report 1 joe final w 25eran for etisalat

Modern wireless networks

Deployment Scenarios for Cognitive Radio

in LTE Networks

By:

Ameera Mohamed Ali Badawy

Eslam Saeed Elsayed Aboelliel

Mina Youssre Younan

Mohamed Abd El Wahab Ahmed

Under The Supervision of

Assoc. prof. Dr.Mohsen M.Tantawy

Dr. Reem Hamed Abd ELhadi

National Telecommunication Institute

Network Planning Department

January 2015

National Telecommunication Institute

Professional Training Program

Arab Republic of Egypt

Ministry of Communication

and Information Technology

الوعهذ القىهى لالتصاالخ

تراهج التذرة الوتخصص

هحىر االتصاالخ الالسلكح الوتقذهح

الرادى االدراكى تطثق سارىهاخ

فى شثكاخ التطىر طىل األهذ

قذهه

اهرج هحوذ عل تذوي

اسالم سعذ السذ أتىاللل

ها سري ىاى

هحوذ عثذ الىهاب احوذ

اشراف

د هحسي هحوذ ططاوي.م.أ

رن حاهذ عثذ الهادي. د

الوعهذ القىهى لالتصاالخ

قسن تخطط الشثكاخ

2015 ار

جوهىرح هصر العرتح

وزارج األتصاالخ و تكىلىجا

الوعلىهاخ

I

Abstract

Long Term Evolution (LTE) has many advantages as spectral efficiency, high data rate

and throughput, scalable bandwidth, seamless network IP and simple architecture in

comparison to previous generations. Cognitive Radio (CR) addresses the scarce

spectrum problem. CR devices sense the environment, detect spatially unused spectrum

and opportunistically access available spectrum without creating harmful interference to

the incumbents. This work investigates an experimental framework for next mobile

generation cognitive radio access in the LTE cellular system. It can be used to study CR

concepts in different scenarios. We tried to find and extend the frequency band for

applying LTE by finding holes in DCS then using CR technology. This technique

utilizes the frequency band in a good and efficient way with taking into consideration

the rapid growth of users’ needs and expectations.

In this work, the team scan the spectrum to recommend an extra secondary bandwidth

that be used to extend the bit rate and provide VAS to LTE network.

II

Contents

ABSTRACT I

LIST OF FIGURE V

LIST OF TABLES II

ABBREVIATIONS VIII

Chapter 1

Overview 2

1.1 Overview ............................................................................... 2

1.2 Objective of cognitive radio ................................................. 3

1.3 Project Organization .............................................................. 3

Chapter 2

Long Term Evolution (LTE) System 4

...........................................................................................................

2.1 Introduction .......................................................................... 4

2.2 Core network .......................................................................... 5

2.2.1 Access network ........................................................... 6

2.3 Physical layer ........................................................................ 6

2.4 LTE-Advanced (LTE-A) ........................................................... 7

2.4.1 Carrier Aggregation.......................................................... 7

2.4.2 MIMO, Multiple Input Multiple Output ......................... 9

2.4.3 Relay Nodes ................................................................... 10

2.5 LTE solutions ......................................................................... 11

2.6 Value added services of LTE .................................................. 13

2.6.1 Introduction .................................................................... 13

2.6.2 Car information and entertainment systems .................. 13

2.6.3 Remote security systems: ............................................... 14

2.6.4 LTE last-mile connections ............................................ 15

2.6.5 LTE location-based services .......................................... 16

2.6.6 Mobile cloud services .................................................... 16

III

2.6.7 mHealth and mCommerce ............................................... 16

2.7 LTE Regularity ....................................................................... 16

Chapter 3 Cognitive Radio overview 19

3.1 Introduction to Cognitive Radios ........................................ 19

3.2 Cognitive Radio characteristics .......................................... 20

3.2.1 Cognitive Capability ................................................ 21

3.2.1.1 Spectrum Sensing: ....................................... 22

3.2.1.2 Spectrum Decision ..................................... 22

3.2.1.3 Spectrum Sharing ....................................... 23

3.2.1.4 Spectrum Mobility ..................................... 23

3.2.2 Geo-location database ............................................... 23

3.2.3 Cognitive Radio Network Architecture ...................... 24

3.2.4 Software Defined Radio (SDR) .................................. 26

3.3 Cognitive Radio Equipments .................................................. 27

3.3.1 SDR Hardware Platforms ............................................... 27

3.3.1.1 Universal Software Radio Peripheral2 (USRP2) 27

3.4 Capex and Opex in cognitive radio ......................................... 29

3.4.1 Sensor network CAPEX ................................................. 31

3.4.2 Cognitive access network CAPEX ................................ 31

3.4.3 Operational expenditures (OPEX) ................................. 32

3.4.4 Results ............................................................................ 33

3.4.4.1 Base case results ................................................. 33

3.4.4.2 Sensitivity analysis ............................................. 33

3.4.4.2.1 Fixed sensor density............................ 33

3.4.4.2.2 Fixed sensor OPEX ............................ 34

3.5 Cognitive Radio Value Added Services (VAS).................... 34

3.5.1 Medical Body Area Network (MBANs) .................... 34

3.5.2 Efficient VIdeo Streaming For Cogenitive Radio ....... 36

3.5.3 Intelligent Transportation System (ITS) ..................... 38

3.5.4 Public Safety ................................................................ 38

3.5.5 Agriculture ................................................................... 38

3.5.6 Cellular network .......................................................... 38

3.6 Standards and Regulation ........................................................ 39

3.6.1 Introduction ................................................................... 39

3.6.2 Spectrum trading ............................................................ 40

IV

3.6.3 Standards ....................................................................... 43

Chapter 4 Cognitive Radio Over LTE 46

4.1 Introduction ............................................................................. 44

4.2 LTE Cognitive Femtocell Systems ......................................... 45

4.2.1 Femtocell Networks: ...................................................... 45

4.2.2 Wired broadband Femtocell Networks in LTE ............. 47

4.2.3 Femtocell as a Relay in LTE .......................................... 48

4.2.4 Backhaul Link Using TV White Space Frequency Bands50

4.3 Interference Management Using Cognitive Radio in LTE..... 51

4.3.1 Interference in two-tier cellular networks ....................... 51

4.3.2 Femtocells Networks using Cognitive Radio in LTE..... 53

Chapter 5 Deployment Scenarios for Cognitive Radio in LTE Cellular

Network ………………………………………… 56

5.1 project assumption………………………………………… 56

5.2 Equipments used in project and measurement procedures … 56

5.2.1 Laser DistanceMeter……………………………………56

5.2.1.1 Measurement criteria …………………………..57

5.2.2 EME SPY 140…………………………………….. …..57

5.2.2.1 Measurement criteria…………………………..58

5.2.3 FSH Handheld Spectrum Analyze…………………… 65

5.2.3.1 Measurement criteria……………………… … 65

5.4 Cognitive Radio in operators Bands………………………….71

Chapter 6

Conclusion and Future Work 72

REFERENCES 75

V

LIST OF FIGURE

Figure 2.1: System Architecture Evolution (SAE)…………………………..…………4

Figure 2.2: LTE Resource block…………………………………………………..……7

Figure 2.3: Carrier Aggregation – FDD…………………………………………….......8

Figure 2.4: Carrier Aggregation – Intra- and inter-band alternatives………………….8

Figure 2.5: Carrier Aggregation; Serving Cells Each Component Carrier corresponds to a

serving cell. The different serving cells may have different coverage………….............9

Figure 2.6: Simplified illustration of 2x2 MIMO (Spatial Multiplexing)……….…....9

Figure 2.7: MIMO is recommended for high S/N and TX diversity is preferably used for

low S/N scenarios……………………………………………………………………….10

Figure 2.8: The Relay Node (RN) is connected to the DeNB via the radio interface Un.11

Figure 2.10: Remote Radio Unit (RRU)………………………………………………..11

Figure 2.11 Antenna Integrated Radio (AIR)…………………………………………13

Figure 2.12: RBS 6101………………………………………………………………..13

Figure 2.13: RBS 6101…………………………………………………………………13

Figure 2.14: Example for The VML700 LTE vehicle modem………………………...14

Figure 2.15: remote security …………………………………………………………...15

Figure 2.16: LTE-to-WiFi router……………………………………………………….15

Figure 2.19: UK operators in LTE auction (prices and band)…………………………17

Figure 3.1: Mobile Spectrum in Europe, ref. [2]…………………………………….18

Figure 3.2: Cognitive Radio characteristics (a) Spectrum hole concept, (b) Cognitive

Radio transceiver, ref [3]………………………………………………………………..20

Figure 3.3: General Cognitive Cycle established in [4]…………………………………21

Figure 3.4: Cognitive radio network architecture……………………………………....25

Figure 3.5: General architecture for Software defined Radios…………………………26

Figure 3.6: Cognitive Radio Architecture (CRA)………………………………………

Figure 3.7: SDR in cognitive system……………………………………………………27

Figure 3.8: Simplified Overview of a SDR Setup Built Around an NI USRP……….28

Figure 3.9SENDORA system architecture……………………………………………..28

Figure 3.10: Medical Body Area Network……………………………………………..35

Figure 3.11: Example of video streaming in VANET…………………………………36

Figure 3.12: Rebroadcasted Nodes Selection…………………………………………..37

Figure 3.13: Cognitive radios share spectrum with different radio systems. Depending on

regulatory status, vertical or horizontal spectrum sharing is done…………………….40

Figure 3.14: Spectrum Trading…………………………………………………………41

Figure 3.15: Underlay and overlay spectrum sharing of a frequency agile cognitive radio

using spectrum on an opportunistic basis………………………………………………43

Figure 4.1: compared between femtocell networks and normal microcells……………46

Figure 4.2: LTE access point connected with the core network via wired broadband IP

connection……………………………………………………………………………….47

Figure 4.3: femtocell HeNB in LTE…………………………………………………….47

Figure 4.4: Wired and wireless femtocells networks…………………………………...48

VI

Figure 4.5: 2-tier network femtocells under umbrella macrocells………………...……49

Figure 4.6: CR over LTE…………………………………………………………..……50

Figure 4.7: Backhaul Link Using TV White Space Frequency Bands………………….51

Figure 4.8 Interference scenarios in OFDMA-based femtocell networks………..….52

Figure 4.9: Agile Radio Resource Management………………………………..….…..53

Figure 5.1: Simulation for wireless department from AutoCAD and Photoshop software

Figure 5.2: laser meter device and how to use it………………………………………..…..56

Figure 5.3: EME spy 140……………………………………………………………..……..57

Figure 5.4: connecting between EME SPY and laptop……………………………….…….58

Figure 5.5: graph shows all technologies after measurement process……………………...58

Figure 5.6: shows UL and DL for only DCS technology………………………...…………59

Figure 5.7: average power destiny per 10 samples……………………………………….…59

Figure 5.8: example for some samples ……………………………………………………..60

Figure 5.9: example of excel sheet after conversion ……………………………………….61

Figure 5.10: downlink of the head of the department office……………………………..…62

Figure 5.11: uplink of the headquarter office………………………………………….....…62

Figure 5.12: uplink graph for our session room………………………………………..…..62.

