks, it is necessary to investigate in the first place if the co-existence can lead to problems for the smart grid communication and second, how unproblematic co-existence can be ensured. In this chapter we first consider this generically for cellular networks and second more specifically for LTE, where we propose specific enhancements of LTE to ensure co-existence that does not allow human-type traffic to jeopardize critical smart grid applications.

3.1 Cellular M2M Network Access Congestion: Performance Analysis and Solutions

M2M communications are the direct communications between devices without human involvements. As an enabling technology for a wide range of IoT applications such as smart metering, M2M has drawn huge attentions in the past few years. Driven by the consumers’ demand for new services and enterprises’ need to reduce costs, significant growth in the M2M market during the next few years has been predicted. With the rapid development of the M2M landscape, it is inevitable to use the ubiquitous cellular infrastructure to facilitate M2M communications. Recently, M2M has been intensively discussed in the cellular standardization bodies such as 3GPP LTE, as mobile operators and vendors are seeking new services and applications for sustainable profits for the next decade. M2M over cellular is one of the best options to connect diverse devices over great distances by using established, ubiquitous and robust networks with proven technologies. Basically, to facilitate M2M communications, a cellular network has to meet the following requirements:

support of extremely high number of devices, with different traffic characteristics,

support of various latencies, high reliability and low power consumption,

support of different mobility profiles, and

no negative impact on human to human (H2H) communications. One of the most critical challenges in cellular M2M is the network access congestion problem, where massive M2M accesses happen in a very short time and consequently cause very low access success probability and long access delays [1]. The huge amount of M2M devices in a cell could cause severe congestion in network access. For a given wireless communication system, multiple access protocols have huge impact on the system performance. Basically there are two kinds of contention-based access protocols. One is the carrier sense multiple access (CSMA) protocol based on the principle of listen before talk. The other is the Aloha-based access protocol [3], in which a user simply transmits whenever it wants to. Normally, CSMA-based access is more efficient than Aloha-based access and is widely used in short-range networks such as Wi-Fi. However, due to the hidden node problem [4], it is not suitable for networks with large cell sizes such as cellular and satellite systems. To improve system efficiency, access requests in cellular systems (from 2G to 4G) are typically based on Aloha random access. We analyse the cellular random access protocol and propose some new solutions. First the impact on the slotted Aloha performance due to M2M applications is studied. Secondly, two solutions, namely the use of transmission probability control (TPC), as well as two-hop Aloha, are proposed.



3.1.1 Analysis Suppose there are 𝑀 users and each user sends the request message with an arrival rate 𝜆′, and the total arrival rate (transmission attempts) will be 𝜆 = 𝜆′𝑀. Assume there are 𝑁 opportunities for the users to send the request, where an opportunity can be a narrow band, an access code in a given time slot, or a combination of both, depending on the cellular system in place. The arrival of independent transmissions can be modelled as a Poisson distribution, i.e., the probability that 𝑘 transmissions occur is given as

𝑃(𝑘) =(

𝜆

𝑁)𝑘𝑒

−𝜆𝑁

𝑘! (1)

Now the throughput of successful transmission in one opportunity is defined as the probability of having just one transmission, i.e., 𝑃(1):

𝑇𝑐 =𝜆

𝑁𝑒−𝜆/𝑁 (2)

Then the throughput of all the opportunities can be calculated as

𝑇 = 𝑁𝑇𝑐 = 𝜆𝑒−𝜆

𝑁 (3)

When M2M users are integrated into cellular networks, they have to compete with conventional non-M2M users during the random access procedure. Given the total number of users 𝑀, all the users are divided into 2 sets for M2M users and non-M2M users as ℂ𝑖 (1 ≤ 𝑖 ≤ 2), and 𝑀 =

∑ 𝑀𝑖 2𝑖=1 , where 𝑀𝑖 = |ℂ𝑖|. Denote 𝜆𝑖

′ as the transmission rate of each user in ℂ𝑖 , then the arrival

rate for the 𝑖𝑡ℎ set will be 𝜆𝑖 = 𝜆𝑖′𝑀𝑖 and the total arrival rate will be 𝜆 = ∑ 𝜆𝑖

2𝑖=1 . Now we will

analyze the impact on the non-M2M users when the M2M users share the opportunities with the non-M2M users. Based on (3), we can get the access throughput as

𝑇 = 𝜆𝑒−𝜆

𝑁 = 𝑒−

𝜆

𝑁 ∑ 𝜆𝑖

2𝑖=1 = ∑ 𝑇𝑖

2𝑖=1 (4)

where 𝑇𝑖 = 𝜆𝑖𝑒−

𝜆

𝑁 is the access throughput of the 𝑖𝑡ℎ class set. Figure 25 shows the throughput of

non-M2M users vs. the number of M2M users with different number of opportunities (8 and 16), assuming each user’s arrival rate is 0.2 and there are 20 non-M2M users. It clearly demonstrates that the throughput of non-M2M users will degrade significantly with the increasing number of M2M users.



Figure 25: Non-M2M throughput vs. number of M2M users

In [2], dedicated opportunities have been proposed, where the M2M users are allocated some dedicated opportunities to access the network instead of sharing the same opportunities with non-M2M users. Dedicated opportunities can surely mitigate the impact on the non-M2M users, but network access congestion still remains for the M2M users. Backoff has also been suggested to avoid access collision, whereas a user will retransmit the request at a later time slot through a given backoff policy if a collision happens. In the following, two methods are investigated to specifically address the M2M network access congestion problem. One is the transmission probability control strategy and the other is the two-hop slot Aloha algorithm.

3.1.1.1 Transmission probability control (TPC)

As mentioned before, M2M enables a wide range of applications from smart metering to fleet management, all having very different traffic characteristics. Some applications may need to access the network almost in real time while others are delay-tolerant. According to application’s quality of service (QoS) requirement, the base station (BS) can impose a transmission probability. This is also known as access class barring in the 3GPP literature. The access throughput and delay are directly related to the total arrival rate, which is composed of the individual arrival rates and the number of users. The individual arrival rates can be controlled by

the BS through a transmission probability as shown in Figure 26, where a predefined value 𝛼8 is assigned to all the users or a given set of users. Whenever a user wants to send a request, it will generate a random value 𝛽 with uniform distribution in the range of (0, 1), and compare it with 𝛼. If 𝛽 < 𝛼, the request will be sent straightaway; otherwise, the user has to wait for the next time slot and repeat the aforementioned steps.

8 𝛼 is a random value set by the BS for access control

0 20 40 60 80 100 120 140 160 180 2000

0.5

1

1.5

2

2.5

3

3.5

4

M2M User number

No

n-M

2M

Th

rou

gh

pu

t

Opt num 8

Opt num 16



Figure 26: Random access with transmission probability control

Here we only analyse the case when 𝛼 is used for all the 𝑀 users each with arrival rate of 𝜆′. The total arrival rate will become 𝛾 = 𝛼𝜆 and similar to (3) the throughput for all the opportunities is given as

𝑇𝑡𝑝𝑐 = 𝛾𝑒−𝛾

𝑁 (5)

The new arrival rate 𝛾 is the actual arrival rate to the base station, and the delay with the new arrival

rate is 𝐷𝑡𝑝𝑐′ = 𝑒𝛾/𝑁. With transmission probability control, every attempt with the original arrival

rate 𝜆 will have a probability of 𝛼 to become an attempt with arrival rate, hence it can be observed that the delay 𝐷𝑡𝑝𝑐 with the original arrival rate 𝜆 is given as 𝛼𝐷𝑡𝑝𝑐 = 𝐷𝑡𝑝𝑐

′ , i.e.,

𝐷𝑡𝑝𝑐 =𝑒𝛾/𝑁

𝛼 (6)

3.1.1.2 Two-hop M2M random access

The second solution to the network access congestion problem is to divide the users into clusters or groups, and the users in the same cluster have to go through a gateway or aggregator to access the BS, which makes the access procedure become a two-hop Aloha protocol. Figure 27 shows a two-hop M2M network, where we assume only the users generate the requests and the gateways are only responsible for collecting and forwarding the request to the base station. Assume there are 𝐼 clusters

each having 𝑀𝑖 users with arrival rate of 𝜆𝑖′, then the total number of users is 𝑀 = ∑ 𝑀𝑖

𝐼𝑖=1 and the

total arrival rate at BS will be

𝜆 = ∑ 𝜆𝑖 𝐼𝑖=1 (7)

where 𝜆𝑖 is the arrival rate from the gateway of cluster 𝑖, which is equal to the access throughput in cluster 𝑖:

𝜆𝑖 = 𝑀𝑖𝜆𝑖′𝑒−

𝑀𝑖𝜆𝑖′

𝑁 (8)

