Andr¶e Amorim de Planiﬂca»c~ao de Redes Wimax Ponto ...

Universidade de AveiroDepartamento deElectronica, Telecomunicacoes e Informatica,

2009

Andre Amorim deFaria Cardote

Planificacao de Redes Wimax Ponto-Multiponto eem Malha

Universidade de AveiroDepartamento deElectronica, Telecomunicacoes e Informatica,

2009

Andre Amorim deFaria Cardote

WiMAX Point-to-Multipoint and Mesh modeNetwork Planning

Dissertacao apresentada a Universidade de Aveiro para cumprimento dosrequesitos necessarios a obtencao do grau de Mestre em EngenhariaElectronica e de Telecomunicacoes, realizada sob a orientacao cientıficada Prof. Dra. Susana Sargento, Professora auxiliar do Departamento deElectronica, Telecomunicacoes e Informatica da Universidade de Aveiro edo Eng.o Sergio Pires, a data de inıcio responsavel pelo departamento deinvestigacao da Celfinet e actualmente Colaborador do Institudo de Teleco-municacoes - Polo de Aveiro.

A memoria

de

Goncalo Roberto Cruz

o juri / the jury

presidente / president Prof. Dr. Atılio Manuel Silva GameiroProfessor associado do Departamento de Electronica, Telecomunicacoes e In-

formatica da Universidade de Aveiro

vogais / examiners committee Prof. Dra. Susana SargentoProfessora auxiliar do Departamento de Electronica, Telecomunicacoes e In-

formatica da Universidade de Aveiro (orientador)

Prof. Dr. Manuel Alberto Pereira RicardoProfessor Associado da Faculdade de Engenharia da Universidade do Porto (ar-

guente)

agradecimentos /acknowledgements

Neste desfecho de mais uma etapa da minha vida e com muito gosto queagradeco a todos os que me acompanharam ao longo destes anos e sem osquais nao me conseguiria imaginar a terminar esta fase. Alguns presentesdesde o primeiro dia em que integrei o curso, outros que fui conhecendo aolongo do tempo, mas todos igualmente importantes.

A minha familia que sempre me apoiou incondicionalmente em todas asdecisoes que tomei, bem como no decorrer da minha formacao.

A professora Susana Sargento, pela incondicional disponibilidade que sempreteve para ouvir todos os problemas que surgiram no decorrer do trabalho,dando sempre os melhores conselhos de forma a que os pudesse resolver.

A todos os meus amigos, que preenchem a minha vida dando-me motivospara sorrir a cada dia.

palavras-chave IEEE 802.16, IEEE 802.16-2004, IEEE 802.16e-2005, WiMAX, OFDM,OFDMA, MIMO, SIMO, Antenas Adaptativas, Planeamento, Celular,Malha, Redes Moveis

resumo Numa sociedade em que, crescentemente, utilizamos a Internet como meiode trabalho, ludico ou simplesmente informativo, sao necessarios novosmeios de levar esta tecnologia ate todos, de maneira a que possamos tirarpartido e evoluir com ela.

Sendo a falta de cobertura dos actuais operadores de telecomunicacoes, emcertas partes do territorio mais reconditas, um dos impedimentos para quetoda a populacao possa ter ligacao a Internet a precos acessıveis, o WiMAXsurge como uma solucao sem fios de banda larga com grande cobertura ebaixo custo de implementacao, que possibilita tanto o acesso directo porparte dos utilizadores como o funcionamento em modo de backhaul pararedes Wi-Fi ou de outro tipo.

O objecto de estudo desta dissertacao e o planeamento de redes WiMAXao nıvel fisico, ou seja, a disposicao dos diversos elementos constituintes darede da melhor forma possıvel para maximizar a cobertura da rede ao menorcusto.

No ambito deste trabalho foram efectuados diversos estudos ao nıvel depropagacao de ondas para a tecnologia WiMAX e planeamento de redesponto-multiponto e em malha. Foram desenvolvidos algoritmos que per-mitem o planeamento de redes em diversos cenarios, tais como: acesso emzonas de grande densidade populacional, acesso em zonas longınquas, ateonde se torna difıcil a passagem de um cabo, ou mesmo cenarios em queseja mais proveitoso utilizar o Wi-Fi como tecnologia de acesso para osutilizadores, ficando o WiMAX como tecnologia de backhaul. Foi tambemcriado um mechanismo de posicionamento automatico e optimizado doscomponenentes de uma rede em malha.