Figure 5.13: downlink graph for our session room……………………………………..…...62

Figure 5.14: power levels on the measured points………………………………………..…63

Figure 5.15: color code for the power levels measured in dBm………………………….….63

Figure 5.16: FSH device……………………………………………………………….…….64

Figure 5.17: The same signal with different RBW…………………………………………..64

Figure 5.18: FSH results importing results ………………………………………………….65

Figure 5.19 Example from measured points………………………………………………....66

Figure 5.20 Example from measured points…………………………………………….…...67

Figure 5.21 detected signal in band 1880 to 1900…………………………………………...70

VII

LIST OF TABLES

Table 2.1: RBS difference……………………………………………………..13

Table 3.1: Capex Assumptions…………………………………………….….31

Table 3.3: Cognitive Access Network Capex Assumptions………………..…32

Table 3.4: Opex assumptions………………………………………………….33

Table 5.1: holes in each office………………………………………………...68

Table 5.2: Indoor analysis common holes results……………………………..69

VIII

Abbreviations

3GPP 3rd Generation Partnership Project

AS Access Stratum

BS Base Station

C-eNodeB Cognitive eNodeB

CN Core network

CR Cognitive Radio

CRA Cognitive Radio Architecture

CRN Cognitive Radio Network

DSM Dynamic Spectrum Manager

eNodeB evolved-NodeB

EPC Evolved Packet Core

EPS Evolved Packet System

E-UTRAN Evolved Universal Terrestrial Radio Access Network

FCC Federal Communications Commission

HSS Home Subscription Server

IP Internet Protocol

LTE Long term Evolution

MIMO Multiple Input Multiple Output

MME Mobility Management Entity

NAS Non Access Stratum

OFDM Orthogonal Frequency Division Multiplexing

PCRF Policy and Charging Resource Function

P-GW Packet Data Network Gateway

PU Primary User

QoS Quality of Service

RAN Radio Access Network

RB Resource Block

RRM Radio Resource Management

RSS Receive Signal Strength

SAE System Architecture Evolution

SDR Software Defined Radio

S-GW Serving Gateway

SU Secondary user

UE User Equipment

WLAN Wireless Local Area Network

CHAPTER 1 |introduction

2

Chapter 1

Introduction

1.1 Overview

Increasing demands for capacity and high spectral efficiency by bandwidth thirsty

applications such as mobile gaming, video streaming etc are steering the technological

evolution in wireless communications systems today. According to [1], mobile traffic

has increased rapidly at about 130% annually over the last 5 years, generating a huge

gap between the customer demand and the technologies enabling them. Today, relying

on sophisticated modulation techniques alone is no longer an option to address this

traffic explosion. Introducing new advanced technologies that provide improved capacity

and efficient spectrum utilization hence is of paramount importance.

Long Term Evolution (LTE) is one solution proposed by the 3rd Generation

Partnership Project (3GPP) to meet the bandwidth demands. In contrast to the

traditional circuit-switched model of previous cellular systems, LTE supports only

packet-switched services. It aims to provide seamless connectivity to its users with no

disruption even during high mobility scenarios. Release 8 of LTE provides peak rates

of 300 Mb/s, a radio-network delay of less than 5 ms and new flat radio-network

architecture designed to simplify operation. It also enables asymmetric spectrum

utilization by providing the possibility for different uplink and downlink bandwidths.

Even though LTE is the front runner in sufficing current demands, trend remains that

wireless networks receive minimal spectrum efficiency gain by improving just the air

interface technologies alone.

a survey of spectrum utilization [2] made by the Federal Communications

Commission (FCC) has indicated that a large portion of the actual licensed spectrum

is used sporadically, resulting in spectral inefficiency. In this context, the capabilities of

CR offer the possibility to significantly enhance the performance of the wireless

systems. Cognitive Radio, a term coined by Mitola [8], is a smart radio that is aware of

its surroundings at all instances and adapts its behavior based on the knowledge

acquired. In CR, users with no licenses, also called secondary users (SU), check the

spectrum availability from time to time and choose the idle channels for communication.

Once the primary user (PU) needs the channel, the SU switches to a different idle

channel if available (to avoid interference with PU) or otherwise terminates the

transmission altogether, thereby exploiting the underutilized spectrum opportunistically.

It was also established by Mitola that architecture was necessary for establishing a

cooperating platform for such environment-aware nodes. Most of the research on

cognitive radio architecture focus on providing basic cognitive frameworks designed

CHAPTER 1 |introduction

3

to solve specific communication problems. Implementation on CR systems is limited

to be tested developments [13], [14], [15].

No significant work has been done so far in establishing general control architectures

for cognitive radio systems, a primary focus of this project.

1.2 Objective of cognitive radio

The implemented Cognitive Radio Architecture (CRA) should serve as a platform for

investigating different resource management algorithms. It should be aware of its

environment and configure parameters based on the derived knowledge to produce

radio resource management (RRM) decisions and actions. This architecture should

also be LTE compliant so that it has the capabilities to match the current standards

for their performance. The feasibility of using scripting languages to implement such

complex architectures is also to be tested. This not only makes the implementation

faster, but also makes it easier to add more functionality with minimal degradation in

performance. Interactions with external networks should be realized, for the latter to

utilize the predefined services offered by the CRA. Resource Management algorithms

that demonstrate the Cognitive and self organization capabilities of the established

system should be implemented.

1.3 Project Organization Chapter 2 gives a basic introduction to LTE/LTE-A systems. The basic architecture of

LTE and the network elements, interfaces, pictures for LTE equipment and LTE value

added services (VAS).

Chapter 3 gives a basic overview of cognitive radio networks and the key technologies

that enable it, Cognitive Radio architecture and value added services.

Chapter 4 gives cognitive radio over LTE and using the Femtocell wired backhaul and as

a relay and its advantages in terms of coverage and capacity in addition to Capex and

Opex also we addressed using the Cognitive radio as backhaul link using TV white space

frequency bands in LTE.

Chapter 5 gives procedures in terms of the used devices, assumption, our key

performance indicators (KPIs), practical results and how to make a link between practical

results and theoretical points that were introduced in chapter 4.

CHAPTER 2 |LTE

4

Chapter 2

Long Term Evolution (LTE) System

2.1 Introduction

The recent increase in data usage by mobile terminals was the prime motivation for

3rd Generation Partnership Project (3GPP) to work on the Long- Term Evolution

(LTE). With its highly flexible radio interface, LTE substantially improves end-user

throughput, sector capacity and reduces user plane latency thereby significantly

improving the user experience. It provides peak rates of 300 Mb/s, a radio-network

delay of less than 5 ms [17]. Since IP protocol is being tipped as the favorite for

carrying all types of traffic, LTE in contrast to the circuit-switched model of previous

cellular systems, provides support for only packet switched services. It aims to

provide seamless IP connectivity to its users, with no disruption in its service even

during high user mobility. It relies heavily on physical layer technologies such as

Orthogonal Frequency Division Multiplexing (OFDM) and Multiple- Input Multiple-

Output (MIMO) systems to achieve its targets. LTE was also designed to minimize

the system and User Equipment (UE) complexities, allow flexible spectrum

deployment in existing or new frequency spectrum and to enable co-existence with

other 3GPP Radio Access Technologies (RATs). The general network architecture of

LTE is provided in the next section. The network elements that make up the EPC

and E-UTRAN are described in detail in the following subsections. an overview of

the network architecture as shown in (2.1) of a Long Term Evolution (LTE) system

according to the Release 8.

Figure 2.1: System Architecture Evolution (SAE)[17]

CHAPTER 2 |LTE

5

2.2 Core network

The core network is responsible for the overall control of the UE and establishment of the

associated bearers to it. The main logical elements of the core network as introduced in

[19], [20] are:

• MME: The Mobility Management Entity (MME) is the main control node that

processes the signaling between the UE and the CN. It is responsible for

Authentication and security, Mobility management, idle mode UE tracking and

paging procedure including retransmissions [20]. The protocols running between the

UE and the CN are known as the Non Access Stratum (NAS) protocols.

• P-GW: The PDN Gateway connects the EPC to the internet. P-GW is

responsible for allocating IP address to every UE attached to the system, as well as

enforcing QoS and flow-based charging policies, according to rules set in PCRF. It

also acts as a mobility anchor between 3GPP and non-3GPP technologies .

• S-GW: Serving Gateway (S-GW) acts as the local mobility anchor for data

bearers when UE moves between eNBs. All user IP packets are transferred to the

access network through S-GW. It manages and stores UE contexts such as, IP bearer

service parameters, routing information etc [22]. S-GW performs some administrative

functions such as replication of the user traffic in case of lawful interception,

collecting information for charging from visited network etc. It also serves as the

mobility anchor for interworking with other 3GPP technologies.

• HSS: Home Subscription Server (HSS) is a database that contains subscription

information about all permanent users. It stores the master copy of the subscriber

profile, including information about feasibility of roaming into a particular visited

network, the allowed PDN connections etc [20]. The HSS may also integrate the

authentication center (AUC), which generates the vectors for authentication and

security keys.

• PCRF: Policy and Charging Resource Function (PCRF) is the network element

that is responsible for Policy and Charging Control (PCC) in LTE networks. It

makes decisions on how to handle the services in terms of QoS.

CHAPTER 2 |LTE

6

2.2.1 Access network

The LTE Radio Access network typically consists of a single entity, eNodeB which

takes care of all radio related functionality of the system, there by considering

the architecture to be flat. LTE E-UTRAN hence has only two network elements:

• UE: User Equipment (UE) is the device that the end user uses for communication.

UE is responsible for signaling the network for setting up, maintaining and removing

the communication links when necessary. It also performs tasks instructed by the

eNodeB, such as handovers and reporting the terminals location etc.

• eNodeB: performs ciphering/deciphering of the user plane data, heade r

compression/decompression of IP packets for avoiding significant IP overhead. It is

also responsible for the Radio Resource Management (RRM) functions like radio

bearer control, radio admission control, radio mobility control, scheduling and

dynamic allocation of resources to UEs [20]. In addition, the eNodeB also plays an

important role in Mobility Management by taking decisions to hand over UEs

between cells based on the radio signal level measurements sent out by the UEs.

LTE integrates all radio controller functions into the eNodeB mostly because there is

no need to support s o f t handovers unl ike in previous technologies. The protocols

that run between the eNBs and the UE is called as the Access Stratum (AS)

protocols.

2.3 Physical layer

L1 or the physical layer is responsible for coding, modulation, mapping of the signal

to the appropriate physical time–frequency resources etc. Adaptive Modulation and

Coding (AMC) schemes are used to protect data against channel errors. Physical

layer also provides indications to the upper layers regarding the link quality by

processing the measurement reports from the UE. Multiple input multiple output

(MIMO) configurations are supported at both eNodeB and the UE. Orthogonal

Frequency Division Multiple Access (OFDMA) with a sub-carrier spacing of 15 kHz

and this for DL and Single Carrier Frequency Division Multiple Access (SC-FDMA)

have been chosen as the transmission schemes for the UL. Each radio frame in LTE

is 10ms long. 1 frame further contains 10 sub-frames and one sub frame contains two

time slots, each slot capable of carrying 6 or 7 OFDM symbols. Each OFDM

Symbol further contains 12 subcarriers in frequency domain. One such sub- carrier is

called a resource element and it is the smallest physical resource in LTE. 7 OFDM

symbols (or one slot) in time domain along with 12 sub carriers (or one ODFM

symbol) in frequency domain together constitute as a Resource Block, the smallest

frequency-time resource unit assigned to every UE. An LTE resource block is as

shown in figure (2.3).