Hence the access throughput at the BS is

𝑇𝑡𝑤𝑜−ℎ𝑜𝑝 = 𝜆𝑒−𝜆

𝑁

= ∑ 𝑀𝑖𝜆𝑖′𝑒−

𝑀𝑖𝜆𝑖′

𝑁 𝐼

𝑖=1 𝑒−∑ 𝑀𝑖𝜆𝑖

′𝑒−

𝑀𝑖𝜆𝑖′

𝑁 𝐼

𝑖=1𝑁

(9) Then the average access delay from a user to the BS is composed of the average access delay from the user to the gateway 𝐷𝑢𝑠→𝑔𝑤 and the average delay from the gateway to the BS 𝐷𝑔𝑤→𝑏𝑠, which

can be calculated as 𝐷𝑡𝑤𝑜−ℎ𝑜𝑝 = 𝐷𝑔𝑤→𝑏𝑠 + 𝐷𝑢𝑠→𝑔𝑤

Next time

No

No

𝛼

𝑠𝑒𝑛𝑑 𝑟𝑒𝑞𝑢𝑒𝑠𝑡

User BS

𝛽 ∈ 𝑈(0,1)

𝛽 < 𝛼

Yo



= 𝑒𝜆/𝑁 +1

𝐼∑ 𝑒𝑀𝑖𝜆𝑖

′/𝑁𝐼𝑖=1

= 𝑒∑ 𝑀𝑖𝜆𝑖

′𝑒−

𝑀𝑖𝜆𝑖′

𝑁 𝐼

𝑖=1𝑁 +

1

𝐼∑ 𝑒𝑀𝑖𝜆𝑖

′/𝑁𝐼𝑖=1 (10)

Figure 27: A two-hop M2M network

3.1.1.3 Dynamic random access

The TPC or two-hop solution can be enhanced to maximize the throughput by dynamically controlling the transmission probability 𝛼 for TPC or optimizing the number of groups 𝐼 for the two-hop method.

3.1.1.3.1 Dynamic TPC

We need to find 𝛼∗ such that 𝛼∗ = arg max

0<𝛼≤1𝑇𝑡𝑝𝑐 (11)

By setting the derivative of 𝑇𝑡𝑝𝑐 with respect to 𝛼 to zero, 𝛼∗ is obtained as

𝛼∗ = min (𝑁

𝜆, 1) (12)

3.1.1.3.2 Dynamic two-hop access

We need to find 𝐼∗ such that 𝐼∗ = arg max

𝐼∈ℕ+𝑇𝑡𝑤𝑜−ℎ𝑜𝑝 (13)

where ℕ+is the set of natural numbers excluding zero. Similarly, by setting 𝜕𝑇𝑡𝑤𝑜−ℎ𝑜𝑝

𝜕𝜆= 0, we can get

∑ 𝑀𝑖𝜆𝑖′𝑒−

𝑀𝑖𝜆𝑖′

𝑁 = 𝑁 𝐼∗

𝑖=1 (14)

3.1.2 Performance comparison The system performance is studied in terms of access delay and access throughput. It is assumed that there are 16 radio access opportunities and varying number of users, and each user has the same arrival rate of 0.8. There are five cases to compare: “Normal” means conventional slotted Aloha, “TPC” is the TPC method using fixed transmission probability, i.e., 𝛼 = 0.4, “Two-hop” is the two-hop access method using fixed number of groups, i.e., 𝐼 = 5, and “TPC-Op” and “Two-hop-Op” are dynamic TPC and dynamic two-hop access opportunity respectively. In the following, we are mainly interested in the performance with larger number of users, i.e., 𝑀 > 30. Figure 28(a) illustrates the access delay with different number of users. Compared with the “Normal” case, access delay can be significantly reduced even with fixed transmission probability. We can see that dynamic TPC can further improve the delay, while the two-hop solutions with either fixed grouping or dynamic grouping provide the lowest delays. The throughputs vs. number of users are depicted in Figure 28(b), where the dynamic TPC or two-hop solutions outperform the others with a large margin. As expected, the dynamic solutions are adaptive to the number of users and always

… … …

BS

Gateway

User …….

…



provide the best throughputs. Even the fixed two-hop access method can give a much better throughput than the “Normal” method.

Figure 28: (a) Delay vs. number of users (b) Throughput per opportunity vs. number of users

3.1.3 Conclusions This section has studied random access of M2M communications in cellular networks to address the network access congestion problem. Two methods, namely transmission probability control and two-hop access Aloha, have been investigated. Performance evaluation shows the proposed solutions can significantly improve the access delay and throughput of M2M applications.

3.2 Massive M2M Access with Reliability Guarantees in LTE Systems

Among the major drivers for the evolution of current cellular networks towards the fifth generation (5G) is the efficient support of Machine-to-Machine (M2M) communications and services. Different from human-centric services (H2x), which are mainly characterized by the ever-increasing data rates, M2M services pose a different set of challenges, associated with the support of a massive number of users exchanging small amounts of data, often with requirements in terms of reliability and availability. A model for a particularly demanding M2M scenario is the one where the cellular network access should be offered with reliability guarantees in the case of massive almost-simultaneous arrivals. An example is correlated reporting of an alarm event by tens of thousands of devices in a cell [88]. The main concern in such scenarios is the overload of the cellular access infrastructure, i.e., the collapse of the RACH, which happens due to the signalling overhead associated with each individual transmission [89]. We note that the RACH overload precludes any service operation, i.e., blocks the system, and it is therefore of paramount importance to prevent it. Several methods have been recently proposed to prevent the RACH overload in LTE [86], in the context of M2M communications. Specifically, two main solutions are the extended access class barring (EAB) [82] and dynamic allocation [84]. EAB is valid only for delay-tolerant M2M traffic and is an extension of the standard access class barring method. On the other hand, dynamic allocation is a straightforward approach: upon detection of RACH overload the number of RAOs per second is increased. However, both schemes have inherent limitations, as they are both reactive and triggered upon RACH overload detection. Once the overload is detected, there is an additional delay until the EAB or the dynamic allocation feedback messages are delivered from the BS to the M2M devices, which can take up to 5 s [83], as these messages are typically broadcasted periodically over the paging channel. Therefore, these two methods cannot ensure timely and reliable operation in M2M scenarios with massive synchronous arrivals, as it becomes apparent further in this text.

0 50 100 15010

0

101

102

103

104

User number

Dela

y

Normal

TPC

Two-hop

TPC-Op

Two-hop-Op

0 50 100 150 200 250 300 350 4000

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

User NumberT

hro

ug

hp

ut

Normal

TPC

Two-hop

TPC-Op

Two-hop-Op



Motivated by the deficiencies of the reactive approaches, in this work we propose a proactive approach for the reliable support of M2M service. The proposed approach consists of two phases, an estimation phase and a serving phase, which reoccur periodically. In the first phase, the BS estimates how many M2M devices are attempting to access. We show that by using an estimator that is tuned to the LTE access mechanisms this can be done in a simple and, more importantly, fast manner, requiring just a single RAO to estimate the number of accessing users in the order of tens of thousands. Following the estimation phase, the parameters of the access mechanism are tuned such that the RAOs of the serving phase are used in an efficient way, providing a reliable service. The proposed solution can be easily incorporated in the standard LTE access mechanism, leaving the radio interfaces intact and used both for the case of massive synchronous arrivals as well as the asynchronous traffic with Poisson arrivals. In this way the mobile operators can provide M2M service in a controlled manner, with guaranteed reliability and no overload, i.e., the operators can be provided with a technical data-sheet indicating the performance of the system for a given number of devices and the associated latency. This is a significant step towards reliable M2M services in LTE, which are currently based on the best effort approach.

3.2.1 Proposed Solution The core of the proposed solution consists of a reoccurring access frame, which is composed of RAOs that are dedicated to M2M devices.9 It is assumed that the arrival process is gated, i.e., new arrivals are accepted at the frame beginning and all arrivals during the frame wait for the beginning of the next one. The frame time duration is assumed to be fixed and limited to half of the maximum allowed delay 𝜏 guaranteed by the network operator. The frame is then composed by up to 𝐿 M2M dedicated RAOs within 𝜏/210. Obviously, a larger 𝐿 implies a longer delay, but it also accommodates more devices.

Figure 29: Proposed access frame consisting of an estimation RAO followed by S≤L-1 serving RAOs.

The frame consists of two parts, dedicated to the estimation and serving phase, as depicted in Figure 29. We design the estimation part such that it consists just of a single RAO and describe in the following the proposed estimation technique, showing that a huge range in the number of accessing M2M devices 𝑁 can be reliable estimated.11 The length of the serving phase 𝑆 is determined by the

estimated number of arrivals �̂�, with the constraint that 𝑆 ≤ 𝐿 − 1. The access algorithm in the

serving 𝑆 is based on the standard LTE RACH operation, but tuned to �̂� such that its resources, i.e., RAOs, are used so that the required reliability 𝑅𝑟𝑒𝑞 is met. Particularly, we distinguish two modes of

operation in the serving phase. In the first mode, the length required by the target reliability 𝑆req is

lower or equal to 𝐿 − 1 and the actual length is set to 𝑆 = 𝑆req. In the second mode 𝑆req > 𝐿 − 1,

which implies that there are not enough resources to provide required service. In this case, the length of the serving phase is set to 𝑆 = 𝐿 − 1, and a barring factor is introduced to prevent RACH overload.

