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Document: FP7-ICT-2011-8-318115-CROWD/D 1.2Date: March 31, 2014 Diss. level: PUStatus: Submitted Version: 1.0, 14:09

Document Properties

Document Number: D 1.2

Document Title:

Business Cases, Economic and Migration Scenarios Analysis

Document Editor: Ivan Gouletquer (Alcatel Lucent International)

Authors:

Ivan Gouletquer (Alcatel Lucent International)Arianna Morelli (INCS)Christian Vitale (IMDEA)Vincenzo Sciancalepore (IMDEA)Vincenzo Mancuso (IMDEA)Martin Draxler (UPB)Erick Bizouarn (ALBLF)

Target Dissemination Level: PU

Status of the Document: Submitted

Version: 1.0, 14:09

Production Properties:

Reviewers:

Arianna Morelli (INCS)Erick Bizouarn (ALBLF)Antonio de la Oliva (UC3M)Vincenzo Mancuso (IMDEA)

Document History:

Revision Date Issued by Description

v0.1 20/02/2014 Ivan Gouletquer Draftv0.2 18/03/2014 Ivan Gouletquer Initial Releasev1.0 31/03/2014 Ivan Gouletquer Submitted Release

Disclaimer:This document has been produced in the context of the CROWD Project. The research leading to these results hasreceived funding from the European Community’s Seventh Framework Programme (FP7) under grant agreementn◦ 318115.All information in this document is provided “as is” and no guarantee or warranty is given that the informationis fit for any particular purpose. The user thereof uses the information at its sole risk and liability.For the avoidance of all doubts, the European Commission has no liability in respect of this document, which ismerely representing the authors view.

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Abstract:The innovative concept of the CROWD architecture has been studied from a business point ofview, investigating the potential economic improvement due to CROWD.The study has been conducted leveraging Alcatel Lucent Bell Labs HetNet Strategy Optimizer(HSO) business modeling process and tools, developing a comparative Total Cost Ownership(TCO) analysis where Present Mode of Operation (PMO) is defined by a typical ReferenceNetwork Model (LTE Macro/Small Cells + WiFi APs) and Future Mode of Operation (FMO)integrates technical and economical gains brought by CROWD approach.Reference Network Model was defined leveraging CROWD project Preliminary Architecturedocument reference scenarios. Five typical configurations were considered, corresponding tosituations where usage is either primarily residential, business related, that of a shopping mall,university campus or with higher mobility.Then five CROWD themes/features were selected as contributing either throughput, powerconsumption or efficiency gains:

• ABSF Control Application with SDN

• Opportunistic WiFi-assisted Cluster Relay

• Load Balancing through offloading

• Energy Saving

• Dynamic Network (re-)configuration

As a result, Business Modeling simulations indicate that CROWD approach could deliver TCOgains ranging from 20% up to 44% depending on the considered situation.

Keywords:

economic analysis, HetNet, dense mobile networks, distributed mobility management

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Document: FP7-ICT-2011-8-318115-CROWD/D 1.2Date: March 31, 2014 Security: PUStatus: Submitted Version: 1.0, 14:09

Contents

List of Figures iii

List of Tables vi

List of Project Partners viii

List of Acronyms x

1 Study Objective and Methodology 1

2 Present Mode of Operation - Reference Network 32.1 Network Element Types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32.2 Network Elements Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2.2.1 Macro Network Elements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32.2.2 LTE Small Cells and Wi-Fi Network Elements . . . . . . . . . . . . . . . . . 42.2.3 Network Elements Cost Structure . . . . . . . . . . . . . . . . . . . . . . . . 42.2.4 Spectrum Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42.2.5 Subscriber Base and Traffic Forecast . . . . . . . . . . . . . . . . . . . . . . . 52.2.6 User Distribution as a Function of Mobility Model . . . . . . . . . . . . . . . 5

3 Future Mode of Operation - Contributing CROWD Features 73.1 ABSF Control Application with SDN . . . . . . . . . . . . . . . . . . . . . . . . . . . 73.2 Opportunistic Wi-Fi-assisted Cluster Relay . . . . . . . . . . . . . . . . . . . . . . . 83.3 Load Balancing through Offloading . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83.4 Energy Saving . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93.5 Dynamic Network reconfiguration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