CHAPTER 2 |LTE

7

Figure 2.3: LTE Resource block.[20]

2.4 LTE-Advanced (LTE-A)

In LTE-Advanced as introduced in [23] focus is on higher capacity: The driving force to

further develop LTE towards LTE–Advanced - LTE Release10 was to provide higher

bitrates in a cost efficient way .

Increased peak data rate, DL 3 Gbps, UL 1.5 Gbps

Higher spectral efficiency, from a maximum of 16bps/Hz in R8 to 30 bps/Hz in R10.

Increased number of simultaneously active subscribers

Improved performance at cell edges, e.g. for DL 2x2 MIMO at least 2.40 bps/Hz/cell.

The main new functionalities introduced in LTE-A are Carrier Aggregation (CA),

enhanced use of multi-antenna techniques and support for Relay Nodes (RN).

2.4.1 Carrier Aggregation

The most straight forward way to increase capacity is to add more bandwidth. Since it is

important to keep backward compatibility with R8 and R9 mobiles; the increase in

bandwidth in LTE-Advanced is provided through aggregation of R8/R9 carriers. Carrier

aggregation can be used for both FDD and TDD.

Each aggregated carrier is referred to as a component carrier. The component carrier can

have a bandwidth of 1.4, 3, 5, 10, 15 or 20 MHz and a maximum of five component

carriers can be aggregated. Hence the maximum bandwidth is 100 MHz. The number of

aggregated carriers can be different in DL and UL, however the number of UL

component carriers is never larger than the number of DL component carriers. The

individual component carriers can also be of different bandwidths, see figure (2.4).

http://www.3gpp.org/LTE

CHAPTER 2 |LTE

8

Figure 2.4 : Carrier Aggregation – FDD[23]

The LTE-A UE can be allocated resources DL and UL on up to five Component Carriers

(CC) while The LTE UEs can be allocated resources on any ONE of the CCs. The CCs

can be of different bandwidths. The easiest way to arrange aggregation is to use

contiguous component carriers within the same operating frequency band (as defined for

LTE), so called intra-band contiguous. This might not always be possible, due to

frequency allocation scenarios. For non-contiguous allocation it could either be intra-

band, i.e. the component carriers belong to the same operating frequency band, but are

separated by a frequency gap, or it could be inter-band, in which case the component

carriers belong to different operating frequency bands as shown in figure (2.5).

Figure 2.5: Carrier Aggregation – Intra- and inter-band alternatives.[23]

When carrier aggregation is used there is a number of serving cells, one for each

component carrier. The coverage of the serving cells may differ – due to e.g. component

carrier frequencies. The RRC connection is handled by one cell, the Primary serving cell,

CHAPTER 2 |LTE

9

served by the Primary component carrier (DL and UL PCC). The other component

carriers are all referred to as Secondary component carrier (DL and possibly UL SCC),

serving the Secondary serving cells. In the inter-band CA example shown in figure (2.6), carrier aggregation on all three

component carriers is only possible for the black UE; the white UE is not within the

coverage area of the red component carrier.

Figure 2.6: Carrier Aggregation; Serving Cells Each Component Carrier corresponds to a serving cell. The

different serving cells may have different coverage.[23]

2.4.2 MIMO, Multiple Input Multiple Output (or spatial multiplexing)

MIMO is used to increase the overall bit rate through transmission of two (or more)

different data streams on two (or more) different antennas - using the same resources in

both frequency and time, separated only through use of different reference signals - to be

received by two or more antennas as shown in figure (2.7).

Figure 2.7: MIMO[23]

CHAPTER 2 |LTE

10

Two different data streams are transmitted on two TX antennas and received by two RX

antennas, using the same frequency and time, separated only by the use of different

reference signals. MIMO can be used when S/N (Signal to Noise ratio) is high, i.e. high

quality radio channel. For situations with low S/N it is better to use other types of multi-

antenna techniques to instead improve the S/N, e.g. by means of TX-diversity, as shown

in figure (2.8).

Figure 2.8: MIMO is recommended for high S/N and TX diversity is preferably used for low S/N scenarios[23].

In LTE-A three new UE categories are introduced, category 6, 7 and 8 – where UE

category 8 supports the maximum number of CC and 8x8 spatial multiplexing.

2.4.3 Relay Nodes

In LTE-A, the possibility for efficient heterogeneous network planning – i.e. a mix of

large and small cells - is increased by introduction of Relay Nodes (RNs). The Relay

Nodes are low power base stations that will provide enhanced coverage and capacity at

cell edges, and hot-spot areas and it can also be used to connect to remote areas without

fiber connection. The Relay Node is connected to the Donor eNB (DeNB) via a radio

interface, Un, which is a modification of the E-UTRAN air interface Uu.

Hence in the Donor cell the radio resources are shared between UEs served directly by

the DeNB and the Relay Nodes. When the Uu and Un use different frequencies the Relay

Node is referred to as a Type 1a RN, for Type 1 RN Uu and Un utilize the same

frequencies, see figure (2.9). In the latter case there is a high risk for self interference in

the Relay Node, when receiving on Uu and transmitting on Un at the same time (or vice

versa). This can be avoided through time sharing between Uu and Un, or having different

locations of the transmitter and receiver. The RN will to a large extent support the same

functionalities as the eNB – however the DeNB will be responsible for MME selection.

CHAPTER 2 |LTE

11

Figure 2.9: The Relay Node (RN) is connected to the DeNB via the radio interface Un[23]

UEs at the edge of the donor cell are connected to the RN via Uu, while UEs closer to the

DeNB are directly connected to the DeNB via the Uu interface. The frequencies used on

Un and Uu can be different, out band, or the same, in band. In the in band case there is a

risk for self interference in the RN.

2.5 LTE solutions

3GPP technologies All major 3GPP and 3GPP2 technology tracks are supported namely:

GSM/EDGE, WCDMA/HSPA, CDMA and LTE as well as non 3GPP technologies such

as Wi-Fi.

RBS 6000 – Owning the future : supports GSM/EDGE, WCDMA/HSPA, LTE, CDMA

and Wi-Fi in a single package. The RBS 6000 provides backwards compatibility with the

highly successful RBS 2000 and RBS 3000 product lines.

RBS Modules

Figure 2.10: Remote Radio Unit (RRU)[25]

CHAPTER 2 |LTE

12

The remote radio unit (RRUS) is designed to be installed close to the antennas. The

units support multi-standard operation, which means that they can operate on GSM,

WCDMA or LTE on the same RRUS hardware.

RBS difference:

Table 2.1: RBS difference

RBS 6202 RBS 6201 RBS 6101 Band 14 (758-768 MHz)

Band 20 (791-821 MHz)

Band 28 (758-803 MHz)

Band 14 (758-768 MHz)

Band 20 (791-821 MHz)

Band 28 (758-803 MHz)

Band 14 (758-768MHz)

Band 20 (791-821MHz)

Band 28 (758-803MHz)

Transmitter

Frequency

Range

Band 14 (788-798 MHz)

Band 20 (8320862 MHz)

Band 28 (703-748 MHz)

Band 14 (788-798 MHz)

Band 20 (8320862 MHz)

Band 28 (703-748 MHz)

Band 14 (788-798MHz)

Band 20 (832086MHz)

Band 28 (703-748MHz)

Receiver

Frequency

Range

5 , 10, 15 or 20 MHz 5 , 10, 15 or 20 MHz 5 , 10, 15 or 20 MHz Channel

Bandwidth

Up to 60W Up to 60W Up to 60W Transmitter

Power Output

483 x 370 x 489 mm 600 x 483 x 1435 mm 700 x 700 x 1450 mm Cabinet Size

(W x D x H)

(70 kg) fully equipped 215 kg (180 kg) fully equipped,

excluding batteries Cabinet

Weight

-48 VDC -48 VDC, +24VDC, 100-

250 VAC

110-250 VAC, -48 VDC Site Power

Temperature: +5° to +50°

C

Relative humidity: 5-85%

Temperature: +5° to +50° C

Relative humidity: 5-85%

Temperature: -33° to +50° C

Relative humidity: 15-100% Operating

Requirements

2x2 MIMO support 2x2 MIMO support 2x2 MIMO support MIMO

indoor site built for built for outdoor use Usage

Support Support Support Remote

Electrical Tilt

(RET)

CHAPTER 2 |LTE

13

Shape

2.6 Value added services of LTE 2.6.1 Introduction

Service providers around the world are spending billions of dollars rolling out new LTE

infrastructure. Over 20 operators worldwide have already deployed LTE services

including T-Mobile, China Mobile, AT&T, NTT DoCoMo, Sprint, Telstra, Vodafone and

Telefonica. They will soon look for new revenue streams to make the best use of their

huge investments in LTE network infrastructure.

Some interesting new business models will, and are already appearing in different

application categories, some for service providers alone, some in co-operation with a

hardware or application service provider partner.

2.6.2 Car information and entertainment systems

Vehicle-mounted LTE routers will enable high-speed downlink of up to 100 Mb/s to the

car. This is enough to support 5 parallel high-definition TV channels, and more than

enough to support the more typical mix of video, voice, internet access and social media

applications used by passengers. Content will be pushed to the vehicle depending on

where it is.

It is only a matter of time before every vehicle is equipped with either a vehicle-mounted

LTE router or mobile phone adapter which turns a 4G phone into a mobile Wi-Fi hotspot

as shown in fig (2.14)

Public Safety LTE gives government personnel the most advanced wireless broadband

technology, providing the speed, priority, control, security and performance they need.

The VML700 LTE Vehicle Modem connects the patrol car, fire apparatus or command

vehicle to an LTE network, bringing the benefits of wireless broadband to the vehicle.

CHAPTER 2 |LTE

14

Figure 2.11: Example for The VML700 LTE vehicle modem[25]

Business Model

Operator revenue based on vehicle LTE services will be based on well-known models

used for mobile phones: traffic based flat rate, local or wide area roaming, plus

subscription to services such as digital tour guide and on demand TV content.

2.6.3 Remote security systems

LTE will enable the cost-effective placement of streaming video cameras in covert and

hard-to-reach areas. LTE will make it economically feasible to remotely monitor

stockrooms, retail outlets, factories, healthcare facilities, airports, prisons, schools,

hotels… etc.

Indeed, the humans (or robots) keeping watch over these locations can be located

thousands of kilometers away, making security surveillance an out source able service,

similar to how remote call centers developed alongside low-cost long-distance telephony.

Business model

Operators will be able to bill for security services based on equipment installation,

equipment provisioning and leasing, management software, hours per day used (for

example, only after shop closing times, or only when motion detected), and cloud-based

storage of video streams as shown in fig(2.12).

CHAPTER 2 |LTE

15

Figure 2.12: remote security[25]

2.6.4 LTE last-mile connections

\Current residential broadband internet connections, both Cable and ADSL, require a

physical connection to the home. As the price for LTE connectivity drops, a wireless 4G

connection directly to an Wi-Fi router will replace the wired connections, decreasing

costs, eliminating installation and service calls (“truck roll”), and lowering the costs of

connections to new residential buildings where a single LTE to Wi-Fi router could

provide service to hundreds of residential units.

Business model

Similar to current ADSL/cable subscriptions with reduced installation, equipment and

technical support costs. LTE-to-WiFi routers will replace cable and ADSL last mile

connections as shown in figure (2.13).