9 The use of dedicated resources for M2M has been proposed previously in [84] and [90], in an attempt to prevent M2M RACH accesses from affecting H2x services. 10 Assuming the H2x dedicated RAOs occur every 5 ms [83], then within a 𝜏/2 = 0.5 seconds, there will be up to 𝐿 = 400 available RAOs for M2M access, i.e., 8 RAOs per LTE frame. 11 We note that the approach grants straightforward extension to cover the cases when the estimation phase consists of two or more RAOs.

Estimation Phase RAO

Serving Phase RAOs( S ≤ L-1 )



For the sake of simplicity we skip the derivation of the algorithm and refer the reader to [81] for the actual description of this algorithm. The estimator performance is shown in Figure 30, where it can

be observed that the E[N̂] follows closely the actual value of N.

Figure 30: Proposed estimator performance when the expected arrival rate is not a priori known. Through exhaustive numerical search it was found that for a dynamic range between N=[1,30000]

the optimal values of the estimator parameters are p0=0.001 andα=1.056.

3.2.1.1 Practical Implementation

All the information required by the devices to attempt access is broadcasted, similarly to the EAB, in a new system information message (SIB) [85] that takes place in each access frame, immediately after the estimation RAO. This SIB message includes the following information: First it indicates in which subframe the upcoming estimation RAO will take place together with the values of 𝑝0 and 𝛼 and the number of preambles 𝐽. Further, it informs the contending devices of the number of RAOs in the serving phase and 𝑆1. Finally, a bitmap is included which indicates in which subframes these RAOs will occur. If the load exceeds the amount of capacity pre-reserved by the operator, the barring factor 𝑄 is also included in the SIB, to prevent the RACH overload. The proposed scheme operation is then as follows. Assume that 𝑁 contending devices become active prior to start of the access frame. When the estimation RAO occurs, each of these 𝑁 devices attempt

access enabling the eNodeB to obtain the estimation of the number of arrivals �̂�. The detection of collisions in the estimation phase is performed during the execution of the Access Reservation Procedure. Namely, after the devices that have selected the same random access preamble, transmit

their UE request, which will result in a collision. Based on �̂�, the eNodeB then defines how many RAOs are required in the serving phase to reach the contracted 𝑅𝑟𝑒𝑞 and informs the devices where

these RAOs will occur by broadcasting the corresponding SIB. Then, the contending devices select randomly between the serving RAOs, using the ARP. In the meantime, other contending devices become active, which will wait until the start of the next access frame before proceeding in the same way. We note that the proposed scheme requires minimal changes to the current LTE protocol, with no modifications to the physical layer at all.

3.2.2 Case Study for Two M2M Traffic Classes We now consider a case study with two traffic classes characterized by different requirements and serving probabilities. Let traffic class 1 (TC1) and traffic class 2 (TC2), have a respective reliability

requirement 𝑅𝑟𝑒𝑞(1)

and 𝑅𝑟𝑒𝑞(2)

. Further, let TC1 have priority access to the available serving RAOs over

TC2, e.g., alarm reports take priority over periodic reporting in the context of smart metering.



Specifically, we try first to reach as close as possible to 𝑅𝑟𝑒𝑞(1)

and only then as close as possible to

𝑅𝑟𝑒𝑞(2)

. Furthermore, we assume that each class has separate estimation and serving phases, as

depicted in Figure 31. At the beginning of the frame there is a single estimation RAO for each traffic

class, where the number of contending devices of each class is estimated to be �̂�1 and �̂�2

respectively. With the knowledge of �̂�1, �̂�2 we define a resource allocation strategy based on the scheme described in our detailed paper [81].

Figure 31: Proposed access scheme for two traffic classes TC1 and TC2, where TC1 has priority over TC2.

The access frame duration – demarcated by the estimation phase RAOs occurrence – is constrained by the traffic class with the most stringent latency requirement, here given by TC1. Although, in this study we consider that both TCs have an estimation phase in each access frame, we note that in the case where TC2’s latency requirement is much larger than TC1’s, it might be worthwhile to consider the case where TC2 estimation RAO only occurs in some of the access frames, in order to optimize the amount of RAOs dedicated for estimation.

3.2.2.1 Performance Results and Discussion

The performance results are obtained from a LTE event-driven simulator implemented in MATLAB, which models the complete access reservation procedure. For the same network conditions, we compare the performance of the legacy LTE with dynamic allocation12 with the performance of the proposed scheme. The system parameters of interest for the legacy system are listed in Table 14; we assume an ideal, best-case dynamic allocation, where the network overload is detected instantaneously and there is no delay to change the parameters of the system such as the number of available RAOs. The incoming traffic is classified into two traffic classes: (TC1) alarm and (TC2) periodic reporting; where the alarm reporting takes priority over periodic reporting.

Parameter Value Parameter Value

Preambles per RAO (𝐽) 54 MSG 2 Window 5 ms

Max. RAOs per LTE frame 8 MSG 4 Timer 24 ms

Max. Retransmissions 9 Contention Timer 48 ms

System BW 20 MHz Backoff 20 ms

eNodeB Processing Time 3 ms UE Processing Time 3 ms

Table 14: Legacy LTE system parameters.

The alarm reporting case is modelled by a Beta distribution with parameters 𝛼 = 3 and 𝛽 = 4 [87], which trigger 𝑁1 smart meters within the cell to access the same access frame with latency requirement 𝜏1. The periodic reporting is modelled as a Poisson process with total arrival rate 𝜆 =

12 We do not include a numerical comparison with EAB, as the algorithm that controls the blocking of M2M traffic is not standardized.

Estimation RAO TC 1

Serving Phase RAOs for TC1

Estimation RAO TC 2

Serving Phase RAOs for TC2

Estimation Phase Serving Phase



𝑁2/𝑅𝐼, where 𝑁2 denotes the number of M2M devices and 𝑅𝐼 = 𝜏2 = 60 s, chosen so to match the arrival rate and latency requirement 𝜏2 of a typical M2M application such as smart metering [87]. The performance comparisons are done using different access frame 𝐿 lengths, obtained from half of the maximum allowed delay for alarm reporting 𝜏1/2 = {0.5,2.5,5} seconds.13

Figure 32: Achievable transient 𝑅(1) within the access frame by the legacy and access frame solutions,

with 𝑁2 = 10𝑘 and 𝑅𝑟𝑒𝑞(1)

= 𝑅𝑟𝑒𝑞(2)

= 0.99.

The performance evaluation is performed with the focus on the reliability achieved within the duration of the access frame. Specifically, we illustrate the performance during the peak of traffic

due to the alarm reporting. The achievable reliability of TC1, 𝑅(1), for different number of active TC1 devices is shown in Figure 32. We first observe that the LTE legacy with dynamic allocation, is not able to provide reliable access for 𝑁1 > 1𝑘 (in the legacy solution TC1 and TC2 are treated in the same way). On the other hand, the proposed mechanism is able to provide a reliable service for a considerably higher range of simultaneously accessing devices. Specifically, the proposed scheme

provides service with a reliability guarantee of 𝑅𝑟𝑒𝑞(1)

= 0.99 for up to 𝑁1 = 30𝑘 smart meters if the

tolerable delay is 𝜏1 = 10 s. For TC2, the offered reliability will be constrained by the amount of TC1 arrivals in the same access frame. However, due to TC1 bursty nature and the less restrictive TC2 latency requirement (i.e. 𝜏1 < 𝜏2), we have observed that, after the “storm” caused by the alarms is

over, our solution is able to meet the set 𝑅𝑟𝑒𝑞(2)

.

We emphasize, that beyond this specific example, our proposed solution is tailored to offer the traffic reliability requirements, as long as the allowed latency constraints are in accordance with the number of devices to be served. Furthermore, it enables to achieve a trade-off between latency and reliability.

3.2.3 Conclusions One of the key challenges associated with machine-to-machine (M2M) communications in cellular networks is to be able to offer service with reliability guarantees, particularly when a massive amount of simultaneous M2M arrivals occurs. While current solutions take a reactive stance when dealing with massive arrivals, by either imposing barring probabilities or increasing the contention space, they do so without knowledge of the volume of incoming traffic.

13 Thus, taking into account the 2 RAOs per frame reserved for other purposes (e.g., H2x), the maximum amount of RAOs in each frame is then 𝐿 = {400,2000,4000}.



Here we propose a proactive approach, based on dedicated access resources for the M2M traffic, combined with a novel frame based serving scheme composed by an estimation phase and a serving phase. In the estimation phase the volume of arrivals is estimated and then used to dimension the amount of resources in the serving phase, such that reliable service guarantees are provided. The provided framework can be extended for more than two traffic classes, which is one of the future work directions. Other directions include combination of the proposed approach with the existing access control mechanisms, such as the EAB.



4 Analysis and Enhancement of Neighbourhood Area Networks

While the primary communication technology in SUNSEED is LTE, it is interesting also to keep an eye on upcoming technologies that can serve as alternatives to LTE or as complimentary technologies to improve coverage and/or improve reliability. In the following, we therefore present a preliminary study of IEEE 802.11ah, which can possibly be used for exactly that.