Como resultado do estudo realizado foram desenvolvidas com sucesso, apartir dos algoritmos estudados, duas aplicacoes para planeamento de redesWiMAX, uma em modo ponto-multiponto e outra em modo de malha, queserao devidamente apresentadas ao longo do texto.

Utilizando as aplicacoes desenvolvidas, foi possıvel obter varios resultadosque permitirao uma melhor compreensao e avaliacao de redes WiMAX.

keywords IEEE 802.16, IEEE 802.16-2004, IEEE 802.16e-2005, WiMAX, OFDM,OFDMA, MIMO, SIMO, Adaptive Beamforming, Planning, Cellular, Mesh,Mobile Networks

abstract In a society where the Internet is increasingly used for working, playing orsimply watching the news, new ways to get this technology to everyoneare required, so that each and everyone can take part on this great globalcommunity.

One of the major barriers for the widespread of the low-cost Internet is theunlikelihood to reach very remote locations of the territory. WiMAX appearsas a promising broadband wireless access technology with low deploymentcost and high coverage, allowing users to access directly to the network usingthis technology or providing backhaul connections for other technologies,such as Wi-Fi.

The aim of this MSc thesis is the physical WiMAX network planning, i.e.the placement of the various elements of a network in order to maximize decoverage with the lowest cost.

In the scope of this work, several studies in wave propagation for the WiMAXtechnology point-to-multipoint and mesh mode network planning were per-formed. Some algorithms that allow network planning in various scenarios,such as access in highly populated areas, isolated places, where it is hardto run a wire or even in scenarios where it is preferable to make the lastmile access in Wi-Fi were developed, as well as a mechanism to select thepositions of the elements in a WiMAX mesh network.

As a result of this study two applications for WiMAX network planning weresuccessfully developed, based on the formulated algorithms: one for point-to-multipoint mode and the other for mesh mode operation, which will beproperly presented through this text.

Using the developed applications, it has been possible to perform severalessays that will allow a better comprehension and evaluation of WiMAXnetworks.

Contents

Contents i

List of Figures v

List of Tables vii

Acronyms ix

1 Introduction 11.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.3 Contributions of the thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.4 Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

2 Background 52.1 IEEE 802.16 Access Technologies . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.1.1 IEEE 802.16-2004 - Fixed WiMAX . . . . . . . . . . . . . . . . . . . . 52.1.2 IEEE 802.16e-2005 - Mobile WiMAX . . . . . . . . . . . . . . . . . . . 62.1.3 IEEE 802.16 Mesh mode . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.2 MAC Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112.2.1 Convergence Sublayer (CS) . . . . . . . . . . . . . . . . . . . . . . . . 112.2.2 Common Part Sublayer (CPS) . . . . . . . . . . . . . . . . . . . . . . 122.2.3 Security Sublayer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

2.3 PHY Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132.3.1 Modulation Schemes: OFDM . . . . . . . . . . . . . . . . . . . . . . . 132.3.2 Modulation Schemes: OFDMA . . . . . . . . . . . . . . . . . . . . . . 162.3.3 Propagation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

2.4 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192.4.1 WiMAX Base Station Planning Tool . . . . . . . . . . . . . . . . . . . 19

2.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

3 IEEE 802.16 Performance and Enhancement Techniques 233.1 Performance Comparison: Fixed and Mobile WiMAX . . . . . . . . . . . . . 23

3.1.1 Primary comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233.1.2 Type of Terrain influence in Fixed and Mobile WiMAX . . . . . . . . 243.1.3 Sectoring in Fixed and Mobile WiMAX . . . . . . . . . . . . . . . . . 243.1.4 Range vs. Capacity . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

i

3.2 Enhancement Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263.2.1 MIMO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263.2.2 SIMO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283.2.3 Adaptive Beamforming . . . . . . . . . . . . . . . . . . . . . . . . . . 28

3.3 Enhancement Techniques comparison: Scenarios and Results . . . . . . . . . 283.3.1 Primary Comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293.3.2 Range Improvement for Fixed and Mobile WiMAX in Rural Scenarios 293.3.3 Range Improvement vs. Type of Terrain . . . . . . . . . . . . . . . . . 303.3.4 Capacity Improvement for Fixed and Mobile WiMAX in Rural Scenarios 323.3.5 Capacity Improvement vs. Type of Terrain . . . . . . . . . . . . . . . 33