Figure 2.13: LTE-to-WiFi router[25]

CHAPTER 2 |LTE

16

2.6.5 LTE location-based services

LTE connectivity combined with satellite-based (or other) global positioning systems will

give operators the ability to offer new types of services as Enhancing shopping and

tourism.

Vision is our primary sense, accounting for more than 80% of all information we receive

about the world. Video-rich LTE services will therefore improve many services with

position-relevant content that enhances the mobile shopping and tourism experience.

Business model

These services can be hosted on any LTE-enable mobile device, giving operators the

ability to provide traditional telecom services in addition to charging for enhanced

features such as location-relevant videos, historical information, information about

friends, family (and possible new friends) in the vicinity of the user.

2.6.6 Mobile cloud services Latency and other technological advantages will allow operators to deploy LTE mobile

cloud services, for example, the Japanese operator NTT DoCoMo is focused. One of

these services is mobile video telephony with simultaneous translation, which will allow

users from different countries to communicate with each other regardless of the language.

2.6.7 mHealth and mCommerce Cellular operators enhanced the activity in the sphere of mobile telemedicine (mHealth ( .

For medical data transmitting through mobile networks; it is necessary to provide high

throughput for fast delivery, reliable communications, and data integrity and

confidentiality. The (3G) meets these requirements only partly. Because of the LTE

technology has a better performance than 3G, so LTE has the capabilities to support the

(mHealth) For example; it allows the use mHealth applications of higher quality such as

video conferencing between a doctor and a patient.

mCommerce on LTE networks. This is caused by the possibility of introducing a new

generation of services, such as mobile commerce, combined with location-based (LBS)

services with elements of 3D and virtual reality.

2.7 LTE Regularity

A Wide range of regulatory, operational, and technical provisions ensure that radio

services are compatible with another and harmful interference among countries is

avoided. The Radio Regulations are updated in response to changes in needs and

demands at World Radio communication Conferences (WRC .( The same thing for

different operator at the same country, each one has different frequency band for GSM

and different carriers for UMTS.

CHAPTER 2 |LTE

17

Mobile operators with the right LTE strategy have an opportunity to use the rise of fourth

generation networks to improve their overall market position

After more than 50 rounds of bidding, the UK’s 4G auction had concluded, with EE,

Three, Niche Spectrum Ventures Ltd (part of BT), O2 and Vodafone raising £2.34 billion

($3.62 billion) to secure a total of 250 MHz of spectrum[16].

According to results published by the UK’s communications regulator Ofcom, the five

bidders each paid in the hundreds of millions of pounds to secure parts of the important

800 MHz and 2.6 GHz bands. O2 emerged as the winner of one 800 MHz lot and will

have to provide indoor coverage to 98 percent of the UK population by the end of 2017.

The bids are broken down as follow

EE won two 5 MHz lots of 800 MHz and two 35 MHz lots of 2.6 GHz spectrum for a

total of £588,876,000 ($909.5 million)

Three secured two 5 MHz lots of 800 MHz for £225,000,000 ($347.5 million), as BT’s

Niche Spectrum Ventures won two 15 MHz of 2.6 GHz and one 20 MHz lot of 2.6 GHz

(unpaired) spectrum for £186,476,000 ($288 million).

Paying £550,000,000 ($852 million), Telefonica (O2) went on to win two 10 MHz lots of

800 MHz spectrum.

Vodafone, perhaps the most vociferous of the operators following EE’s early launch of its

1800 MHz 4G networks last year, managed to get its hands on two 10 MHz lots of 800

MHz, two 20 MHz lots of 2.6 GHz and one 25 MHz lot of 2.6 GHz for a total of

£790,761,000 ($1.22 billion).Vodafone ended up paying over £200 million more to

secure more 4G spectrum than its rivals as shown in fig (2.14).

Figure 2.14: UK operators in LTE auction (prices and band)[26]

CHAPTER 2 |LTE

18

With Vodafone and EE winning vital 800 MHz and 2.6 GHz spectrum, they are well-

placed to deliver wide-ranging 4G networks in both urban and rural areas. Winning 2.6

GHz spectrum, the two carriers will deliver high-speed connections, while 800 MHz is

better suited to travelling long distances (using the same spectrum previously reserved for

analog TV signals).

Now that the auction has ended, Ofcom will now work with the winning parties to

identify where their spectrum will be located. Once this has been completed (and fees

have been paid), Ofcom will grant the spectrum to the auction winners, who will then be

able to roll out their 4G network.

EGYPT 2016 intends to offer LTE (4G) license. National Telecommunication Regularity

Authority (NTRA) make internal spectrum auction between Egypt's operators (Mobinil -

Vodafone - Etisalat) for available whitespace which will be

determined when we scan the following bands :

First band: (800-1000) MHz

Second band: (1700-1900) MHz

Third band: (1900-2100) MHz

LTE technology operates with scalable frequency band, So each operator can take a

license for (1.4, 3,5,10 or 20 MHZ), it is up to operator. We are group (C) and Next

week we will scan 1700-1900 MHZ frequency band and record the results for available

white space.

CHAPTER3| Cognitive Radio Overview

19

Chapter 3

Cognitive Radio overview

3.1 Introduction to Cognitive Radios

Electromagnetic spectrum is considered as a national resource and is managed by

government agencies such as National Telecommunication Regulatory Authority

(N T R A ) , FCC (for United States), TRAI (for India), FICOR (for Finland) etc. In

Europe, frequencies up to 6 GHz are used for various mobile technologies as shown

in figure 3.1. The radio spectrum is usually allocated in chunks to different

organizations for commercial purposes on a long term basis over wide geographical

areas. Recent studies [7] have indicated that large portion of this spectrum is

underutilized, r e s u l t i n g in large spectral inefficiency. This incompetence caused by

allocating a primary user with a band of frequencies which he never uses, was termed

as spectrum hole in [4]. Cognitive Radio, introduced in [25] makes efficient use of the

spectrum by exploiting the existence of such spectrum holes. Secondary users (SU)

check the spectrum availability from time to time and choose the idle channels for

communication during the absence of primary users (PU). Coordination b e t we e n

SUs and PUs for efficient and fair spectrum sharing is usually done by a central

network entity called the Spectrum Broker. The concept of spectrum hole is shown in

figure 3.2(a).

Figure 3.1: Mobile Spectrum in Europe, ref. [2]


20

3.2 Cognitive Radio characteristics

Haykin, important researcher Six years after Mitola, defines Cognitive Radio as an

intelligent wireless communication system capable of analyzing its surrounding

environment and adapting to the statistical variations by a understanding by

building methodology, with efficient spectral utilization and reliable

communication as its objective [4]. These systems are characterized by:

• Cognitive capabilities: Ability to sense and understand the environment.

• Reconfigurability: Ability to adapt t he operational parameters according to

the sensed information for improved performance.

CR looks towards software-defined radio (SDR) for reconfigurability. It is an

extended version of SDR that additionally performs sensing and adaption based on

its environment. The concept of SDR is discussed later. The CR transceiver unit

shown in 3.2(b) has a RF front-end (amplifier, mixer, A/D converter) which is

capable of being tuned to any part of the spectrum and an equally flexible baseband

processing unit.

Figure 3.2: Cognitive Radio characteristics (a) Spectrum hole concept, (b) Cognitive Radio

transceiver, ref [3]


21

3.2.1 Cognitive Capability

Figure 3.3: General Cognitive Cycle established in [4]

Different signal processing and machine learning algorithms have to be implemented

for achieving cognitive capabilities. The basic cognitive cycle described by [4] is

shown in figure 3.3. On receiving RF stimuli due to the change in its operating

environment, Cognitive radio starts scanning the entire radio spectrum for available

spectrum holes. Based on its internal policies, it selects a suitable channel for

communication purposes. Coordinated spectrum a c c e s s is necessary since many

CRs try to access the spectrum simultaneously resulting in collision. If the PU

requires the channel, a spectrum handoff has to be initiated. Based on the above

principles, CR cycle is classified into four basic spectrum management functions

spectrum sensing, spectrum decision, spectrum sharing, and spectrum mobility each

of which are explained further.

CHAPTER 4| Cognitive radio Network over LTE

22

.

3.2.1.1 Spectrum Sensing Spectrum sensing is the most primitive functionality of any CR network. But

detecting weak primary signals over a wide spectrum range in real-time can be

quite a task. Different types of sensing techniques have been established over

the years based on suitability and type of knowledge available at the CR node, a

summary of which is available in [3]. Matched filter detection is employed when

prior knowledge of the characteristics of the primary user is available. In this

method, noise is assumed to be Gaussian and the source signal is

deterministic and known to the receiver, making it is easier to match the

source and received signals. If this prior information is not available, Energy

detection is used wherein signal is assumed to be independent and identically

distributed (iid), and the detection accumula tes e n e r g y from all its signal

samples. The performance of such techniques is vulnerable to the uncertainty

in noise power. A more robust Feature de tec t ion technique can be employed

to overcome this susceptibility. By exploiting the cyclostationarity of the

signal, this technique extracts features in the Primary user signal. However, it

is computationally complex and requires long observation intervals, as

feature detection is performed by analyzing a spectral correlation function.

3.2.1.2 Spectrum Decision

After sensing available free spectrum for its communication purpose, CR now

has to choose the best possible spectrum hole. Even though this selection can

be done at random, choosing spectrum hole based on the QoS requirements,

channel characteristics and on the behavior of the PU results in a more

efficient system [3]. Activities of other CR nodes may also have an impact

while making this decision. Each available spectrum hole is allocated with a

rank based on the above metrics (no of other CR nodes, channel characteristics

etc.) And the spectrum with the highest rank is finally used for

communication. The probability of primary user appearing in the same

channel during a CR transmission also plays an important role in ranking the

spectrum hole. In case no single spectrum band is available to meet the given

QoS requirement, multiple non-contiguous spectrum bands can be clubbed

together for transmission.


23

3.2.1.3 Spectrum Sharing

The shared nature of the wireless channel forces different CR nodes to cooperate

between one another. This cooperation is increasingly difficult in CR networks, as

CR nodes not only have to co-exist with one another but also have to establish

cooperation with their primary users. Spectrum sharing can be classified based on

several criteria. It can be classified into

Centralized or Decentralized (Distributed).

Cooperative or non-Cooperative.

Overlay sharing or Underlay sharing.

Centralized, where spectrum allocation is controlled by a central entity or

decentralized, where spectrum allocation is done distributive by applying

local policies. Based on their information sharing mechanism, Spectrum

sharing can also be classified into cooperative, where CR nodes form

clusters to share interference information locally between one another or

non-cooperative, where no such information is exchanged between the

neighbors. Yet another type of classifying Spectrum sharing involves

Overlay sharing, where CR and PU use only explicit spectrum that are

not mutually used by one another or Underlay sharing where no such

explicitness is defined and the transmission of one is considered as noise by

the other.

3.2.1.4 Spectrum Mobility

Whenever a primary user becomes active, it is compulsory for CR to switch to

new operating spectrum band, as the priority always lies with the PU. This

process is called the spectrum mobility. For undisrupted communication, i t

is necessary for this switch to have minimal overhead and also requires a new

kind of handoff called the spectrum handof f . Different algorithms have to be

implemented to take care of such handoffs. Every time a CR switches to a

new channel, operation parameters have to be modified accordingly. The use of

Spectrum mobility management (SMM) entities to ensure smooth transition

and minimum performance degradation during a spectrum handoff as Initiated

in [3].