4.1 Performance study of IEEE 802.11ah Wi-Fi network for smart grid applications

The IEEE 802.11 standard family is globally recognised as the most successful Wi-Fi technology due to its easy deployment and low cost. It has been considered as a workable communication solution for the HANs and NANs in smart grid. However, for M2M type of communications, the main limitations of current 802.11 Wi-Fi systems are the coverage, power consumption and the limited capability in terms of number of stations. This is mainly because the traditional contention-based medium access control processes do not work well on event-driven data traffic with massive devices. The IEEE 802.11ah specification for sub-1GHz license-free bands with an aim of supporting massive M2M type, low power, wide coverage Wi-Fi attracts attention of SUNSEED. Some examples of the sub 1GHz license-free bands include 863-868 MHz in Europe, 950-958 MHz in Japan, and 902-928 MHz in US. Owing to the superior propagation feature of lower frequency, the 802.11ah provides longer transmission range, with 6dB higher link margin compared to the wireless transmissions at 2.4GHz. Different from the IEEE 802.11af, which is another sub-1GHz Wi-Fi technology to operate more like a traditional Wi-Fi network solution, IEEE 802.11ah is better suited for M2M and IoT data models, due to its specific design of PHY and MAC mechanism. Typical sub-1GHz Wi-Fi characteristics are summarised in Table 15.

Category Comment

Location Indoor, outdoor

Coverage range <1km (outdoor, sub-urban)

Carrier frequency fc<1GHz, ISM-band (license-free)

Environment type Urban, sub-urban, rural

STA/AP communication 2-way (meter data & control)

Data rate 100kbps (expected PHY data rate)

Mobility Stationary

Traffic type Continuous/periodic/burst

STA/AP capacity STA: <2000 (high density), AP: 1

STA/AP elevation STA: 2m, AP: 2m, 15m

Actors SMs/WAMS nodes/Automation devices, AP

Table 15: Sub-1GHz use case in smart grid WAMS. The PHY layer of 802.11ah is all MIMO OFDM-based transmission with 32 or 64 subcarriers. Channel bandwidths of 1 MHz and 2 MHz are expected to be adopted in Europe. The MAC layer is designed to maximise the number of stations supported while endeavouring to maintain minimum energy consumption. To support the large number of stations, the IEEE 802.11ah Task Group introduces a group-synchronised distributed coordination function for dense wireless networks [91]. The mechanism involves using the restricted access window (RAW) and RAW slots, with stations being categorised into three types: Traffic indication map (TIM), Non-TIM and unscheduled stations. TIM stations listen to AP beacon to transmit or receive within allocated RAW slots. They will remain idle



for the rest of the time. Non-TIM stations periodically transmit in periodic restricted access windows (PRAW) on a predefined or renegotiated base. Unscheduled stations do not have to listen to beacons, either. Usually as sporadic “visitors”, they simply send a poll frame to the AP asking for immediate access to the channel, and wait for a response frame indicating an interval outside both restricted access windows. The grouping mechanism provides the ability for a single AP to manage massive STAs with low collision rate. The STAs also benefits from reduced power consumption for carrier sensing. The idea of grouping network nodes has been widely adopted in various researches, for example, the clustering strategy in wireless sensor networks and ad hoc networks. It increases energy efficiency, decreases management complexity and optimises other network performance metrics. The purpose of grouping is to divide the STAs into groups and let different groups access the cannel in a predefined order. The channel will only be shared by a group of STAs rather than the whole of them. Based on how the groups are organised, the grouping schemes can be categorised into centralised and decentralised ones. Centralised scheme provides more accurate and fast grouping, relying on pre-established network infrastructure and extra control signalling to manage groups. A decentralised scheme can be more cost-effective in terms of lower overheads and more suitable for a dynamic network scenario. In the centralised scheme, STAs are assigned to groups uniformly by AP, and in the decentralised scheme, STAs select the group to join individually. The channel utilisation for small data transmissions is often low, due to the high MAC overhead and payload ratio. In addition to grouping mechanism, the IEEE 802.11ah proposes the use of an 18-byte MAC header, instead of 28 bytes in current 802.11 systems in order to reduce the impact of these overheads. The standard also incorporates bi-directional transmit opportunity (TXOP), restricted access window, and target wake times, which makes it suitable for IoT type of communications. Considering its unique features, it is a good opportunity to study its potentials in the SUNSEED use cases, as the alternative to cellular and PLC based networks to support communications in the WAMS. The 802.11ah standards are still in the development phase. The research in SUNSEED can make suggestion and propose enhancements on MAC/PHY design, which may effectively lead to valuable contribution to the standardisation of the technology. As a typical SUNSEED one-hop NAN scenario, an 802.11ah AP is placed outdoor at the DSO facility, the stations, i.e., smart meters and WAMS nodes, are deployed at various indoor and outdoor locations. The coverage of the AP will be up to 1 km whereas 100 kbps data rate is assumed. The transmit power will be limited according to 100 mW maximum. The modelling of SM/WAMS traffic as presented in relevant sections of the deliverable, as well as in deliverable D3.1, will be adopted in this study. Grouping mechanisms and its impact on network reliability will be discussed. In this report we present prelimenary numerical simulation results in order to show the effectiveness of using grouping method in terms of reducing collision rate and transmission failure rate, under the setting of the 802.11ah CSMA/CA MAC. We analyse the performance use a simplifed network scenario, where the network consists of one AP and a number of STAs (SMs and WAMS nodes) installed in non-line of sight locations (in buildings). The distance between the STAs and the AP range from 10 to 300 metres. Figure 33 illustrates the topologies of two of the networks considered in the simulations. The left network consists of 1 AP and 30 STAs while the right network has 1 AP and 1080 STAs. The data packet/frame size and the transmission power and data rate, as well as all the Interframe spaces, are according to the current 802.11ah standardisation. The STAs have one transmission request per four hours, i.e., the SMs and the WAMS nodes send out measurement data every four hours and the reporting period is 15 minutes. In order to show the worst case, all STAs are assumed to have the requests at the same time. The simulation is focused on calculating the number of collisions during the CSMA contention period, as well as the number of transmission failure. Noted that a collision counts for one failure and the data packets that is unsuccessfully transmitted after the reporting period will be dropped, which means that the



meesage will not be stored for the next reporting time. For simplicity, we assigned an arbitary delivery rate of >95% for the physical layer radio link. The minimum and maximum contention windows are set to 32 and 1024, respectively.

Figure 33: Network topologies. (Left: 1 AP, 30 STAs; Right: 1 AP, 1080 STAs)

For each network scenario, we conduct a number of simulations. Table 16 lists the averaged network performance as the results of 10 independent simulations, without considering any grouping method. It is seen that as the number of STAs increases, the collision rate increases from around 0.9% to over 30%. The number of transmission failure also increases to an unacceptable rate, simply means that the network is not able to support that many STAs.

Table 16: Simulation results: without grouping Table 17 lists the results of the network of 1080 STAs with different sizes of groups. According to the current proposal of 802.11ah standards, the maximum number of STAs in one group is 256. It is seen that when grouping method is used, both the numbers of collisions and the transmission failure are significantly reduced compare to the previous scenario. Having the same transmission conditions, when all of the 1080 STAs are managed as 36 groups (30 STAs per group), the collision rate is only 0.76% comparing with the 31.48% of the scenario without grouping. The effectiveness of applying grouping into the contention based CSMA/CA process is obvious. Figure 34 shows the change of collision rate and failure rate as the number of STAs per group increases. Based on the simulation results we are able to suggest that 30 STAs will be an optimal size of the group. More detailed scenario needed to be formulated and studied before any contrete conclusion can be made. However we can expect that, having such enhancement to the current Wi-Fi systems, the IEEE 802.11ah technology has the potential to be considered as a workable solution for the WAMS in smart grids.

NUMBER OF STAS

AVE FAILURE

AVE COLLISION

AVE DELIVERY

COLLISION RATE

FAILURE RATE

30 7.7 1.9 172.3 0.90% 3.65% 120 44.3 23.8 675.7 2.76% 5.14% 270 144.5 138.8 1475.6 7.14% 7.43% 480 441.3 449.2 2438.7 13.00% 12.77% 750 962.2 1161.5 3448.5 21.51% 17.82%

1080 1595.8 2447.9 4346.5 31.48% 20.52%



NUM OF STAS PER

GROUP

AVE FAILURE

AVE COLLISION

AVE DELIVERY

COLLISION RATE

FAILURE RATE

10 441 24 6039 0.32% 5.92% 20 326 37 6154 0.49% 4.29% 30 300.8 58.1 6179.2 0.76% 3.95% 40 306.6 77.6 6173.4 1.01% 4.00% 60 305.5 121.7 6173 1.58% 3.96%

120 333 234.9 6144.2 3.03% 4.29% Table 17: Simulation results: 1080 STAs with different grouping methods

Figure 34: Collision and failure rates vs number of STAs per group

4.1.1 Conclusions This chapter investigated the characteristics of the IEEE 802.11ah network and its potential application in the WAMS. Initial simulation results show the effectiveness of the stations grouping method for supporting numerous transmission requests in a CSMA/CA based network. More detailed results and discussions will be presented in later SUNSEED deliverable reports.