4 Results 114.1 Shopping Mall Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114.2 Business Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124.3 University Campus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134.4 Residential Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144.5 Way to School . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

5 Conclusion 17

Bibliography 19

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List of Figures

1.1 Cost Flow Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

2.1 Macro and Small Cell Cost Breakdown . . . . . . . . . . . . . . . . . . . . . . . . . . 4

3.1 Performance comparison of legacy frequency-reuse schemes (FR1, FR2, FR3), softfractional frequency-reuse schemes (“Enhancing Cell Edge performance”, ECE [1]and CROWD solution based on ABSF pattern optimization using the Base StationBlanking (BSB) algorithm defined in [2]. The figure shows the time needed tocomplete a downloading task (content distribution to a group of mobile users) usingdifferent schemes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

3.2 Architecture for cluster-based opportunistic access to LTE and ad-hoc WiFi re-sources. Cellular resources are allocated per cluster, and the scheduling in eachcluster uses a channel-opportunistic approach. WiFi is used to relay packets be-tween mobile nodes. [3] [4] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

3.3 eNodeB power consumption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

4.1 Shopping mall area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114.2 Business area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124.3 University campus area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134.4 Residential area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144.5 Way to school area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

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List of Tables

2.1 User distribution at busy hour (Busy Hour (BH)) . . . . . . . . . . . . . . . . . . . . 52.2 Traffic profile per device breakdown . . . . . . . . . . . . . . . . . . . . . . . . . . . 52.3 Busy Hour Traffic per subscriber (Voice) . . . . . . . . . . . . . . . . . . . . . . . . . 52.4 Busy Hour Traffic per subscriber (Data) . . . . . . . . . . . . . . . . . . . . . . . . . 52.5 Type of subscriber per configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

4.1 Shopping mall results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114.2 Business area result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124.3 University campus result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134.4 Residential area result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144.5 Way to school result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

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List of Project Partners

Name Acronym Country

Intecs S.p.A. (coordinator) INCS Italy

Alcatel-Lucent International ALI France

Alcatel-Lucent Bell Labs France ALBLF France

Fundacion IMDEA Networks IMDEA Spain

Signalion Gmbh SIG Germany

Universidad Carlos III de Madrid UC3M Spain

Universitaet Paderborn UPB Germany

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List of Acronyms

ABSF Almost Blank Sub-Frame

AP Access Point

BH Busy Hour

BSB Base Station Blanking

CAPEX Capital Expenditure

CROWD Connectivity management for eneRgy Optimised Wireless Dense networks

DL DownLink

FMO Future Mode of Operation

GSM Global System for Mobile communications

HSPA High Speed Packet Access

HSO HetNet Strategy Optimizer

LTE Long Term Evolution

MIMO Multiple Input Multiple Output

OPEX OPerational Expenditure

PMO Present Mode of Operation

QoE Quality of Experience

RAN Radio Access Network

SDN Software Defined Network

SINR Signal to Interference plus Noise Ratio

TCO Total Cost Ownership

UL UpLink

UMTS Universal Mobile Telecommunication System

WiFi Wireless Fidelity

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1 Study Objective and Methodology

The objective of the study was to estimate the potential TCO benefits that can be expected fromCROWD approach towards Self-optimizing and Mobility-optimized Dense Networks.

The study has been conducted leveraging Alcatel Lucent Bell Labs HetNet Strategy Optimizer(HSO) business modeling process and tools, developing a comparative Total Cost of OwnershipAnalysis where PMO is defined by a typical Reference Network Model (LTE Macro/Small Cells +WiFi APs) and FMO integrates technical and economical gains brought by CROWD approach.

The main flow of the model is described in figure below:

Figure 1.1: Cost Flow Model

To constitute the Reference Network, a series of inputs were defined concerning notably:

• Candidate Network Technologies (Global System for Mobile communications (GSM), Univer-sal Mobile Telecommunication System (UMTS), LTE, Small Cells and WiFi access points)

• Current and future spectrum assets

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• User base, traffic profiles, and related evolution over time

• Performance and cost for all considered network elements

• Probability for the end-user to find himself in various situations during the busy hour: athome, at the office, within a public hotspot or on the move

HetNet Strategy Optimizer then simultaneously solves the whole system by optimizing the ob-jective function which can be either the discounted cash cost of the network and the user incurredsubsidization, or the profitability, i.e. minimizing cost and/or maximizing profit. The main con-straint is to adjust service supply on the network side to match the demand side generated by usersand traffic forecast.