3.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

4 802.16 Mesh Mode Planning 354.1 Challenges of mesh planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . 354.2 Interference-aware model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 364.3 Traffic and Candidate sites definition and classification . . . . . . . . . . . . . 37

4.3.1 Traffic Points . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 384.3.2 Candidate Sites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 404.3.3 Internal and External Traffic . . . . . . . . . . . . . . . . . . . . . . . 41

4.4 Scenario Description and Configuration . . . . . . . . . . . . . . . . . . . . . 414.5 Mesh Planning Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

4.5.1 Step 1: Set closer CSs . . . . . . . . . . . . . . . . . . . . . . . . . . . 454.5.2 Step 2: Fill ordered list of CSs . . . . . . . . . . . . . . . . . . . . . . 464.5.3 Step 3: Assign access . . . . . . . . . . . . . . . . . . . . . . . . . . . . 484.5.4 Step 4: Assign backbone . . . . . . . . . . . . . . . . . . . . . . . . . . 49

4.6 Optimally Placing Candidate Sites . . . . . . . . . . . . . . . . . . . . . . . . 514.7 Integration WiMAX-Wi-Fi . . . . . . . . . . . . . . . . . . . . . . . . . . . . 534.8 Optimal Mesh Planning: Integer-Based Mathematical Approach . . . . . . . 544.9 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

5 Mesh Planning Evaluation 575.1 Mesh Planning Tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

5.1.1 Starting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 575.1.2 Loading map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 585.1.3 Entering Traffic Points . . . . . . . . . . . . . . . . . . . . . . . . . . . 585.1.4 Entering Wi-Fi hotspots . . . . . . . . . . . . . . . . . . . . . . . . . . 585.1.5 Randomly generating Traffic Points . . . . . . . . . . . . . . . . . . . 605.1.6 Entering Candidate Sites . . . . . . . . . . . . . . . . . . . . . . . . . 615.1.7 Randomly generating candidate sites . . . . . . . . . . . . . . . . . . . 615.1.8 Auto-Placing Candidate Sites . . . . . . . . . . . . . . . . . . . . . . . 615.1.9 Removing Traffic Points or Candidate Sites . . . . . . . . . . . . . . . 615.1.10 Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 615.1.11 Calculate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 635.1.12 Saving Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 635.1.13 Loading Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

5.2 Comparison with the optimal linear programming approach . . . . . . . . . . 645.2.1 Scenario 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

ii

5.2.2 Scenario 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 645.2.3 Scenario 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 655.2.4 Random scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

5.3 Scenarios and Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 685.3.1 CS placing methods comparison . . . . . . . . . . . . . . . . . . . . . 685.3.2 Reaching remote locations without wire . . . . . . . . . . . . . . . . . 735.3.3 WiMAX-Wi-Fi Integration Example . . . . . . . . . . . . . . . . . . . 745.3.4 External and Internal traffic . . . . . . . . . . . . . . . . . . . . . . . . 765.3.5 Fixed and Mobile WiMAX . . . . . . . . . . . . . . . . . . . . . . . . 765.3.6 Growth of the amount of devices with the density of users . . . . . . . 78

5.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

6 Conclusion and Future Work 81

Bibliography 83

iii

List of Figures

2.1 Mesh WiMAX to improve territory coverage . . . . . . . . . . . . . . . . . . . . . 82.2 IEEE 802.16 mesh mode frames . . . . . . . . . . . . . . . . . . . . . . . . . . . 92.3 Three-way-handshake process . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102.4 Service Flow transitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122.5 MAC Sublayers [5] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122.6 Multipath example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142.7 Signal at the receiver in a multipath scenario . . . . . . . . . . . . . . . . . . . . 142.8 OFDM subcarriers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152.9 OFDM transmission . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162.10 OFDMA access method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172.11 WiMAX Base Station Planning Tool . . . . . . . . . . . . . . . . . . . . . . . . . 212.12 WiMAX Base Station Planning Tool: calculation result . . . . . . . . . . . . . . . 22