5 Ultra-Reliable Smart Grid Communication

Communication between two end nodes in a smart grid system can fail due to different reasons. When considering real-time communication in cellular networks, the failure reason is typically either 1) overloaded of the cellular network for example due to many human users using multimedia-heavy applications at the same time, or 2) infrastructure failures such as a failing base station or intermediate router failure. In this chapter we first explore an approach to ensuring reliable reporting by periodically allocating a dedicated resource pool to smart grid reporting, thereby achieving reliability. Second, we consider different methods for improving reliability by using multiple interfaces and paths for packet transmissions.

5.1 Reliable Reporting in LTE with Periodic Resource Pooling

In the past two decades, the main focus of the cellular access engineering was on the efficient support of human-oriented services, like voice calls, messaging, web browsing and video streaming services. A common feature of these services is seen in the relatively low number of simultaneous connections that require high data rates. On the other hand, the recent rise of M2M communications introduced a paradigm shift and brought into research focus fundamentally new challenges. Particularly, M2M communications involve a massive number of low-rate connections, which is a rather new operating mode, not originally considered in the cellular radio access. Smart metering is a showcase M2M application consisting of a massive number of devices, up to 30000 [93], where meters periodically report energy consumption to a remote server for control and billing purposes. On the other hand, the report size is small, of the order of 100 bytes [94]. The current cellular access mechanisms, considering the entire associated overhead, cannot support this kind of operation [95]. There are on-going efforts in 3GPP that deal with the cellular access limitations, investigating methods for decreasing the access overhead for small data transmissions [96], access overload control [97] and guaranteed quality of service [98]. Besides LTE, the work in [99] proposes an allocation method for reports with deadlines in GPRS/EDGE, showing that up to 104 devices can be effectively supported in a single cell by avoiding random access and using a periodic structure to serve the devices such that the deadlines are met. In this contribution, we consider a system with a periodically occurring pool of resources that are reserved for M2M communications and shared for uplink transmission by all M2M devices. The re-occurring period is selected such that if a report is transmitted successfully within the upcoming resource pool, then the reporting deadline is met. We note that, if each device has a deterministic number of packets to transmit in each resource pool and if there are no packet errors, then the problem is trivial, because a fixed number of resources can be pre-allocated periodically to each device. However, if the number of packets, accumulated between two reporting instances, is random and the probability of packet error is not zero, then the number of transmission resources required per device in each transmission period is random. On the other hand, as the number of transmission resources in each instance of the resource pool is fixed, the following question arises: How many periodically reporting devices can be supported with a desired reliability of report delivery (i.e., 99.99%), for a given number of resources reserved for M2M communications? We analyse the proposed approach in LTE context; however, the presented ideas are generic and implementable in other systems where many devices report to a single base station or AP. The results show that, for fixed reliability of access, the proposed method requires less LTE resources compared to the comely used random access.



5.1.1 System Model We focus on the case of periodic reporting, where the length of the reporting interval (RI), denoted by 𝑇𝑅𝐼, depends on the application requirements [94]. The M2M resources for uplink transmission are reserved to occur periodically, at the end of each RI. The periodic reporting is typically modelled either as a Poisson process with arrival rate 𝜆 = 1/𝑇𝑅𝐼, where devices can actually send none, one, or multiple reports within RI, or as a uniform distribution, where devices send a single packet per RI [100], [101]. Our analysis will focus on the former case, but we note that the derived results can be readily applied to the latter arrival model, as well. We assume that all report arrivals that occur within the current reporting interval are served in the next reporting interval.

Figure 35: Representation of the LTE uplink resource structure, where a set of RBs has been reserved for M2M purposes.

As explained in details in section 1.4, the LTE access combines TDMA and FDMA, such that access resources are represented in 2D, see Figure 35. As depicted, time is organized in frames, where each frame is composed of subframes with duration 𝑇𝑠 = 1 ms. The minimum amount of uplink resources that can be allocated to a device is one PRB, corresponding to a single subframe and 12-subcarriers in frequency. We assume that the uplink resources are split into two pools, one reserved for M2M services (depicted in blue in Figure 35), and the other used for other services. Note that the approach of splitting the resources for M2M and non-M2M has often been used [102], as their requirements are fundamentally different. Finally, we assume that there is a set of 𝑌 RBs reserved for M2M resource pool in each subframe. The M2M resource pool is divided into two parts, denoted as the preallocated and common pool, which reoccur with period 𝑇𝑅𝐼, as depicted in Figure 36 a). We assume that there are 𝑁 reporting devices, and each device is preallocated an amount of RBs from the preallocated pool dimensioned to accommodate a single report and an indication if there are more reports, termed excess reports, from the same device to be transmitted within the same interval. The common pool is used to allocate resources for the excess reports, as well as all the retransmissions of the reports/packets that were erroneously received. These resources can only be reactively allocated and in our case we consider the LTE data transmission scheme, where each transmission has an associated feedback that can be used to allocate the resources from the common pool14. The length of the M2M resource pool, preallocated pool and common pool, expressed in number of subframes, are denoted by 𝑋, 𝑋𝑃 and 𝑋𝐶, respectively, see Figure 36 b), such that

14 The minimum latency for the feedback is 6 ms (6 subframes), which includes processing times at the base station and at the device, and which can be assumed negligible taking into account that the RI that we are considering is of the order of thousands subframes.



𝑋 = 𝑋𝑃 + 𝑋𝐶 = 𝛼𝑁 + 𝑋𝐶 ,

where 𝛼 ≤ 1 denotes the fraction of RBs per subframe required to accommodate a report transmission and where the value of 𝑋𝐶 should be chosen such that a report is served with a required reliability. The analysis how to determine 𝑋𝐶, given the constraints of the required number of RBs per report, number of devices and reliability, is not presented here in details, but can be found in [92]. Finally, we note that the duration of 𝑋 has a direct impact on the delay; in the worst case a (successful) report is delivered 𝑇𝑅𝐼 + (𝑋 ⋅ 𝑇𝑠) seconds after its arrival, which also defines the delivery deadline.

Figure 36: a) Periodically occurring M2M resource pool. b) Division of M2M resource pool in the pre-allocated and common pool.

5.1.2 Results We first validate the assumptions used in the analysis by comparing the probability density function (PDF) and cumulative density function (CDF) of the number of required transmissions R obtained using the Gaussian model and the simulation. Figure 37 presents a tight match between the model and simulations, when number of reporting devices is 𝑁 = 100, the maximum number of transmissions per report is 𝐿 = 10, the probability of report error 𝑝𝑒 takes values 0.1 and 0.4,

respectively, and the number of the simulation runs is set to 105 for each parameter combination.



Figure 37: Comparison of simulated and analytical PDF and CDF of 𝑹 when 𝑵 = 𝟏𝟎𝟎 and 𝑳 = 𝟏𝟎.

Further, using the bound derived in [92], we determine the fraction of LTE system resources required for reliable M2M services, defined as the ratio of RBs required for reliable M2M services and the total amount of RBs available. The amount of required RBs depends on the modulation15 and the report size (RS). We assume a typical 5 MHz LTE system [103], typical individual LTE transmission error of 𝑝𝑒 = 0.1 [104], and the maximum number of report transmissions is again set to 𝐿 = 10. The maximal number of devices is set to 30000, as indicated by 3GPP in [93]. Finally, the probability of report failure is set to 𝑃[Φ] ≤ 10−3, i.e., the desired reliability to at least 99.99%. To validate the

analytical upper bound, we performed simulations with a random scheduler with 105 repeats for each parameter combination. Figure 38 shows the performance of the proposed scheme, when report size RS is 100 bytes and reporting interval RI is 1 minute, corresponding to the most demanding RI according to [94]. It can be observed that for the lowest-order modulation (QPSK), up to 30000 devices can be served with only 9% of the available system resources. If 64-QAM is used, then only 3% of the available resources are required to achieve the target reliability.

Figure 38: Fraction of system capacity used for M2M services, when 𝑷[𝜱] ≤ 𝟏𝟎−𝟑, RI of 1 minute, RS

of 100 bytes, bandwidth of 5 MHz and 𝒑𝒆 = 𝟏𝟎−𝟏.

Figure 38 also compares the performance of the proposed scheme with the performance of the legacy LTE, obtained using a LTE simulator with 2 random access opportunities per frame, which is a

15 In this work we consider the lowest-order (QPSK) and the highest-order (64-QAM) LTE modulation schemes.



typical configuration [103]. The devices perform random access for every report, and the reports are also sent in the RBs reserved for M2M traffic. Obviously, the legacy LTE requires about two times more system resources than the proposed scheme; this is due to the uncertainty of the individual report arrivals and retransmissions, demanding a high amount of reserved RBs. In the proposed scheme, the individual reports are grouped (over a RI), and this aggregation exhibits far less uncertainty, requiring less reserved RBs for a reliable service. Furthermore, it can be shown that if the resource reservation is not used and there are only M2M devices present in the cell, the fraction of the system resources required for the target reliability in the legacy LTE is similar as in the proposed scheme. However, in presence of additional traffic in the cell, the reliability of M2M services without reservation cannot be guaranteed anymore.