For each identified solution, outputs include the Total Cost of Ownership as well as a detailedbill of quantities of the various network elements required to match the need.

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2 Present Mode of Operation - ReferenceNetwork

Based on CROWD project preliminary architecture design document Scenario 1 & 2 story lines [5],the following configurations were modeled:

• Shopping Mall area where usage primarily corresponds but is not restricted - to a commercialarea

• Residential area where traffic is primarily driven by usage at home

• Business area where traffic is primarily driven by usage at the office

• University Campus area where usage primarily corresponds to a medium to low mobilityenvironment.

• Way to school: a zone where traffic is more driven by higher mobility usage

2.1 Network Element Types

For each of the five considered configurations, we considered a zone where three multi-standard(GSM+UMTS+LTE) macro base stations are deployed.

As a complement, four types of LTE Small Cells (Residential, Enterprise, Indoor/Outdoor PublicHotSpot Metro Cell) as well as four types of WiFi Access Point (AP)s (Residential, Enterprise,Indoor/Outdoor Public HotSpot Access Point) are also candidate for deployment.

2.2 Network Elements Performance

2.2.1 Macro Network Elements

For LTE Macro Capacity, considering Multiple Input Multiple Output (MIMO) 2x2, the assumedaverage aggregate cell throughput is 7500 kbps i.e. 1.5 bps/Hz per carrier Data DownLink (DL).

For 3G Macro Capacity, considering High Speed Packet Access (HSPA) MIMO 1x2, the assumedaverage aggregate cell throughput is 3710 kbps i.e. 0.742 bps/Hz per carrier Data DL.

Note: if considering HSPA MIMO 2x2, Data DL would be ≈ 1 bps/Hz and if considering 64QAM,Data DL would be ≈ 0.9 bps/Hz.

Considered nominal utilization ratio for macro network elements is 70% (i.e. 30% headroom),threshold beyond which one can consider that macro begins to saturate; this also allows to accom-modate traffic burstiness.

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2.2.2 LTE Small Cells and Wi-Fi Network Elements

For LTE Small Cells, considered spectral efficiencies are 2.22 bps/Hz for residential enterprise smallcells, 3.375 bps/Hz for Indoor Metro Cell: 3.375 bps/Hz and 1.9 bps/Hz for Outdoor Metro Cell:1.9 bps/Hz. A 100% utilization in baseline is assumed for these network elements.

For WiFi access points of all types, a 2.97 bps/Hz spectral efficiency is considered.

2.2.3 Network Elements Cost Structure

Below is an indicative cost breakdown corresponding to Macro and Small Cell deployment schemes.

Figure 2.1: Macro and Small Cell Cost Breakdown

2.2.4 Spectrum Assumptions

Spectrum assets are assumed to be constant over the ten year study period and are comprised of:

• 2x10 MHz in 800 MHz

• 2x 10 MHz in 900 MHz




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2.2.5 Subscriber Base and Traffic Forecast

We considered a population of about 16 000 people, moderately increasing over the ten year studyperiod.

The considered device breakdown, traffic profiles and their evolution are as provided below:

2.2.6 User Distribution as a Function of Mobility Model

Depending on the considered configuration, the end-user might find him/herself in different sit-uations during the busy hour. Below are the respective breakdown assumptions reflecting theprobability of the end-user to be either at home, at the office, within reach of an indoor or out-door hotspot, or on the move, for each of the modeled configurations (Shopping Mall, Residential,Business, Campus and Way to School).