3.1 Comparison between Fixed and Mobile WiMAX . . . . . . . . . . . . . . . . . . . 243.2 Influence of the type of terrain in Fixed and Mobile WiMAX . . . . . . . . . . . . 253.3 Horizontal and vertical radiation patterns . . . . . . . . . . . . . . . . . . . . . . 253.4 Number of sectors and capacity for the same scenario . . . . . . . . . . . . . . . . 263.5 Range vs. Capacity for the different types of scenarios . . . . . . . . . . . . . . . . 273.6 Multiple antenna techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273.7 Adaptive beamforming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293.8 Enhancement techniques comparison . . . . . . . . . . . . . . . . . . . . . . . . . 303.9 Comparison between the implementation of the enhancement techniques in Fixed and

Mobile WiMAX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313.10 Range improvement of each technique for each type of terrain . . . . . . . . . . . . 313.11 Range improvement of each technique for each type of terrain compared to SISO in % 323.12 Capacity improvement with MIMO 4x2 S.M. in a rural environment . . . . . . . . . 323.13 Capacity improvement with MIMO 4x2 S.M. for each type of terrain . . . . . . . . 333.14 Capacity improvement with MIMO 4x2 S.M. related to SISO for each type of terrain

in % . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

4.1 Two hop interference-aware model . . . . . . . . . . . . . . . . . . . . . . . . . . 364.2 Two hop interference-aware model with some possible values . . . . . . . . . . . . 374.3 Interference-aware model considering all nodes’ traffic with some possible values . . 374.4 Scenario representation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 424.5 Algorithm scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 434.6 Modulation scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

v

4.7 Step 1 scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 474.8 Sorting method example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 484.9 Step 3 scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 504.10 Step 4 scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 524.11 Order for optimized CS placing . . . . . . . . . . . . . . . . . . . . . . . . . . . 534.12 Linear programming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

5.1 Traffic point configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 595.2 Dragging a TP to its position . . . . . . . . . . . . . . . . . . . . . . . . . . . . 605.3 Wi-Fi hotspot configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 605.4 Candidate site configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 625.5 Options panel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 635.6 Scenario 1 and results for both tools . . . . . . . . . . . . . . . . . . . . . . . . . 655.7 Scenario 2 and results for both tools . . . . . . . . . . . . . . . . . . . . . . . . . 665.8 Scenario 3 and results for both tools . . . . . . . . . . . . . . . . . . . . . . . . . 675.9 Number of MRs and MAPs for both tools . . . . . . . . . . . . . . . . . . . . . . 685.10 Time required for each tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 695.11 Scenario 1 - Traffic Points . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 705.12 Manual Placing CSs: case 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 705.13 Manual Placing CSs: case 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 715.14 Auto Placing CSs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 725.15 Randomly Placing CSs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 735.16 Reaching remote locations without wire - Traffic Points . . . . . . . . . . . . . . . 735.17 Reaching remote locations without wire . . . . . . . . . . . . . . . . . . . . . . . 745.18 WiMAX-Wi-Fi Integration - Traffic Points . . . . . . . . . . . . . . . . . . . . . . 755.19 WiMAX-Wi-Fi integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 755.20 Scenario 2 - Traffic Points . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 765.21 Scenario 2 - Placed CSs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 775.22 Relation between the internal/external traffic rate and the number of MAPs and MRs 775.23 Fixed and Mobile WiMAX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 785.24 Urban Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 795.25 Growth of the amount of devices with the density of users . . . . . . . . . . . . . . 79

vi

List of Tables

2.1 802.16 standards comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . 72.2 SUI model parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172.3 SNRRx for each modulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182.4 Constants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202.5 Typical usage values for the residential profile . . . . . . . . . . . . . . . . . . 202.6 Typical usage values for the business profile . . . . . . . . . . . . . . . . . . . 20

3.1 Results for Fixed and Mobile WiMAX . . . . . . . . . . . . . . . . . . . . . . 243.2 Results for the three types of terrain . . . . . . . . . . . . . . . . . . . . . . . 243.3 Capacity growth with the number of sectors in Fixed and Mobile WiMAX . . 253.4 Results for the various enhancement techniques . . . . . . . . . . . . . . . . . 293.5 Results for range improvement in Fixed and Mobile WiMAX . . . . . . . . . 30