Figure 39: Fraction of system capacity used for M2M services, when 𝑷[𝜱] ≤ 𝟏𝟎−𝟑, RI of 1 minute,

bandwidth of 5 MHz, 64-QAM and 𝒑𝒆 = 𝟏𝟎−𝟏.

Figure 39 depicts the required fraction of system capacity for M2M service, when the RS varies between 100 bytes and 1 kbyte [94], the system bandwidth is set to 5 MHz, modulation scheme is 64-QAM, and 𝑝𝑒 = 0.1. Obviously, the report size has a large impact in the results, demanding up to 30% of the system capacity in the worst case. Finally, we note that Figure 38 and Figure 39 also demonstrate a tight match between the analytical and simulated results.

5.1.3 Conclusions We have introduced a contention-free allocation method for M2M that relies on a pool of resources reoccurring periodically in time. Within each occurrence, feedback is used to reactively allocate resources to each individual device. The number of transmissions required by an M2M device within the pool is random due to two reasons: (1) random number of arrived reports since the last reporting opportunity and (2) requests for retransmission due to random channel errors. The objective is to dimension the pool of M2M-dedicated resources in order to guarantee certain reliability in the delivery of a report within the deadline. The fact that the pool of resources is used by a massive number of devices allows basing the dimensioning on the central limit theorem. Promising results have been shown in the context of LTE, where even with the lowest-order modulation only 9% of the system resources are required to serve 30K M2M devices with a reliability of 99.99% for a report size of 100 bytes. The proposed method can be applied to other systems, such as 802.11ah.

5.2 Multi-Interface Transmissions for Ultra-High Reliability

Multipath transmissions have been successfully used in streaming media applications to ensure reliable and thus uninterrupted delivery of audio and video content. In the emerging field of M2M



communications, where a prevailing example is smart grid communications, there are examples of streaming data applications that require high reliability levels for real-time delivery of measurement data. However, different information flows may have different requirements to latency and reliability, and therefore each flow must be treated individually. One approach to improve reliability of transmissions is to use multipath transmissions for selected communication flows from a source node to a destination node, where each packet is duplicated on multiple interfaces that use partially or completely independent paths. Thereby, failures in one path will not lead to complete failure of the communication between two end points. In this work we present an analysis framework that allows determining, which transmission mode is required to satisfy the latency and reliability requirements of different traffic flows. In traditional reliability engineering, the term reliability refers to the probability that a system is still operational after being in operation for a certain time. In this work, we will use another definition where reliability is the probability that an information packet is being successfully delivered with a certain deadline. There are many researches that consider variants of multi-path transmissions as surveyed in [106]. While most of these approaches are useful for bulk content distribution or streaming audio/video, the challenge of communicating low-latency real-time information in a very reliable manner is more difficult to tackle. Approaches that use re-routing in case of failure are most likely not useful, since it may be impossible to detect failure and re-route within the latency requirement. Therefore it is necessary to employ redundant transmissions, so that in case the primary transmission fails, it is possible for a secondary transmission on an independent path to deliver the information within the deadline. The most straightforward way to implement this redundancy is to simply duplicate the transmitted message on multiple interfaces and paths thereby using the path diversity technique [107]. While this clearly improves reliability, the bandwidth usage is also proportionally larger. However, if instead of simply duplicating each message over multiple paths, the message is split into multiple pieces and coded with redundancy information so that it is possible to recover from some degree of loss, the total used bandwidth can be kept low while improving the reliability. One specific path diversity technique that works in this way is the Multiple Description Coding (MDC) [108]. This technique has been widely considered for reliable video distribution in content distribution networks since its structure enables graceful degradation and rate adaptation that are favourable features for video streaming. While media streaming applications typically use performance metrics such as signal-to-noise ratio and picture signal-to-noise ratio to quantify the impact on content quality, sensor measurements require different performance metrics that will be use case specific. Therefore, in this work we consider the reliability as a function of the latency deadline. This is exemplified in Figure 40, where the blue curve characterizes the performance of a communication connection between two nodes in terms of the probability that a packet is delivered within a certain latency deadline. Such a curve can be produced from network monitoring measurements, e.g., by continually pinging the remote host and recording success rate and latency (estimated from round-trip time). The shape of the curve depends on two factors: 1) the variability of latency due to factors such as medium access, queuing and processing delays, and 2) the loss rate due to various failures between the two hosts. The former factor determines the shape of the increasing slope, whereas the latter determines the convergence level, in Figure 40 approximately 0.98. Furthermore, in Figure 40 two examples of service requirements are sketched; first, a strict real-time service that does not accept higher latency than 25 ms, and second, a less strict service that is able to function with higher latencies up to 50 ms. As indicated by the dash-dotted and dashed lines, the achievable reliabilities for the two services are 0.67 and 0.98, respectively.



Figure 40: Example of latency-reliability CDF of a network interface (blue curve). The red dash-dotted line and the black dashed line indicate the obtainable reliability for latency requirements 25 ms and

50 ms, respectively.

In this fictitious example it is not possible to achieve higher reliability than 0.98. If another independent communication connection is available, so that for example both a wired and a wireless connection are used, we know from reliability engineering [105] that the reliability 𝑅 of the two independent connections in parallel would be:

𝑅 = 1 − (1 − 𝑅1)(1 − 𝑅2)

= 1 − (1 − 0.98)(1 − 0.95) = 0.999, (1) given that the secondary connection has a reliability 0.95. This way of calculating reliability is representative for communication connections that are justifiably independent, e.g., a wired fiber connection and a cellular LTE link.16 However, for communication options such as LTE and GPRS (cite to relevance in M2M) it can be more difficult to justify their independence, since the base station equipment may often be located in the same physical base station towers, and therefore being dependent on for example the same power supply and backhaul links. In order to evaluate the reliability of such dependent configurations, we consider the different states of the system, as sketched in Figure 41. Transitions between states are specified by the failure rates denoted by 𝜆 and restoration rates denoted by 𝜇.

16 While the telco core network may be shared for such two options, the core network has several redundancy mechanisms to ensure very high levels of reliability and we will therefore not focus on its impact in the following.



Figure 41: State-transition diagram of the continuous time Markov chain that represents the case of dependent LTE (4G) and GPRS (2G) connection options. The color of a state indicates the level of

degraded service.

The system is considered to be in failure when it is in states 4 and 5. In state 4 the failure is due to simultaneous occurrences of independent failures of LTE and GPRS, while in state 5 the failure is due to common factors. The system is considered operational when it is in states 1, 2, or 3. The system reliability is evaluated as the probability that the system does not enter a failure state within some specified mission time 𝑇. This can be calculated from the state probabilities 𝜋(𝑡)𝑡 of the continuous time Markov chain in Figure 41 at time 𝑡 = 𝑇 as:

𝜋(𝑡) = 𝜋(0) 𝑒𝑄𝑡 (2) where 𝜋(0) is the initial state probability vector, 𝑄 is the state-transition matrix reflecting Figure 41 and 𝑒 is the matrix exponential function. The availability of the system is given from the the steady-state probability distribution 𝜋(𝑡) when 𝑡 → ∞.

5.2.1 System model We consider an M2M device such as a smart meter or a WAMS node that needs to communicate reliably with a specific end-host, e.g., a DSO backend monitoring or control device. The M2M device has N communication interfaces available to reach the end-host, as pictured in Figure 42. For each interface, a latency CDF 𝐹(𝑥) is available that describes the probability of successful message delivery for a given latency requirement 𝑥.



Figure 42: Considered network architecture for independent and partially dependent technologies.

The available communication technologies are fiber, 4G (LTE), and 2G (GPRS), with the specifications shown in Table 18.

Table 18: Reliability and latency specifications of considered communication technologies.

Technology Reliability Latency (𝝁, 𝝈)

Fiber 𝑅fi = 0.998 2 ms, 0.5 ms 4G (LTE) 𝑅4G = 0.98 20 ms, 2 ms

2G (GPRS) 𝑅2G = 0.98 250 ms, 40 ms

Figure 43: Latency-reliability curves of considered technologies.

The latency-reliability curves for the considered technologies are shown in Figure 43. Finally, we assume that the base station reliability is 𝑅BS = 0.9995. That is, 4G and 2G can fail independently with probability 0.02, while they can fail at the same time due to base station failure with probability 0.0005.

5.2.2 Reliability of Multi-Interface Transmissions This section presents the considered multi-interface transmission methods used for evaluating the reliability of multi-interface transmissions. Initially, we consider interfaces that can be considered



independent and thereafter we consider the special cases of dependent two-interface transmissions and three-interface transmissions.