Table 2.1: User distribution at busy hour (BH)

Number of users in BH 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023

Urban 15.989 16.026 16.118 16.149 16.168 16.208 16.264 16.309 16.353 16.397 16.442Zone 1 15.989 16.026 16.118 16.149 16.168 16.208 16.264 16.309 16.353 16.397 16.442Total 15.989 16.026 16.118 16.149 16.168 16.208 16.264 16.309 16.353 16.397 16.442

Table 2.2: Traffic profile per device breakdown

Subscriber Morphology Breakdown 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023

Urban-Zone1Handset 44% 42% 40% 38% 36% 34% 32% 30% 28% 26% 24%Smartphone 41% 42% 43% 44% 45% 46% 47% 48% 49% 50% 51%Dongle 15% 16% 17% 18% 19% 20% 21% 22% 23% 24% 25%Total 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Table 2.3: Busy Hour Traffic per subscriber (Voice)

BH for Voice (Erlang) 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023

Handset 0.006 0.007 0.007 0.007 0.008 0.008 0.009 0.010 0.011 0.012 0.013Smartphone 0.012 0.012 0.013 0.014 0.015 0.016 0.017 0.019 0.020 0.022 0.024Dongle - - - - - - - - - - -

Table 2.4: Busy Hour Traffic per subscriber (Data)

BH for Data (Kb/s per device) 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023

Handset 0.009 0.011 0.012 0.014 0.016 0.019 0.021 0.024 0.028 0.032 0.037Smartphone 0.631 0.730 0.867 1.122 1.671 2.430 3.505 4.549 5.121 5.977 7.044Dongle 8.423 10.436 13.787 19.490 30.305 42.364 56.684 74.267 86.550 96.777 108.325

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Table 2.5: Type of subscriber per configuration

% of subscribers presence per zone Shopping Residential Business Campus Way to school

At home 5% 50% 5% 5% 10% at BH

At Office 5% 5% 50% 5% 10% at BH

In indoor hotspots 35% 15% 15% 50% 10% at BH

In outdoor hotspots 50% 20% 20% 35% 20% at BH

On the move 5% 10% 10% 5% 50% at BH

Total 100% 100% 100% 100% 100%

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3 Future Mode of Operation - ContributingCROWD Features

Five CROWD themes/features were selected as contributing either throughput, power consumptionor efficiency gains.

3.1 ABSF Control Application with SDN

ABSF Control Application with SDN is a new control application that allows to control the ABSFpatterns of neighbor base stations in LTE networks using the SDN paradigm.

Figure 3.1: Performance comparison of legacy frequency-reuse schemes (FR1, FR2, FR3), softfractional frequency-reuse schemes (“Enhancing Cell Edge performance”, ECE [1] andCROWD solution based on ABSF pattern optimization using the BSB algorithm de-fined in [2]. The figure shows the time needed to complete a downloading task (contentdistribution to a group of mobile users) using different schemes.

The benefits in the context of dense networks include:

• Minimizing interference and enabling frequency-reuse-1

• Reducing the activity of base station (increasing spectral efficiency and user-perceived Qualityof Experience (QoE))

• Potential power consumption reduction (at both base station and user side)

The ABSF control application reduces content distribution time by a factor 3 or more as comparedto legacy schemes frequency-reuse-3 or 5.

Aggregate cell throughput increases from 39.84Mbps for frequency-reuse-3 to ≈ 82Mbps for theABSF CROWD proposal, (i.e. about +103% aggregate throughput gain). This gain isreferred to Macro cell deployments in the urban environment (7 Base stations and 750 users).

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3.2 Opportunistic Wi-Fi-assisted Cluster Relay

This feature is a new opportunistic packet relay scheme that allows to use LTE resources efficientlyand in a fair way.

Figure 3.2: Architecture for cluster-based opportunistic access to LTE and ad-hoc WiFi resources.Cellular resources are allocated per cluster, and the scheduling in each cluster uses achannel-opportunistic approach. WiFi is used to relay packets between mobile nodes.[3] [4]


• maximizing throughputs

• boosting throughput fairness

• boosting energy efficiency (lower user costs and lower opex)

Throughput is increased by up to 50% and fairness close to 1, with doubled energy efficiency.

Aggregate cell throughput increases from ≈ 40Mbps for Round Robin to ≈ 75Mbps for theCROWD proposal (CL(WRR)), i.e. representing a +87.5% aggregate throughput gain. Ifcompared to ideal proportional fair (PF) schedulers, the gain with CROWD is ≈ 15Mbps per cell(+25%).