3.2.2 Geo-location database

It is the responsibility of every Cognitive Node to perform the above spectrum

management functions whenever it requires new frequencies for

communication. Even though this is feasible, it sometimes becomes a

boring process to scan the entire spectra just to get a suitable channel for


24

a limited period of time, resulting not only in additional overhead but

also in power draining the system. To eliminate this inefficiency, the

concept of using Geo-location database for accessing TV white space

was introduced in [26]. Instead of performing all the spectrum

management functions by itself, a Cognitive Radio sends a query to a

central database for the available frequencies. it may use for a specific

duration based on its current locations. The Geo-location database provides

this response along with predefined rights and obligations attached in using

the allocated spectrum. It now becomes the responsibility of the database to

determine how the cognitive radio can coexist with the primary user.

One prerequisite for this method to work, is that the Cognitive Node

should always be aware of its location and it has to update the Geo-location

database of its position. Based on the location of the Cognitive node, the

Geo-location database will prepare a list of the available channels,

calculate the acceptable transmission power levels and the duration for which

the channels are available. Every time the CR device changes its position by

more than predefined distance, it has to obtain new set of parameters from

the database. This result in lighter CR nodes, as the complexities involved in

performing the spectrum management functionality is now moved to Geo-

location databases [5].

3.2.3 Cognitive Radio Network Architecture

The components of the cognitive radio network architecture, as shown in Figure

(3.4), can be classified in two groups as the primary network and the cognitive

network. Primary network is referred to as the legacy network that has an

exclusive right to a certain spectrum band. On the contrary, cognitive network

does not have a license to operate in the desired band. The basic elements of the

primary and unlicensed networks are defined as follows:

Primary User: Primary user has a license to operate in a certain spectrum band.

This access can be only controlled by its base-station and should not be

affected by the operations of any other unauthorized user.

Primary Base-Station: Primary base-station is a fixed infrastructure network

component which has a spectrum license. In principle, the primary base-

station does not have any cognitive radio capability for sharing spectrum with

cognitive radio users. However, primary base-station may be required to have

both legacy and cognitive radio protocols for the primary network access of

cognitive radio users.


25

Cognitive Radio User: Cognitive radio user has no spectrum license. Hence,

the spectrum access is allowed only in an opportunistic manner. Capabilities

of the cognitive radio user include spectrum sensing, spectrum decision,

spectrum handoff and cognitive radio MAC/routing/transport protocols. The

cognitive radio user is assumed to have the capabilities to communicate with

not only the base-station but also other cognitive radio users.

Cognitive Radio Base-Station: Cognitive radio base-station is a fixed

infrastructure component with cognitive radio capabilities. Cognitive radio

base-station provides single hop connection to cognitive radio users without

spectrum access license.

Figure 3.4: Cognitive radio network architecture[3]

As shown in Figure (3.4), cognitive radio users can either communicate with each

other in a multi-hop manner or access the base-station. Thus, in our cognitive

radio network architecture, there are three different access types over

heterogeneous networks, which show different implementation requirements as

follows:


26

Cognitive Radio Network Access: Cognitive radio users can access their own

cognitive radio base-station both in licensed and unlicensed spectrum bands.

Since all interactions occur inside the cognitive radio network, their medium

access scheme is independent of that of primary network.

Cognitive Radio Ad Hoc Access: Cognitive radio users can communicate with

other cognitive radio users through ad hoc connection on both licensed and

unlicensed spectrum bands. Also cognitive radio users can have their own

medium access technology.

Primary Network Access: The cognitive radio user can access the primary base-

station through the licensed band, if the primary network is allowed. Unlike

other access types, cognitive radio users should support the medium access

technology of primary network. Furthermore, primary base-station should

support cognitive radio capabilities.

3.2.4 Software Defined Radio (SDR)

Cognitive Radio is based on the principle that adding dynamic intelligence to the

receivers, with further assistance by external databases, can provide higher

spectral efficiency than the existing systems.

In a SDR, the components of radio that have been typically implemented in

hardware (e.g. mixers, filters, amplifiers, modulators/demodulators etc.) are

instead implemented by means of software on host computers limiting the radio

hardware only for RF transmission and reception purposes.

Figure 3.5: General architecture for Software defined Radios[3]


27

3.3 Cognitive Radio Equipments

3.3.1 SDR Hardware Platforms

The hardware aspects of a SDR platform consist of the radio-frequency (RF) parts,

communications links to the software-based signal processing elements (mostly a

Host-PC) as shown in figure (3.7)

It has three major components:

FPGA: for high-speed digital up and down conversion from baseband to IF.

ADC/DAC: for converting the signal from analog to digital domain and vice-

versa.

Daughterboard: this holds the RF transceivers used for translating the baseband

signal into the carrier frequency.

Figure 3.6: SDR in cognitive system[4]

3.3.1.1 Universal Software Radio Peripheral 2 (USRP2) It is a brainchild of Matt Ettus (Ettus Research LLC). The USRP2 is a second

generation of Universal Software Radio Peripheral, its platform consist Xilinx

Spartan-III FPGA and general purpose AeMB processor. The USRP-E100 is its

next revision that’s a stand-alone system powered by the combination of an ARM

Cortex A8 processor& TI C64x+ DSP and a Xilinx Spartan3A-DSP1800 FPGA.


28

Fig 3.7: USRP[17]

The Complete SDR shape:

Figure 3.8: Simplified Overview of a SDR Setup Built Around an NI USRP[17]

USRP hardware:

Types:

1- NI USRP-2920 (50 MHz to 2.2 GHz Software Radio ):

Price:

Description US Dollars

NI USRP-2920, 50 MHz to 2.2 GHz Software Radio

Bundle

$ 3,220.00


29

2-NI USRP-2921 2-Band, 2.4 GHz to 2.5 GHz and 4.9 GHz to 5.9 GHz

Price:


NI USRP-2921, 2.4 AND 5 GHz Software Radio Bundle $ 3,220.00

3- NI USRP-2922 400 MHz to 4.4 GHz SDR:


NI USRP-2922, 400 MHz to 4.4 GHz Software Radio Kit $ 3,220.00

4-NI USRP-2930 50 MHz to 2.2 GHz SDR with GPS-Disciplined Clock:

Description US

Dollars

NI USRP-2930, 50 MHz to 2.2 GHz + GPS Clock Software

Radio Kit

$ 4,315.00

5- NI USRP-2932 400 MHz to 4.4 GHz SDR with GPS-Disciplined Clock:


NI USRP-2932, 400 MHz to 4.4 GHz + GPS Clock

Software Radio Kit

$ 4,315.00

3.4 Capex and Opex in cognitive radio

Definition in business

The traditional cash flow analysis will be used to get an indication of the

profitability. The cash flow means income (revenues) subtracted by cost

(investments and operational Costs) for a given time period.

Sensitivity analysis is done by changing the value of one (critical) input

parameter and showing how the economical results are changing.


30

ARPU (Average Revenue Per User).

CAPEX (Capital expenditures) is expenditures associated with the

implementation or extension of fixed assets. There is a residual value associated to

these expenses. Investment is often used as a n identical term to CAPEX.

OPEX (Operational expenditures) is defined as expenditures necessary for

running the business or the equipment, indispensable to keep the services active

and

Running. Once made, these expenses have no residual value.

NPV (Net present value) is the sum of a series of cash flows (revenues subtracted

by costs), when discounted to the present value.

NPV is the most important criteria when defining the profitability of the project.

Discount rate is the rate used for discounting amounts to other points in time as in

the calculation of NPV. It reflects the inflation and the fact that the estimated

amounts in the future carry significant uncertainty. Typical values of discount rate

are around 10%.

IRR (Internal rate of return) is the discount rate, that gives NPV = 0. The

higher the IRR is, the better the project is.

Payback period is the amounts of years that it takes to have the accumulated

revenues equal the accumulated costs (CAPEX and OPEX).

ASSUMPTIONS

The business case is calculated for a hypothetical European city with 1 million

inhabitants and with an area of 400 km2. The study period is assumed to be from

2015 to 2020. The discount rate for present value Calculations is set to 10%.


31

Figure 3.9: Cash flow analysis[20]

3.4.1 Sensor network CAPEX

The joint venture will establish sensor network based on fixed sensors covering

gradually the total area of the studied city. In addition it is assumed that 50%

of the terminals belonging to the customers of the joint venture have an integrated

sensor. Other assumptions for the sensor network are given in Table (3.2).

Table 3.2: Capex Assumptions[20]

3.4.2 Cognitive access network CAPEX

The joint venture will use existing base stations as much as possible. Cognitive

Functionality will be added to these by software upgrades and/or additional

hardware. The cognitive functionality in the user terminals is assumed to be


32

included as part of the normal terminal development, hence this cost will be paid

by the users and is not included in the business case calculations. Other

assumptions for the cognitive access network are given in the following table(3.3).

3.4.3 Operational expenditures (OPEX) OPEX is the periodic operating costs for the joint venture when it’s running its

nomadic broadband business. The OPEX assumptions are given in Table (3.4)

Table 3.4: Opex assumptions


33

3.4.4 Results 3.4.4.1 Base case results

Combining costs (CAPEX and OPEX) with revenues gives yearly cash flows and

from cash flows the standard profitability indicators, like NPV (Net Present

Value), IRR (Internal Rate of Return) and pay-back period, can be extracted.

Figure 3.10: Accumulated cash flow[22]

The NPV for this case is 1,9 million Euro (study period 2015-2020), IRR is

24 % and the pay-pack period is in the range of 5 years.

3.4.4.2 Sensitivity analysis

The input assumptions for this kind of future oriented business case are uncertain.

There are many aspects, which are independent of the SENDORA concept, but

have crucial influence on the profitability.

3.4.4.2.1 Fixed sensor density

Table 3.5: NPV SENSITIVITY TO FIXED SENSOR DENSITY[22]


34

The number of fixed sensors must be sufficiently high to get reliable sensing and

so it will have significant influence on the CAPEX and especially on the OPEX.

3.4.4.2.2 Fixed sensor OPEX

Table 3.6: NPV SENSITIVITY Fixed sensor OPEX

3.5 Cognitive Radio Value Added Services (VAS)

No technology can be self-sufficient in today’s world. Cognitive Radio may not only

have to co-exist with other heterogeneous networks in the future, but it can also

be used to complement and support other wireless access technologies for

improved performance. Wireless operators can now make use of the CR technology

for helping them find white space as a supplement to their licensed spectrum to gain

extra mileage in capacity. It can also be used to determine the threshold transmit

power levels by applications so as to reduce co-channel interference. Performance of a

CR-assisted LTE network is analyzed in [5]. The idea behind CRN is that non-licensed

devices (called Secondary Users or SU) take advantage of the licensed bands when the

licensed users (called Primary Users or PU) are not using it.

3.5.1 Medical Body Area Network (MBANs) Body area network (BAN) also known as wireless BAN or body sensor Network (BSN).

We used to put sensors on patient body then connect these sensors to monitors by wires,

but now with MBANs we can replace these wires by wireless. Wearable wireless medical

sensors beneficially impact the healthcare sector, and this market is experiencing rapid

growth. In the United States alone, the telecommunications services market for the

healthcare sector is forecast to increase from $7.5 billion in 2008 to $11.3 billion in

2013[9].We put 24/7 hours wearable devices as we can attach sensor on the patient body


35

and receive his medical status wireless (vital signs) as temperature/pressure/blood

oxygen/ECG...Etc. MBANs improve the mobility of patients and this is a significant

parameter in patients recovery, and facilitate the remote monitoring of patients suffering

from chronic diseases. MBANs have the capability of lowering health care costs, speed

diagnosis, early intervention, and enhancement of patient care as shown in figure (3.10).