4.1 Typical bandwidth usage for some services . . . . . . . . . . . . . . . . . . . . 384.2 Traffic class for each considered service . . . . . . . . . . . . . . . . . . . . . . 384.3 Typical usage values for the residential profile . . . . . . . . . . . . . . . . . . 394.4 Typical usage values for the business profile . . . . . . . . . . . . . . . . . . . 394.5 Typical values for density of users . . . . . . . . . . . . . . . . . . . . . . . . 394.6 Typical configuration values for a candidate site . . . . . . . . . . . . . . . . . 404.7 Capacities of the IEEE 802.11 set of standards . . . . . . . . . . . . . . . . . 54

5.1 Results for scenario 1 with both tools . . . . . . . . . . . . . . . . . . . . . . 645.2 Results for scenario 2 with both tools . . . . . . . . . . . . . . . . . . . . . . 655.3 Results for scenario 3 with both tools . . . . . . . . . . . . . . . . . . . . . . 665.4 Results for both tools with the same random scenario . . . . . . . . . . . . . 685.5 Density of users . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 695.6 Manual Placing CSs: case 1 - Results . . . . . . . . . . . . . . . . . . . . . . . 705.7 Manual Placing CSs: case 2 - Results . . . . . . . . . . . . . . . . . . . . . . . 715.8 Configuration of the Auto Place feature . . . . . . . . . . . . . . . . . . . . . 725.9 Auto Placing CSs - Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 725.10 Randomly Placing CSs - Results . . . . . . . . . . . . . . . . . . . . . . . . . 725.11 Reaching remote locations without wire - Results . . . . . . . . . . . . . . . . 745.12 WiMAX-Wi-Fi Integration - Results . . . . . . . . . . . . . . . . . . . . . . . 745.13 Fixed and Mobile WiMAX - Results . . . . . . . . . . . . . . . . . . . . . . . 77

vii

Acronyms

AAS Adaptive Antenna SystemsAMPL A Mathematical Programming LanguageATM Asynchronous Transfer ModeBMP Bitmap file formatBWA Broadband Wireless AccessCID Connection IdentifierCS Candidate Site or Convergence SublayerCPLEX ILOG CPLEX optimization software packageCPS Common Part SublayerCRC Cyclic Redundancy CheckDSL Digital Subscriber LineertPS Extended Real-Time Polling ServiceFDM Frequency Division MultiplexingFDMA Frequency Division Multiple AccessFEQ Forward Error CorrectionFFT Fast Fourier TransformGIF Graphics Interchange FormatHiperMAN High Performance Radio Metropolitan Area NetworkIFFT Inverse Fast Fourier TransformIP Internet ProtocolIPTV Internet Protocol TelevisionIPv4 Internet Protocol version 4ISI Inter Symbol InterferenceJPEG Joint Photographic Experts GroupLMDS Local Multipoint Distribution ServiceLOS Line Of SightLP Linear ProgrammingMAP Mobile Access PointMBS Multicast and Broadcast ServicesMIMO Multiple Input Multiple OutputMPS Mesh Planning SoftwareMR Mobile RouterMS Mobile StationMSDU Mac layer Service Data Unit

ix

MT Mobile TerminalNLOS Non-Line of SightOFDM Orthogonal Frequency Division MultiplexOFDMA Orthogonal Frequency Division Multiple AccessPDU Protocol Data UnitPHY PhysicalPKM Privacy Key ManagementPKMv1 Privacy Key Management version 1PKMv2 Privacy Key Management version 2PNG Portable Network GraphicsQoS Quality of ServiceP2P Peer-to-PeerPAPR Peak-to-Average Power RatioPMP Point-to-MultipointRF Radio FrequencySC Single CarrierSIMO Single Input Multiple OutputSISO Single Input Single OutputSOFDMA Scalable Orthogonal Frequency Division MultiplexSS Subscriber StationSUI Stanford University InterimTDM Time Division MultiplexingTDMA Time Division Multiple AccessTP Traffic PointVoIP Voice over Internet ProtocolWi-Fi Wireless FidelityWiMAX Worldwide Interoperability for Microwave AccessWMN Wireless Mesh Networks

x

Chapter 1

Introduction

1.1 Motivation

Over the last years, the Internet access has been spread all over the developed countries’population. Although it may seem common to people living in cities, only 23.8% of theworldwide population has access to the Internet [1]. The most probable reason for thisnumber is the poverty experienced by the population of many countries around the world,but not only. Isolated places, like small villages, far away from big cities in developed countriesalso contribute. Many and many will never get a wired Internet connection, just because theoperators would spend more money getting the wire there than they would ever earn byselling some dozens or hundreds of service packs.