3.3 Load Balancing through Offloading

To face the exponential growth of traffic demand and quality of service, increasing the number ofpoint of access in Radio Access Network (RAN) can be a solution. Together with it, an intelli-gent allocation of the resource is suggested to improve capacity and minimize costs. Among thetechniques:

• ABS to reduce interference in UpLink (UL) and DL

• Load balancing: evaluate the best eNB to serve a UE. The best means not only the best Signalto Interference plus Noise Ratio (SINR) but the best considering the load and performanceof the eNB in the district, the total amount of traffic, the energy saving aspect

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• Offloading: offloading the cellular network switching the traffic on WiFi (if possible). Thejust released can be re-located or not


• SDN: CLC for intelligent allocation of the resources

• Spectral efficiency improvements

• OPerational Expenditure (OPEX) and Capital Expenditure (CAPEX) reduction by exploit-ing the WiFi technology to offload LTE network, energy saving switching off the eNB whoseresources are not used.

3.4 Energy Saving

Increasing the number of point of access or wireless backhaul means increasing the energy con-sumption. In some cases this can be reduced if the load balancing works correctly. The eNBswhose resources are not used, can be switched off.

Tests made by INCS show that changing the algorithm according to which the UEs attached theeNB the energy consumption change.

Figure 3.3: eNodeB power consumption

In dense scenarios (100 eNB in a 200x100 meters region) having low load (just 20 active UE),the instantaneous power consumption of the cellular access network, i.e. considering both the en-ergy consumed by the eNB and the backhaul, passes from around 1100 W on average (when usingtraditional association techniques) to 800 W on average (when using the CROWD proposal). Thismeans a 27.3% reduction of the total power consumed.

When instead we deal with high load scenarios (200 UE in the same conditions described before),less components can be switched off by means of an accurate UE/eNB association, and the powerconsumption of the cellular access network passes from 1650W on average to 1300W on average.This means that the reduction of the power consumed is 21.1%.

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3.5 Dynamic Network reconfiguration

This feature allows to:

• Operate the backhaul network in a traffic/demand-proportional manner to:

– Adapt to high demand to serve more users

– Adapt to low demand to save energy

• Optimize controller placement to:

– Avoid unnecessary additional controllers

– Save computational power and energy

According to the most recent results we have an approach to assign wavelengths in an opticalbackhaul network in a demand-proportional manner. As the gain here is a tradeoff and not a fixednumber, it is not trivial to quantify a precise benefit.

Given the choice to assign X optical wavelengths per link, the traditional approach would be toassign all of them, regardless of the need in capacity. Thus in situations with a low number of users,all backhaul equipment has to be running and it wastes energy. In CROWD we can dynamicallyand precisely adapt the number of assigned wavelengths, so unneeded equipment can be switchedoff.

Given a maximum limit of resources a network operator would like to spend, our approach inCROWD is able to serve more users, as resources can be assigned where required instead of assign-ing them statically.

Current evaluation is based on snapshots, but the plan is to extend the approach to assign re-source over time. Then an energy or cost model can be added to the evaluation so as to deriveconcrete figures for the expected gain.

As a result of the above, two main contributors were considered for businessmodeling purposes: Doubling of the spectral efficiency for Macro and Small Cells25% savings on Macro and Small Cells power consumption.

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4 Results

4.1 Shopping Mall Area

Figure 4.1: Shopping mall area

In this configuration, the implemented spectral efficiency gain allows to cut down CAPEX byone third, mainly due to a lessened requirement notably as regards Outdoor solutions (be themSmall Cell Metros or WiFi APs).

Moreover, this reduction of the overall required bill of quantity, combined with power consump-tion gains, significantly reduces OPEX globally.