5.2.2.1 Independent k-Interface Transmissions

For transmissions using k interfaces that can justifiably be considered independent, the resulting latency-reliability function is calculated as a parallel system, i.e.:

𝐹L𝑘−par(𝑥) = 1 − ∏ (1 − 𝐹𝑖(𝑥))𝑘

𝑖=1 , (3)

where 𝐹𝑖(𝑥) correspond to the latency-reliability function of the i-th technology, e.g., fiber, 4G, or 2G.

5.2.2.2 Dependent Two-Interface Transmissions

In case the technologies cannot justifiably be considered independent, e.g., in the case of 4G and 2G if they share the same base station tower, then the state probabilities computed as in eq. (2) are used as:

F𝐿2−dep

(𝑥) = ∑5𝑖=1 𝜋𝑖 ⋅ 𝑃𝑖(𝑥) (4)

where

𝑃(𝑥) =

[ 1 − (1 − 𝐹4G(𝑥))(1 − 𝐹2G(𝑥))

𝐹4G(𝑥)

𝐹2G(𝑥)00 ]

(5)

5.2.2.3 Dependent Three-Interface Transmissions

For the case with three technologies in play where the 2 cellular technologies are not justifiably independent, the Markov Chain in Figure 44 describes the different modes of operation. Specifically, the dependence is expressed through the BS failure events where both 4G and 2G is considered to be down at the same time.



Figure 44: State-transition diagram of triple-redundant system with dependencies. The color of a state indicates the level of degraded service.

First step is to compute steady state probabilities 𝜋𝑖 for all 𝑖 states in the continuous time Markov chain in Figure 44. For each latency value 𝑥, the message delivery reliability of the different system states is computed as:



𝑃(𝑥) =

[ 1 − (1 − 𝐹2G(𝑥))(1 − 𝐹4G(𝑥))(1 − 𝐹fi(𝑥))1 − (1 − 𝐹2G(𝑥))(1 − 𝐹fi(𝑥))1 − (1 − 𝐹4G(𝑥))(1 − 𝐹fi(𝑥))1 − (1 − 𝐹2G(𝑥))(1 − 𝐹4G(𝑥))𝐹fi(𝑥)𝐹fi(𝑥)

𝐹2G(𝑥)𝐹fi(𝑥)𝐹4G(𝑥)𝐹fi(𝑥)00𝐹fi(𝑥)00 ]

(7)

Based on these probabilities, the resulting latency-reliability function for the triple-parallel transmission mode is computed for each latency value 𝑥 as:

𝐹𝐿3−dep

(𝑥) = ∑15𝑖=1 𝜋𝑖 ⋅ 𝑃𝑖(𝑥) (8)

5.2.3 Numerical results For evaluating the resulting performance of the considered transmission modes, actual data on Mean Time to Restoration (MTTR) and availability levels of different technologies has been used. Table 19 presents the failure and restoration rates used in the numerical evaluation.

Table 19: Failure (f) and restoration (r) rates, 𝜆 and 𝜇, respectively, used for numerical evaluations.

Entity 𝜆 (f/week) 𝜇 (r/week) 4G 1.0013 50.4 (200 min/r)

2G 1.0013 50.4 (200 min/r)

Fiber (fi) 0.0561 28 (6 hrs/r)

Base station (BS) 0.0267 50.4 (200 min/r)

Since the failure rates were unknown, these were obtained by setting up equations representing our knowledge about the system and solving the following linear equations. First, we determine the state probabilities 𝑝𝑖𝑖 of the states in Figure 41.

[ 0 1 0 1 10 0 1 1 10 1 −1 0 00 0 0 1 00 0 0 0 11 1 1 1 1]

[ 𝜋1

𝜋2

𝜋3

𝜋4

𝜋5]

=

[ 1 − 𝑅4G

1 − 𝑅2G

0(1 − 𝑅4G)(1 − 𝑅2G)1 − 𝑅BS

1 ]

. (9)

Having obtained the state probabilities 𝝅 = [𝜋1 …𝜋5], we set up the following balance equations that explain the relations between the failure and restoration rates according to Figure 41 and solve the corresponding linear system:



[ −𝜋1 −𝜋1 −𝜋1 𝜋2 𝜋3 𝜋5

𝜋1 −𝜋2 0 −𝜋2 𝜋4 0−𝜋3 𝜋1 0 𝜋4 −𝜋3 0𝜋3 𝜋2 0 −𝜋4 −𝜋4 00 0 𝜋1 0 0 −𝜋5

−1 1 0 0 0 00 0 0 −1 1 00 0 0 1 0 00 0 0 0 1 00 0 0 0 0 1 ]

[ 𝜆4G

𝜆2G

𝜆BS

𝜇4G

𝜇2G

𝜇BS]

=

[ 0000000𝜇4G

𝜇2G

𝜇BS]

.

(10) Thereby we obtain a set of failure rates 𝜆4G, 𝜆2G, and 𝜆BS that satisfy the constraints of the system in terms of state probabilities, restoration rates, and balance relations between states. Finally, the failure rate for fiber has been calculated as:

𝜆fi =(1−𝑅fi)𝜇fi

𝑅fi (11)

where the value 𝜇fi = 28, which corresponds to 6 hrs/repair, has been assumed. With failure and restoration rates being fully specified, the resulting latency-reliability performance is calculated using the method outlined in section 5.2.2. The results are presented in Figure 45.

Figure 45: Latency-reliability curves for the considered multi-interface transmission methods.

The combination of 2G and 4G can increase reliability compared to using just a single cellular technology. If we assume that the transmission paths are independent, 3-nines can be achieved as shown by the orange dotted line, however, a more realistic result is that only 2-nines are achievable (orange solid line) in cases where the two technologies are served by the same base station tower. Four nines can be achieved at low latencies down to around 30 ms with fiber + 4G, while fiber + 2G delivers similar performance for higher latencies above 400 ms. When considering the triple redundant transmission mode, where fiber, 4G and 2G is used at the same time, 6-nines can in principle be reached if 4G and 2G can justifiably be considered independent (black dotted line). Otherwise, as shown by the black dash-dotted line, 5-nines can be

Latency (x)

0 0.1 0.2 0.3 0.4 0.5

F(x

)

0.9999999

0.999999

0.99999

0.9999

0.999

0.99

0.9

0

4: 2-par (Fiber + 4G)5: 2-par (Fiber + 2G)6: 2-par (4G + 2G)7: 2-par (dep.) (4G + 2G)8: 3-par (Fiber + 4G + 2G)9: 3-par (dep.) (Fiber + 4G + 2G)



achieved. However, the 5 and 6-nines only apply for higher latencies above 400 ms, due to the high latency of 2G. If high reliability and low latency is required, the fiber + 4G option suffices. While the results suggest that up to 5 and 6-nines reliability can be achieved, at such high reliability levels the core network of the telco also has to be able to deliver similar reliability.

5.2.4 Conclusions Using multiple communication interfaces in a redundant fashion is shown to improve reliability from 1 to 2 nines reliability for individual interfaces up to 5-nines if fiber, 4G and 2G interfaces are used simultaneously, given that the latency requirements are not stricter than the latency of any of the interfaces. In this work we use latency-reliability functions to characterize the expected reliability for different levels of latency for a given interface. With this, we propose a method to evaluating the reliability of both independent (parallel) communication paths and partially dependent communication paths, e.g., in the case of shared base station tower for 4G and 2G technologies. The latter approach is modelled using Markov chains that describe the relations between the different system states. For the considered system model, which is based on reliability figures provided by TS, we find that the (naïve) parallel systems model, which assumes communication paths to be independent, results in an overly optimistic reliability result, which is one order of magnitude higher than when using the more realistic model that accounts for component dependencies. In future work we are going to investigate how coding techniques, e.g. based on MDC [108] can be used to achieve high reliability of WAMS-SPM reporting, while keeping the bandwidth requirements moderate.