By MBANs, medical professionals can access the patient data independent of the patient

location. One of the most significant challenges that face MBANs is the interference as

MBANs are being introduced in unlicensed frequency bands, where the risk of mutual

interference with other electronic devices can be high. CR can potentially alleviate these

problems in medical communication environments and enhance coexistence with other

collocated wireless systems also when accident is happened we can send the patient’s

medical status for the hospital while transporting him at ambulance so when he arrives

the hospital, doctors were having a full medical data about his status. If there were a

delay; it would have no effect because the time we spent in the city traffic would be

larger than this delay [9].

Figure 3.7: Medical Body Area Network[18]


36

3.5.2 Efficient VIdeo Streaming For Cogenitive Radio VANETs (ViCov)

VANET is a vehicular ad-hoc network where vehicles acting as fast moving mobile

nodes as shown in fig (3.10). While ViCoV is a new protocol for efficient video

streaming over Cognitive radio VANETs.

VANET consists of on-board-units (OBUs) installed on the vehicles and road-side units

(RSUs) deployed along sides of the urban roads/highways which facilitate both vehicle-

to-vehicle communications and vehicle-to-infrastructure communications. The OBU is

buffer storage, positioning system, and intelligent antenna further facilitates efficient

video forwarding and collaborative downloading among vehicles or from/to RSUs.

Figure (3.10): Example of video streaming in VANET[15]

Here, we proposed ViCoV, a video dissemination solution for VANET which

operates under different network and traffic conditions. ViCoV selects the best

channel for transmission and exploits the cognitive radio channels to extend the

capacity of the network in the dense traffic scenarios. In this case, ViCoV

implements an intelligent mechanism to select the best CR channel using a times

series model as shown in fig (3.11). In order to avoid the redundant

retransmissions, ViCoV selects the most central vehicles in the network to

rebroadcast the video stream using a new SNA inspired centrality metric.


37

Figure 3.11: Rebroadcaster Nodes Selection[15]

Scenario for video streaming in vehicular network could be broadcasting video content

using RSUs, where a vehicle downloads video via license-free wireless spectrum when it

is within the RSU transmission range. However, supporting video streaming services by

RSUs using the license-free wireless communication is still an open issue due to the

following two concerns. First, the wireless channel suffers from interference, shadowing

and time-varying fading, which has the major effect on video quality. Second, the RSUs

deployment is highly cost. Thus, VANETs are essential to ensure wide dissemination of

the video in the network.

First, Vicov selects the best available dedicated or Cognitive Radio (CR) channels to

disseminate the content. Then, it carefully chooses a minimum sub-set of rebroadcasted

nodes to reduce interferences and to achieve high video quality. The CR channels are

selected based on their stability over the time, whereas the rebroadcasted nodes are

selected based on specific mechanism [11].

ViCoV is considered a perfect solution for video streaming applications which call for

quality of service requirements in terms of PSNR, frame loss and frames delay. It

enhances the Peak Signal-to-Noise Ratio (PSNR) and frames loss as reducing the video

frame loss to its low values (<2%) and increasing the stream PSNR by more than 50%

compared to the other protocols [10].


38

3.5.3 Intelligent Transportation System (ITS)

ITS has a lot of applications as vehicle collision warning, security distance

warning, driver assistance, cooperative driving…etc. ITS is a systems in which

information and communication technologies are applied in the field of road

transport, including infrastructure, vehicles and users, and in traffic management

and mobility management, as well as for interfaces with other modes of transport.

As we mentioned previously there are on-board-units (OBUs) installed on the

vehicles and road-side units (RSUs) deployed along sides of the urban

roads/highways and by the communication bet vehicle-to-vehicle or vehicle-to-

RSU we can get vehicle collision warning, security distance warning, driver assistance, cooperative driving and a lot of ITS applications and CR can provide

the required channels for this communication.

3.5.4 Public Safety

Public safety organizations have become increasingly reliant on wireless

technology to provide the C3 (command, control, and communications)

capabilities needed in emergency response situations.

In order to increase the effectiveness of the response and ensure the safety

of first responders, an optimal public safety wireless system should have the

flexibility to provide capacity and coverage on demand. Interoperability and

scalability are also required, particularly when multiple agencies come together in

a disaster or other emergency response situation [12].

3.5.4 Agriculture

This is made in the states, everything in the farm is connected to sensors and

these sensors send the information to CR for example : the amount of water in the

valve when it increased the sensors tell me that, so I have to decrease the amount

down and this is done automatically with intelligence [12].

3.5.5 Cellular network

The use of cellular networks is undergoing dramatic changes in recent years

because of consumer’s expectations of being always connected, anywhere and

anytime and also the introduction of smart phones, the popularity of social

networks, growing media sites such as YouTube, have all added to the already

high and growing use of cellular networks for conventional data services such as

email and web-browsing visionary

This presents both an opportunity and a challenge for cellular operators. The opportunity

is due to the increased average revenue per user due to added data services. At the same


39

time, but the challenge is that in certain geographical areas, cellular networks are

overloaded, due to limited spectrum resources owned by the cellular operator

By cognitive radio and TVWS new spectrum would becomes available to cellular

operators, CR radio technologies can augment next generation cellular networks like LTE

and Wi-MAX to dynamically use these newly available spectrums either in the access or

backhaul parts of their networks.

3.6 Standards and Regulation

3.6.1 Introduction

The report of the FCC’s Spectrum Policy Task Force defines spectrum regulatory

mechanisms in a similar way. There, the assignment of spectrum rights is

differentiated into an “exclusive use” model, a "command-and-control” model and

a “commons” or “open access” model. The “command-and-control” is currently

the most often used regulation model and refers to the “licensed spectrum for

shared usage” and “unlicensed spectrum”.

Today’s most often used licensing model is to license spectrum for shared usage

restricted to a specific technology. Emission parameters like the transmission

power and interference to neighboring frequencies like out of band emissions are

restricted The purpose of such regulation is to ensure that users of the designated

frequencies do not interfere with each other or with other licensed users. The

license-exempt frequency bands will be regulated by controlling the use of the

spectrum through technical standards, certification of the transmission equipment,

monitoring and enforcement.

The unused UHF TV band and the ISM band are currently considered as the first

target bands for CR applications as it is shown in figure (3.4).

A spectrum auction has already been held for the UHF TV band in a country such

as the USA and secondary trading has been introduced in some countries.


40

Figure 3.12: Cognitive radios share spectrum with different radio systems. Depending on regulatory

status, vertical or horizontal spectrum sharing is done.[20]

3.6.2 Spectrum trading

Spectrum trading is a business opportunity for wireless operators to get revenues

from their spectrum at times when they do not need all of it themselves. Spectrum

trading is also an opportunity for new actors to enter the wireless communication

ecosystem, such as spectrum brokers and pure spectrum owners .

There might also be a need for involvement of regulators to create a database with

local information on spectrum usage and standardization of the protocols needed

to access this database.

Spectrum Trading Opportunities:

Spectrum trading is an important tool to increase overall spectrum utilization and

to open up opportunities for businesses to get access to desired spectrum.

Regulatory rules for spectrum trading have been implemented in some countries

for some spectrum bands, for instance in the UK and US.

At the same time, systems and architectures for cognitive radio technologies are

being developed that are able to dynamically use spectrum bands with higher

flexibility including functionalities such as dynamic bandwidth, spectrum bands

concatenation, sensing, channel switching and cognition.

A Spectrum Trader can be a seller, buyer, leaser or lessee. The trader can even

take on more of these roles and also speculate in the market.

The following actors may take on the role as a spectrum trader:


41

Figure 3.13: Spectrum Trading[20]

Spectrum license owner

This actor owns spectrum that it wants to sell or lease on the spectrum market. Typical

spectrum license owners are TV broadcasters, wireless and mobile operators, the

military, radar communications operators.

Secondary or cognitive radio operator

This actor will participate in the spectrum market as a spectrum buyer or lessee in order

to buy or rent spectrum. This will typically be a new operator without existing wireless

spectrum licenses that need spectrum to offer a wireless service.

Secondary or cognitive radio device

It is the radio device or end user that participates in the spectrum market as a buyer or

lessee.

Spectrum Broker

The spectrum broker can then be defined as a party which arranges transactions between

a buyer and a seller or leaser and lessee, and gets a commission when a deal is executed.

A spectrum broker might have several additional properties such as providing market

information about prices, spectrum details and market conditions.


42

A Spectrum Database

It contains information about the radio spectrum to be traded. This could be information

about who owns the licenses of the spectrum, which uses the spectrum, spectrum

occupancy, spectrum availability, and noise and interference conditions in a spectrum

band, and so on. This information could be retrieved from sensor networks, geo-location

database (a Cognitive Radio must always access a Geo-location database to get the list of

available channels for its location.); wireless communication operators or it could be

downloaded from databases held by the regulators. Different actors could operate a

spectrum database. It could potentially be an independent third party the regulator

(similar to NTRA) or called the spectrum license owner.

A Wireless Sensor Network (WSN)

It can be used to monitor the radio spectrum to be traded for a given area. The WSN can

provide much of the same information as a spectrum database. In addition it can provide

more detailed information about the real-time spectrum status (spatial, temporal and

frequency) such as noise, interference and detailed location information of radio emitters

The Spectrum Regulator

It is interested in having a high utilization of the spectral resources and that people get

high quality services. Since a spectrum market will simply access to spectrum and enable

more dynamic use of spectrum leading to higher spectrum utilization. But, with

incautious regulation of a spectrum market there might be a risk that the spectrum market

will lead to unfair spectrum allocations, increased interference and unhealthy

competition. To mitigate this, the regulator can take the role as the spectrum broker. The

main task of a spectrum regulator in a spectrum market will be to set out the rules,

policies and processes that must be adhered to in a spectrum market.

(Figure 3.6) shows how the spectrum sharing occurs by using the third criteria (overlay or

underlay). Spectrum sharing involves Overlay sharing, where CR and PU use only

explicit spectrum that are not mutually used by one another or Underlay sharing where no

such explicitness is defined and the transmission of one is considered as noise by the

other.


43

Figure 3.14: Underlay and overlay spectrum sharing of a frequency agile cognitive radio using spectrum

on an opportunistic basis.[18]

3.6.3 Standards

Several organizations are working on providing standards for Cognitive Radio

Networks. The European Conference of Post and Telecommunications

Administrations (CEPT) is providing the specifications for CR operations in 470-

790 MHz range. The Institute of Electrical and Electronics Engineers (IEEE)

provides standards such as 802.11af (an extension of 802.11standards) to operate

in TV white spaces. Wireless Coexistence Technical Advisory Group (TAG) has

come up with IEEE 802.19 standards for developing coexistence between

unlicensed wireless networks. IEEE 802.16h provides similar standards for

coordinated and uncoordinated coexistence mechanisms for Wi-Max systems.

Work is also in progress by IEEE 802LAN/MAN Standards committee to develop

IEEE 802.22 standard for cognitive wireless regional area networks (WRANs).

IEEE 802.22 WRANs are designed to operate in the TV broadcast bands while

ensuring that no interference is caused to the incumbent operation.