Table 4.1: Shopping mall results

Shopping Mall (over ten years) PMO Cost (ke) FMO Cost (ke) Delta Bill of Quantities PMO FMO

CAPEX Macro 188 148 Macro 3 3OPEX Macro 61 49 SC Residential 0 0CAPEX Small Cells 35 20 SC Entreprise 0 0OPEX Small Cells 30 13 SC Indoor Metro 3 4CAPEX WiFi 48 13 SC Outdoor Metro 3 0OPEX WiFi 99 13 Wifi Residential 103 103Total CAPEX 271 180 -34% Wifi Entreprise 13 13Total OPEX 189 76 -60% WiFi Indoor Metro 11 6Total Cost of Ownership 460 256 -44% Wifi Outdoor Metro 14 0

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4.2 Business Area

Figure 4.2: Business area

In this configuration, the implemented spectral efficiency gains allow to avoid residential WiFideployments. This, combined with the power consumption gains, drives interesting TCO gains,though less spectacular as previous case, mainly due to the fact that in such conditions, WiFiHetNet components are privileged rather than Small Cells. And since the implemented gainsconcern Small Cells rather than WiFi APs, the impact is thus lesser overall.

Table 4.2: Business area result

Business Area (over ten years) PMO Cost (ke) FMO Cost (ke) Delta Bill of Quantities PMO FMO


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4.3 University Campus

Figure 4.3: University campus area

In this configuration, the implemented spectral efficiency gain allows to cut CAPEX down by onethird, mainly due to a lessened requirement notably as regards Indoor solutions (be them Small CellMetros or WiFi APs). Moreover, this reduction of the overall required bill of quantity, combinedwith power consumption gains, significantly reduces OPEX globally.

Table 4.3: University campus result

Campus (over ten years) PMO Cost (ke) FMO Cost (ke) Delta Bill of Quantities PMO FMO


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4.4 Residential Area

Figure 4.4: Residential area

In this configuration, the implemented spectral efficiency gains allow to partially reduce theneed for residential WiFi deployments. This, combined with the power consumption gains, drivesinteresting TCO gains, though less spectacular other cases, mainly due to the fact that in suchconditions, WiFi HetNet components are privileged rather than Small Cells. And since the imple-mented gains concern Small Cells rather than WiFi APs, the impact is thus lesser overall.

Table 4.4: Residential area result

Residential (over ten years) PMO Cost (ke) FMO Cost (ke) Delta Bill of Quantities PMO FMO


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4.5 Way to School

Figure 4.5: Way to school area

In this configuration, the implemented spectral efficiency gains allow to partially reduce theneed for enterprise WiFi deployments. This, combined with the power consumption gains, drivesinteresting TCO gains, though the least spectacular of all cases, mainly due to the fact that HetNettype of solutions are less adequate for such higher mobility traffic conditions.

Table 4.5: Way to school result

Way to school (over ten years) PMO Cost (ke) FMO Cost (ke) Delta Bill of Quantities PMO FMO


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5 Conclusion

Overall, CROWD approach spectral efficiency gains as well as power consumption reductions defi-nitely allow to envisage significant TCO benefits over time. Exact impact will however depend onthe precise configurations on which they are applied:

These initial results indicate that CROWD benefits are all the more impactful that the traffic isconcentrated around public hotspots (e.g. Shopping Mall, Campus). On the other hand, thoughstill significant, impact is lesser for Residential and/or Business contexts where required HetNetsolutions are less expensive.

Finally, higher mobility situations obviously reduce the impact and relevance of CROWD benefits.

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Bibliography

[1] M. Rahman and H. Yanikomeroglu. Enhancing cell-edge performance: a downlink dynamicinterference avoidance scheme with intercell coordination. Wireless Communications, IEEETransactions on, 9(4):1414 –1425, apr 2010.

[2] V. Sciancalepore, V. Mancuso, A. Banks, S. Zaks, and A. Capone. Interference CoordinationStrategy for Content Update Dissemination in LTE-A. In IEEE INFOCOM, 2014.

[3] A. Assadi and V. Mancuso. ”On the Compound Impact of Opportunistic Scheduling and D2DCommunications in Cellular Networks”. In ACM/IEEE MSWiM13, Barcelona, Spain, nov 2013.

[4] A. Assadi and V. Mancuso. ”Wifi Direct and LTE D2D in Action”. In Wireless Days13,Valencia, Spain, nov 2013.

[5] C. Cicconetti, S. Auroux, E. Bizouarn, A. de la Oliva, M. Draexler, V. Mancuso, A. Morelli,R. Gupta, I. Sanchez, V. Sciancalepore, P. Seite, and P. Serrano. Preliminary architecturedesign. Project deliverable D1.1, CROWD, 2013.

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