6 Conclusions

In SUNSEED, the primary communication technology to be used in the wide area and neighbourhood area network domains is LTE. Therefore, most of the guidelines for enhancing communication networks for real-time smart grid control considered in this deliverable are targeting LTE. Initially, in Chapter 2, we focus on the capacity of LTE when it is being used for M2M communication such as real-time smart grid communication. A key insight of the researches is that the capacity of an LTE cell covers many different aspects. For example: How do the propagation conditions of a specific scenario and the used scheduling algorithm influence the experienced transmission delay? The delay requirements of, e.g. WAMS-SPM nodes thereby puts a limit on the capacity. An analysis of coverage enhancement techniques discussed in 3GPP indicates a potential to increase the link budget by 10-20 dB, leading to a factor 2-4 increase in cell radius. However, it is important to also consider the capacity effects of coverage enhancement techniques since the effect of these techniques is that a) on average more radio resources are needed per device and b) more devices must be supported in the larger cells. An analytical study shows that the highest gains (up to 10 dB) of LTE coverage enhancement techniques are expected for higher frequencies and urban areas, while in rural areas the gain will be limited due to the capacity constraints. The delay of the LTE uplink transmission for the WAMS and SM nodes is mainly determined by the number of nodes in the cell, amount of available PRBs for transmission, and (especially for higher number of nodes) the scheduling approach. The radio propagation environment (e.g. urban, sub-urban, rural) and the number of allocated PRBs per node do not have significant impact on the delay performance. For a given desired 95% delay requirement of e.g. 1s the LTE cell capacity is in the range of 500 (6 PRBs available) to 5000 nodes (50 PRBs available). The TTI based scheduling is preferred as it improves the 95% delay statistics for WAMS nodes without large impact on the delay for the SM nodes. Future delay based capacity investigations will focus on other radio access technologies (e.g. GSM, UMTS, etc.) and also heterogeneous deployments of e.g. RF mesh with gateways towards a cellular network. Other researches reveal that the access reservation protocol used in LTE for sporadic uplink transmissions, e.g. for smart meter and WAMS-SPM node reporting, is often the limiting factor in M2M communication long before the data resources are exhausted. Specifically we show show that even GPRS can support traditional smart meter traffic, as well as more frequent measurements down to 5 min report intervals. Further, it is shown that LTE can support WAMS-SPM nodes, however with potentially large use of bandwidth (up to 10 MHz), unless the report payloads are appropriately dimensioned by measurement down-selection and/or applying compression techniques compared to traditional PMU measurement traffic characteristics. In addition to this specific study, we have proposed a mathematical model that allows evaluating the capacity of an LTE network at click-speed, in terms of access reservation protocol limitations for a given network configuration. In Chapter 3 we considered the co-existence of machine-to-machine (M2M) and human-type communications in cellular networks. This co-existence can be problematic, since intermittent bursts in human-type traffic can lead to overload situations where the strict real-time and reliability requirements of M2M traffic cannot be satisfied. We have addressed this challenge first by studying the network access congestion problem. Two methods, namely transmission probability control and two-hop access Aloha, have been investigated as means to improve co-existence. Performance evaluation shows the proposed solutions can significantly improve the access delay and throughput of M2M applications. Second, we propose a proactive approach, based on dedicated access resources for the M2M traffic, combined with a novel frame based serving scheme composed by an estimation and a serving phase. In the estimation phase the volume of arrivals is estimated and then used to dimension the amount of resources in the serving phase, such that reliable service



guarantees are provided. The provided framework can be extended for more than two traffic classes, which is one of the future work directions. Following in Chapter 4 we bring a preliminary study on the upcoming long-range Wi-Fi protocol IEEE 802.11ah. This protocol is envisioned as a potential choice for complimenting cellular networks in areas with limited cellular coverage. The study presents initial simulation results that show the effectiveness of the station grouping method for supporting numerous transmission requests in a CSMA/CA based network. Finally, in Chapter 5 we study different methods to ensure high reliability in smart grid communication. First we consider how reliability guarantees can be given to uncoordinated transmissions from M2M devices, by introducing a contention-free allocation method for M2M that relies on a pool of resources reoccurring periodically in time. Promising results in the context of LTE show that the proposed resource pool based scheme uses much less resources than legacy random access in LTE. The proposed method can be applied to other systems, such as 802.11ah. Thereafter we consider the general problem of using multiple transmission interfaces and communication paths to enable redundant and highly reliable communication. We propose a method to evaluating the reliability of both independent (parallel) communication paths and partially dependent communication paths, e.g., in the case of shared base station tower for 4G and 2G technologies. The latter approach is modelled using Markov chains that describe the relations between the different system states. For the considered system model, which is based on reliability figures provided by TS, we find that the (naïve) parallel systems model, which assumes communication paths to be independent, results in an overly optimistic reliability result, which is one order of magnitude higher than when using the more realistic model that accounts for component dependencies.



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Appendix A: Relation between capacity and coverage improvement

In this appendix we derive the formula that gives the actual coverage improvement that can be achieved, taking into account the radius, the user density and the maximum theoretical capacity. We firstly define the following: Ro : Initial cell radius (km), see also Figure 46, assuming the cell can be approximated with a circle ρnode : Actual household density (1/km2) No : Number of households per cell at cell radius R0 Δ : Coverage improvement of link budget (excluding the one related to frequency hopping). Linear value, ΔdB in dB.

RΔ : Cell radius corresponding to the improved link budget (km), see also Figure 46 NΔ : Number of households per cell at cell radius RΔ

NRING : Number of households in the ring between original cell radius R0 and extended cell radius RΔ ρmax : Maximum household density (km2). This parameter represents the capacity limit as it has

been derived in the capacity analysis NMAX : Maximum number of supported households per cell following from the capacity limit ρmax.

We assume that the number of supported households per cell is independent of the cell radius.

NEXCESS : The difference between NMAX and No, represents the extra margin of users that can be supported

L0 : Link budget corresponding to R0 (dB)

LC : Path loss parameter from the Okumura-Hata/COST-231 Hata model that depends on frequency and environment type (dB)

n: Path-loss coefficient of the Okumura-Hata model. n=35.7 for urban/suburban environment, n=34.8 for rural environment, independent of frequency.

We assume the eNodeB antenna height to be 25 m for the urban and suburban environments, and 35 m for the rural environment. The UE is commonly assumed to be located at 1.5 m height.

Figure 46: Original cell radius and radius after the coverage enhancement.



For the number of households per cell, assuming the (sector) cell covers one third of a circle, the following equations hold:

No = π(R0)2ρnode/3 (1) N Δ= π (RΔ)2ρnode /3 (2)

N MAX = π (R0)2ρMAX /3 (3) NEXCESS = N MAX - No (4)

N RING = π(𝑅𝛥2 − 𝑅0

2)ρNODE/3 (5)

As a rule of thumb, we found out in section 2.1.3.2 that an increase of 3 dB results in loss of 50% capacity for the users that apply the coverage enhancement. We assume that users inside R0 are in good radio conditions, and therefore the coverage enhancement will apply only to the users who are in the ring between original cell radius R0 and extended cell radius RΔ. Those users will use the excess capacity of supported users and actual users inside R0. This excess capacity will be decreased with the coverage enhancement. Eventually the following empirical equation holds

N𝑅𝐼𝑁𝐺 =N𝐸𝑋𝐶𝐸𝑆𝑆

𝛥 (6)

Replacing (5), (4) and consequently (1) and (3) in (6), we get

𝜋(𝑅𝛥2− 𝑅0

2)𝜌𝑛𝑜𝑑𝑒

3=

𝜋𝑅02𝜌𝑚𝑎𝑥− 𝜋𝑅0

2𝜌𝑛𝑜𝑑𝑒

3𝛥

𝑅02(𝜌𝑚𝑎𝑥 − 𝜌𝑛𝑜𝑑𝑒) = 𝛥𝜌𝑛𝑜𝑑𝑒(𝑅𝛥

2 − 𝑅02) (7)

The link budget L0 represents the path loss at the cell radius R0 Following the Okumura-Hata and COST-231 Hata models it can be written as:

𝐿0 = 𝐿𝑐 + 𝑛 log10 𝑅0 (8) which after coverage enhancement turns into:

𝐿0 + 𝛥𝑑𝐵 = 𝐿𝑐 + 𝑛 log10 𝑅𝛥

𝑅𝛥 = 10𝐿0+𝛥𝑑𝐵−𝐿𝑐

𝑛 = 10𝐿0−𝐿𝑐

𝑛 10𝛥𝑑𝐵

𝑛 (9)

From (8) and (9) we get:

𝑅𝛥 = 𝑅010𝛥𝑑𝐵

𝑛 or

𝑅𝛥 = 𝑅0𝛥10

𝑛 (10)

Replacing (10) in (7) we have:

𝑅02(𝜌𝑚𝑎𝑥 − 𝜌𝑛𝑜𝑑𝑒) = 𝛥𝜌𝑛𝑜𝑑𝑒𝑅0

2 (𝛥20

𝑛 − 1)

(𝜌𝑚𝑎𝑥−𝜌𝑛𝑜𝑑𝑒

𝜌𝑛𝑜𝑑𝑒) = 𝛥

𝑛+20

𝑛 − 𝛥

or

𝛥𝑛+20

𝑛 − 𝛥 − (𝜌𝑚𝑎𝑥−𝜌𝑛𝑜𝑑𝑒

𝜌𝑛𝑜𝑑𝑒) = 0 (11)



Appendix B: Additional LTE simulation results

(a)

(b)

(c)

(d)

Figure 47: CDFs of WAMS delay performance for 50 PRBs: (a) the influence of number of users per cell on the delay CDFs; (b) the influence of random and TTI based scheduling; (c) the influence of the




(a)

(b)

(c)

(d)

Figure 48: CDFs of SM delay performance for 50 PRBs: (a) the influence of number of users per cell on the delay CDFs; (b) the influence of random and TTI based scheduling; (c) the influence of the




(a)

(b)

(c)

(d)

Figure 49: CDFs of WAMS and SM delay performance for 1/6 WAMS to SM ratio: (a) the influence of number of users per cell on the WAMS delay CDFs; (b) the influence of number of users per cell on the

WAMS delay CDFs; (c) the influence of number of users per cell on the SM delay CDFs; (d) the influence of number of users per cell on the SM delay CDFs

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