In the United States the FCC proposed to allow secondary access by cognitive

radio devices to TV bands in 2004. The FCC has established two classes of TV

bands device: those that may establish a network (called Fixed) and those that may

join a network (also called personal/ portable) to operate in the TV bands. Fixed

devices may transmit at up to 4 W effective isotropic radiated power (EIRP). They

are allowed to operate on any channels between 2 and 51 except channels 3, 4, and

37. Personal/portable devices may operate on any unoccupied channel between 21

and 51, except channel 37, and may use up to 100 mW EIRP, except that

operation on the first adjacent channel to TV stations is limited to 40 mW EIRP

[16].


44

Chapter 4

Cognitive radio Network over LTE

4.1 INTRODUCTION

The current deployment of Long Term Evolution (LTE) is an important step forward

towards the fulfillment of a real broadband wireless system. LTE promises to deliver

data-rates of up to 300 Mbps (downlink) and 75 Mbps (uplink) assuming 20 MHz

bandwidth. Moreover, recent research and standardization efforts have focused on

improving the LTE spectral efficiency in order to meet, or even exceed, the International

Mobile Telecommunications-Advanced (IMT-A) requirements. Some of these

innovations are being considered by the Third Generation Partnership Project (3GPP)

as part of the LTE-Advanced (LTE-A) system. Among the considered proposals to be

adopted by LTE-A, special attention is paid on the following three key items:

Optimized management of heterogeneous cell deployments (macro-/pico-

/femtocells).

Aggregation of both continuous and discontinuous spectrum (carrier aggregation).

Dynamic and efficient use of available spectrum resources enabled by Cognitive

Radio (CR) techniques.

Femtocells provide a cost-effective solution in the cellular communications arena by

improving indoor coverage , Recent studies indicate that more than 60% of mobile

traffic is generated indoors [Informa08] In order to improve indoor coverage, cellular

networks have integrated Femtocell Access Points (FAPs), allowing operators to provide

high data-rate services wherever the outer macro cell signal is weak. In general, the

outer signal strength is severely reduced in indoor environments mainly due to building-

penetration losses due to e.g. walls. Generally, this isolation from the outer world, along

with reduced transmission powers (typically 10-20 dBm), Allow femtocells to reuse the

macrocell working frequency band with a consequent capacity increase. However, if the

isolation is not good enough to prevent interference with the macrocell activity,

femtocells should be re-allocated to use other frequency bands. Alternatively, and

bearing in mind the well-known spectrum scarcity and the fact that some bands are

lightly-used, the femtocell could seek for unoccupied spectrum bands belonging to

other technologies. In this case, CR methodologies and functionalities can be used in

order to identify idle frequency bands thus allowing opportunistic and interference-free

transmission with the licensed (or primary) system, concept that is known as

Opportunistic Spectrum Access (OSA).


45

4.2 LTE Cognitive Femtocell Systems

4.2.1 Femtocell Networks

Femtocell is envisioned as a highly promising solution for indoor wireless

communications. The spectrum allocated to femtocells is traditionally from the same

licensed spectrum bands of macrocells. In this case, the capacity of femtocell

networks is highly limited due to the finite number of licensed spectrum bands and

also the interference with macrocells and other femtocells (figure4.1). In these

sections, we propose a radically new communication paradigm by incorporating

cognitive radio in femtocell networks. The cognitive radio enabled femtocells are able

to access spectrum bands not only from macro cells but also from other licensed

systems (e.g. TV systems) provided the interference from femtocells to the existing

systems is not harmful. It results in more channel opportunities for femtocells. Thus,

the co-channel interference in femtocells can be greatly reduced and the network

capacity can be significantly improved. Because of the difference from other

traditional wireless networks, we argue the traditional spectrum sharing schemes such

as coloring methods are not efficient to femtocell networks especially for dense

deployment scenarios

Main characteristics of FAPs:

Low power.

Low cost.

Few UEs/cell.

IP-based backhaul.

Limited coverage.

Main advantages of femtocell deployment at customers’ side:

Larger coverage.

Higher data rate services.

Increased battery life of device.

Low Cost: The Business Model would be initially by offering Femtocell

as a consumer purchase through mobile operators.


46

These advantages come from the reduced distance between terminal and FAP

at the operator’s side:

Higher consumers’ satisfaction

Lower CAPEX and OPEX

Network Offload

Figure 4.1: compared between femtocell networks and normal macro cells[19]


47

4.2.2 Wired broadband Femtocell Networks in LTE

We adopt the concept of Cognitive Femtocell Base Station (CFBS), which expands the

normal capabilities of LTE-A femtocells (Home eNodeB or HeNBs in 3GPP

nomenclature). Briefly, a CFBS is a simple low-power LTE access point connected with

the core network via wired broadband IP connection (e.g. DSL) in (figure4.2). In

addition, relevant CR functionalities are built in the CFBS, such as spectrum sensing,

interference management and efficient resource allocation. In the ambit of this work ,

figure 4.2 show the femtocells network in LTE.

Figure 4.2: LTE access point connected with the core network via wired broadband IP connection[19]

Figure 4.3: femtocell HeNB in LTE[19]


48

4.2.3 Femtocell as a Relay in LTE

Relay transmission can be seen as a kind of collaborative communications, in

which a relay station (RS) helps to forward user information from neighboring user

equipment (UE)/mobile station (MS) to a local eNode-B (eNB)/base station (BS). In

doing this, an RS can effectively extend the signal and service coverage of an eNB and

enhance the overall throughput performance of a wireless communication system. The

performance of relay transmissions is greatly affected by the collaborative strategy,

which includes the selection of relay types and relay partners (i.e., to decide when, how,

and with whom to collaborate).

Figure 4.4: Wired and wireless femtocells networks[19]

Relays that receive and retransmit the signals between base stations and mobiles can

be used to effectively increase throughput extend coverage of cellular networks.

Infrastructure relays do not need wired connection to network thereby offering savings in

operators’ backhaul costs as shown in figure 4.4 and figure 4.5. Mobile relays can be

used to build local area networks between mobile users under the umbrella of the wide

area cellular networks.

Advantages:

Increased Coverage: With multi-hop relays the macro cell coverage can be expanded to

the places where the base station cannot reach.

Increased Capacity: It creates hotspot solutions with reduced interference to increase the

overall capacity of the system.


49

Lower CAPEX & OPEX: Relays extending the coverage eliminates the need

of additional base stations and corresponding backhaul lines saving wireless operators

deployment costs and corresponding maintenance costs. The relays can be user

owned relays provided by operators and can be mounted on roof tops or indoors.

Figure 4.5: 2-tier network femtocells under umbrella macrocells[19]

Better Broadband Experience: Higher data rates are therefore now available as

users are close to the mini RF access point.

Reduced Transmission power: With Relays deployed there is a considerable

reduction in transmission power reducing co- channel interference and increased

capacity.

Faster Network rollout: The deployment of relays is simple and quickens the network

rollout process with a higher level of outdoor to indoor service and leading to use of

macro diversity increasing coverage quality with lesser fading and stronger signal

levels.


50

Figure 4.6: CR over LTE[19]

4.2.4 Backhaul Link Using TV White Space Frequency Bands in LTE In current mobile networks, there are many access points which have different

capabilities, e.g. they can support different coverage. These access points can be

connected with wireless backhaul link. In 3GPP LTE for example, the backhaul link

between the base station and relay is wireless. The wireless backhaul link operated on

the TVWS can obtain the following advantages: improve the access link capacity;

supporting the existing commercial terminals; providing a simple wireless environment;

providing a better channel quality because of the good propagation performance of the

TVWS bands; improve the capacity of the backhaul link; supporting the sensing

capability and scalable spectrum bands with lower cost.

• Relay node backhaul link

In this scenario figure 4.7, a city or a rural area is composed by many macro cells. In a

macro cell there are some hotspots or blind areas in which relays can provide the

coverage. The relay which has a fixed location e.g. on the roof, could be connected to the

macro cell BS with wireless backhaul link. According to the time and area in which

wireless backhaul link is operated, central control point can select a TVWS

spectrum for this backhaul link so that the TV service and other macro cells do not

suffer harmful interference .


51

Figure 4.7: Backhaul Link Using TV White Space Frequency Bands[19]

4.3 Interference Management Using Cognitive Radio in LTE

4.3.1 Interference in two-tier cellular networks

The mass deployment of femtocells gives rise to several technical challenges. One of the

major challenges is interference management between neighboring femtocells and between femtocell and macrocell (figure4.8). In general, two types of interferences that

occur in two-tier femtocell network architecture are as follows:

Co-tier interference: This type of interference occurs among network elements that

belong to the same tier in the network. In case of a femtocell network, co-tier interference

occurs between neighboring femtocells. For

example, a femtocell UE (aggressor) causes uplink co-tier interference to the neighboring

femtocell base stations (victims). On the other hand, a femtocell base station acts as a

source of downlink co-tier interference to the neighboring femtocell UEs. However, in

OFDMA systems, the co-tier uplink or downlink interference occurs only when the

aggressor (or the source of interference) and the victim use the same sub-channels.

Therefore, efficient allocation of sub-channels is required in OFDMA-based femtocell

networks to mitigate co-tier interference.

CHAPTER 4 | Cognitive radio Network over LTE

52

Cross-tier interference: This type of interference occurs among network elements that

belong to the different tiers of the network, i.e., interference between femtocells and

macrocells. For example, femtocell UEs and macrocell UEs (also referred to as MUEs)

act as a source of uplink cross-tier interference to the serving macrocell base station and

the nearby femtocells, respectively. On the other hand, the serving macrocell base station

and femtocells cause downlink cross-tier interference to the femtocell UEs and nearby

macrocell UEs, respectively. Again, in OFDMA-based femtocell networks, cross-tier

uplink or downlink interference occurs only when the same sub-channels are used by the

aggressor and the victim.

Figure 4.8: Interference scenarios in OFDMA-based femtocell networks


53

4.3.2 Femtocells Networks using Cognitive Radio in LTE

Merging cognitive principles in two-tier networks can permit the successful and cost-

effective deployment of femtocells .

Network Awareness

Agile Radio Resource Management

Orthogonal usage of spectrum amongst M-BS and FAPs can avoid cross-tier interference

Drawback: Notable reduction of overall network’s spectral efficiency, co-tier

interference is still present.

A cognitive FAP, based on the sensing outcome and the transmission strategy,

dynamically assigns available channels to contending users by attempting to maximize a

utility function. This function is often made up of two components: a reward and a price,

The reward describes the gain achieved by a certain UE when choosing a particular

channel (i.e., the data rate) The price represents the cost that this choice implies for the

overall network (such as the interference).

Figure 4.8: Agile Radio Resource Management


54

Spectrum Sharing

In two-tier network scenarios we can identify two classes of UEs:

M-UEs that generally experience low performance due to propagation losses and

interference

F-UEs that likely experience high quality signal due to the limited distance

between the AP and the user-terminal

The macro cell network has not specific functionalities to coexist with the femtocell

network .Femtocells have to avoid interference with both M-UEs and neighboring FAPs

,The spectrum sharing functionalities face the problem of coexistence between

heterogeneous users accessing the radio resource. Three different cognitive transmission

access paradigms are presented in literature: underlay, overlay and interweave.

CHAPTER 5| Deployment Scenarios for Cognitive Radio in LTE Network

55

Chapter 5

Deployment Scenarios for Cognitive Radio

in LTE Cellular Networks

In this chapter we will introduce our procedures in terms of the used equipment,

assumption, our key performance indicators (KPIs), and practical results and how to

make a link between practical results and theoretical points that were introduced in

chapter 4

5.1 project assumption We made indoor survey for the network planning department in the headquarter of

National Telecommunication Institute (NTI) in Nasr city for band GSM (1800) that is

known as DCS band in order to find holes to apply LTE technology and using CR to

extend its band . We got practical results by using equipment which will be discussed

later. We assumed the power level threshold -80 dBm to get holes according to the DCS

sensitivity.

Figure 5.1: Simulation for wireless department from AutoCAD and Photoshop software


56

5.2 Equipments used in project and measurement procedures

5.2.1 Laser Distance Meter

This device has the capability to measure the distance from1cm to 100m with accuracy

+/-1cm.We used this device to get the dimension of our department as we made an indoor

scan as mentioned before.

5.2.1.1 Measurement criteria

This device has a laser beam which used in pointing to the targeted point and by pressing

on the device button; the distance between device to the point will be calculated and

appeared on the device screen as shown in figure (5.2)

Figure 5.2: laser meter device and how to use it

We got the dimensions of all department rooms then using AutoCAD to simulate the

department as shown previous in figure (5.1)

5.2.2 EME SPY 140

EME Spy is a light and portable RF safety personal monitoring device that performs

continuous measurements of the human exposure level to electromagnetic fields as

shown in figure (5.3).


57

Figure5.3: EME spy 140

And this device gives power density measurements displayed in mw/ or W/m…etc.

The purpose of using this device is to know if this area covered by DCS technology (our

targeted band is DCS band).

5.2.2.1 Measurement criteria

We adjusted the device to take a max power sample every four seconds and we choose to

take twenty two points for measurements. We take two points for each lab and head of

the department office; three points for the room 122, and room 124; one point for room

119, room 123, and room 120. Finally we took five points for the hall of the department

Totally we had twenty two points and each point takes two minutes so our measurement

time would be forty four minutes, but we adjusted the device to make measurements for

one hour (60 min) to be in the safe zone as we took into consideration the time to go from

one room to another one so we left one min as a spare. In the adjustment of the device the

Period was four seconds and cycle duration was 60 min. After we finished our

measurement time, we connected the EME SPY 140 to the laptop to present the results as

shown in figure (5.4)


58

Figure 5.4: connecting between EME SPY and laptop

This is a graph for all technologies after finishing the measurement procedures as shown

in Figure (5.5)

Figure 5.5: graph shows all technologies after measurement process


59

This device as shown in figure 5.5 shows FM, TV, GSM, DCS, UMTS, Wi-Fi, Wi-

Max...etc. But we selected GSM (1800) as shown in figure (5.6)

Figure 5.6: shows UL and DL for only DCS technology

Also as shown in figure (5.7) example for the average power density per 10 samples

Figure 5.7: average power destiny per 10 samples


60

This is example of excel sheet as shown in figure (5.8)

Figure 5.8: example for some samples

In this excel sheet we made a conversion from mW/ to dBm and the converting

parameter was the effective area as mentioned before this device gives the power density

measurements displayed in mW/

,

F as 1800 MHZ as our target is the DCS band (1800MHZ band)

= 1.4 dB from the device sheet

So = 28.73

Then = 28.73 *


61

Figure 5.9: example of excel sheet after conversion

And these are some examples for DL and UL resulting from EME SPY measurements

Figure (5.10) and (5.11) show UL and DL for the head of the department office

Figure 5.10: downlink of the head of the department office


62

Figure 5.11: uplink of the headquarter office

And these two figures (5.12) and (5.13) show the DL and UL graphs for our session room

Figure 5.12: uplink graph for our session room.

Fig 5.13: downlink graph for our session room.


63

Then by using excel sheet as shown in figure (5.9) and applying these results on the

figure (5.1) that simulates the department area we get figure (5.14) and (5.15)

Figure5.14: power levels on the measured points

Figure 5.15: color code for the power levels measured in dBm


64

5.2.3 FSH Handheld Spectrum Analyzer

The FSH is a handy, robust and portable spectrum analyzer for rapid and cost-effective

signal investigations as shown in figure (5.16). The FSH can handle anything from

installation or maintenance of mobile radio base stations, through on-site fault location in

RF cables to simple lab applications.

Figure 5.16: FSH device

FSH characteristics as follows:

Frequency range 100 kHz to 6 GHz

Resolution bandwidths 100 Hz to 1 MHz

Video Bandwidth - 10 Hz to 1 MHz

Wide range of detectors: sample, max/min peak, auto peak, RMS.

5.2.3.1 Measurement criteria We adjusted the resolution Band Width (RBW) to 100KHZ and this is the min value can

resolved by the device as shown in figure (5.17) and this should be 1% to 4% of used

channel.

Figure 5.17: The same signal with different RBW


65

The smaller RBW, on the right, has much finer resolution which allows the sidebands to

be visible so we adjust the RBW to be 100KHZ Then we adjusted the video bandwidth to

be 300KHZ and this is what would be displayed on device screen; the manual sweep time

was adjusted to be one second and this means the device would record a value each one

sec; then we adjusted it to take ten values and stop to save the average of them Then we

started to measure in each room at the wireless department to scan the targeted band

(1700 to 1900 MHZ) but in order to collect much data we make 1 MHZ step while

scanning in the hall so we collect 200 values * five points then we get 1000 values in

only the hall space after that we made the step to be 10 MHZ so we collect 20 values in

each lab room. In each step were changing the center frequency to be 1705-1715-1725-

1735...etc to 1895 as the step was 10 MHZ And of course in each time as soon as we

finish a specific room we connect the device to the laptop to get the results and save it as

shown in figure(5.18)

Figure 5.18: FSH results importing results


66

And these are some examples for the results as shown in Figure (5.19) and (5.20)

Figure 5.19: FSH sample results 1


67

Figure 5.20: FSH sample results 2

After getting these results we began to apply our assumption. It’s worth mentioning that

the signal power levels were in range almost -60dBm to -90dBm

These are the resulting holes in each room as shown in table (5.1)


68

Table 5.1: holes in each office

Head of the

department

Room 120 Room 123 Room 124 Room 122 Room 119 Room 125 Room 121 Room

126

1750 – 1760

1750 – 1760 1750 –

1760

1760 – 1770 1760 – 1770 1760 – 1770

1760 – 1770 1760 – 1770 1760 – 1770 1760 – 1770 1760 – 1770

1770 – 1780 1770 – 1780 1770 – 1780

1770 – 1780 1770 – 1780 1770 – 1780 1770 – 1780 1770 – 1780 1770 –

1780

1780 – 1790 1780 – 1790 1780 – 1790

1780 – 1790 1780 – 1790 1780 – 1790 1780 – 1790 1780 – 1790 1780 –

1790

1790 – 1800 1790 – 1800 1790 – 1800

1790 – 1800 1790 – 1800 1890 – 1800 1790 – 1800 1790 – 1800 1790 –

1800

1820 – 1830 1820 – 1830

1820 – 1830 1820 – 1830 1820 – 1830 1820 –

1830

1830 – 1840 1830 -1840 1830 – 1840

1830 – 1840 1830 – 1840 1830 – 1840 1830 – 1840 1830 – 1840 1830 –

1840

1840 – 1850 1840 – 1850

1860 – 1870 1860 – 1870 1860 – 1870 1860 – 1870

1860 – 1870 1860 – 1870 1860 – 1870 1860 – 1870 1860 –

1870

1870 – 1880 1870 – 1880 1870 – 1880 1870 – 1880

1870 – 1880 1870 – 1880 1870 – 1880 1870 – 1880 1870 –

1880

1880 – 1890 1880 – 1890 1880 – 1890

1880 – 1890 1880 – 1890 1880 - 1890 1880 - 1890 1880 –

1890

1890 – 1900 1890 – 1900 1890 – 1900 1890 – 1900

1890 – 1900 1890 – 1900 1890 – 1900 1890 – 1900 1890 –

1900


69

Thus, we took the common holes between these rooms and considered them as holes in

DCS band as shown in table 5.2

Table 5.2: The common holes in the department

1

The band from 1880 to 1900 MHZ was found as a hole at the first time of measurement but at the second time of

measurement to ensure having accurate results we found this band having signal with high power strength as shown in figure

(5.21)

1770 – 1780

1780 – 1790

1790 – 1800

1830 – 1840

1860 – 1870

1870 – 1880

1880-1900 1


70

Figure5.21: detected signal in band 1880 to 1900 MHZ

5.4 Cognitive Radio in operators Bands

LTE operator bands are (1770 – 1880), (1830-1840) and (1860 – 1880)

MHZ, in addition to that operator wants to act as a secondary users in

other operators bands. By applying cognitive radio technology; it can

scan their bands to get all available channels then select the appropriate

channel for the required communication taking into consideration the

quality of service requirement. So here in our work; we can use other

LTE operators’ bands, which are determined by group (A) & (B), as a

secondary band.


71

These bands are as follow

First scanned band (700 to 900)MHZ

1. From 700 to 790 MHZ

2. From 960 to 1000 MHZ, but this band not available for LTE

Technology according to LTE frequency band allocation.

Second band (1900 to 2100)MHZ

1. From 1900 to 1931 MHZ

2. From 1947 to 1970 MHZ

3. From 1980 to 2100 MHZ

Until now, there is no operator takes a license for any band to apply LTE

technology. So, after (NTRA) auction in 2016, each operator can take a

license for specific band. Then, spectrum broker will manage and trade

those licensed bands between operators and each other for cognitive

radio.

CHAPTER 6| Conclusion and Future Work

72

Chapter 6

Conclusion and Future Work

Radio spectrum is a finite resource. There are many spectrum bands which already suffer

from congestion, while at the same time there are other spectrum bands that are highly

underutilized. Improved spectrum utilization is essential to allow for future wireless

services to satisfy the increasing user demand for wireless capacity, coverage and quality

of service. In an attempt to improve the utilization of currently underutilized spectrum

bands, there is a growing regulatory trend to allow for license- exempt users to gain

opportunistic access to spectrum that is in underutilized licensed spectrum bands. An

opportunistic user must act as a cognitive radio in order to avoid interference with

primary/licensed users. It should also cooperate fairly with other opportunistic users (also

known as secondary/license exempt/ cognitive users)

The common holes can be used for applying LTE technology (Table 5.1), and the other

holes can be used for CR in many aspects such as secondary user for other operators or

CR femto-cells as explained in chapter 4. It is worth mentioning that available spectrum

opportunities are found in time and freq domains and this explains the meaning of finding

holes in band in a specific time but it’s not found in another time. CR system based on

location as we explained in chapter 3. The list of available channels are prepared by Geo-

location data base is dependent on the location of the secondary user , also we can use the

TV band for secondary users.

Like that Wireless operators can improve their own spectrum efficiency by using

cognitive radio technology as a sample for applying green network; there are other

brunched technologies for that. Green networking is the practice of selecting energy-

efficient networking technologies and products, and minimizing resource use whenever

possible.

Network Energy Efficiency.

Our principal focus and objective is to continually evaluate all available energy-related

opportunities and technologies and optimize operating cost reductions for our clients by

employing an integrated, comprehensive and multi-pronged approach. All that for

environment safety from network equipment radiations.

74

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