One of the possible solutions for this problem is the use of Worldwide Interoperability forMicrowave Access (WiMAX). WiMAX is a Broadband Wireless Access (BWA) technology,which aims to provide long-distance and high-speed wireless connections, up to 50 Km and 70Mb/s [2]. This way, installation costs can be truly reduced, which will lead to the expansionof the Internet Access towards these farther locations. This is one of the major interest pointsof WiMAX, but there are many others.

WiMAX will also be available at urban areas, once it is a promising technology for fixedand mobile broadband access. Having such high transmission rates, turns it able to handleWi-Fi backhaul connections, thus allowing Wi-Fi access points to operate without a wiredconnection to the backbone. Telemetering1 is also an interesting topic, which can be supportedby WiMAX, as almost the whole country’s territory is supposed to be covered. Moreover,WiMAX supports mesh mode operation, which allows base stations to fully operate withouta wired connection to the backbone.

Although this is a very promising technology, one of the problems of deploying WiMAXnetworks is that, until the date, there have not yet been performed sufficient studies inresource placement: what we call planning. The purpose of this MSc thesis is to fill this gap,by providing mechanisms to evaluate scenarios, optimize deployment solutions, automaticallyplace the necessary resources given the traffic requirements within a certain area or even

1Telemetering is the act of reading the water, energy, natural gas, and other meters remotely. This isan excellent way for the companies to monitor these services and keep the users daily informed about theirconsumption.

1

build backhaul networks to provide connectivity between Wi-Fi hotspots in both Point-to-multipoint and mesh WiMAX operation modes.

1.2 Objectives

The main objective of this thesis is to develop algorithms to aid the design and deploymentof WiMAX networks. The algorithms will then be included in planning tools also developedin the framework of this thesis. For this to be accomplished, several studies on the IEEE802.16 standard must be done, mainly concerning the PHY layer.

The following tasks will be performed:

• Study of the IEEE 802.16-2004 and IEEE 802.16e-2005 standards, focusing on mobility,enhancement techniques and mesh mode operation.

• Implementation of these techniques in a previously developed base station planning toolfor point-to-multipoint (PMP) operation.

• Improvement of a linear programming model, so that it could fit the desired planningfeatures.

• Development of a WiMAX mesh network planning algorithm through comparison withthe linear programming model.

• Development of an optimal base station placing algorithm.

• Integration of WiMAX mesh networks and Wi-Fi hotspots.

• Development of a mesh network planning tool for Microsoft WindowsTM.

1.3 Contributions of the thesis

As all the proposed tasks were accomplished, this thesis contributions are:

• The enhancement of an existing base station planning mechanism and tool, which sim-plifies the deployment of PMP networks.

• The development of a WiMAX mesh planning tool, which optimizes the deployment ofmesh networks, by restricting the network to the minimum number of base stations.

1.4 Outline

This thesis is structured as follows. Chapter 2 presents the IEEE 802.16 standard in termsof the concepts necessary to clearly understand the content of this work, distinguishing thevarious amendments and each one’s features. It also presents the state of the art, along witha previous work on the area: a base station planning tool for PMP. Chapter 3 presents aperformance comparison between Fixed and Mobile WiMAX, some enhancement techniquesdescribed in the IEEE 802.16 standard, as well as its implementation in the base stationplanning tool. Some interesting results, concerning these techniques, obtained with the base

2

station planning tool are also shown. Chapter 4 focuses on mesh mode planning, by explainingthe biggest problem of this type of network planning – interference – and exhibiting analgorithm developed to solve this type of problems. A linear programming model is presented,as a way of accounting for the accuracy of the developed algorithm. Chapter 5 describesthe implementation of the algorithm, presented in Chapter 4, in a mesh planning tool, forMicrosoft WindowsTM, a comparison between solutions calculated by this one and by thelinear programming model, as well as some interesting results and scenarios.

Finally, Chapter 6 presents the final conclusions and future work.

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Chapter 2

Background

The IEEE 802.16 standard has had great evolutions since the first version was released.These evolutions are presented in the form of amendments to the standard, as they arereleased. Section 2.1 will give an overview of these amendments and the evolution of thetechnology, as well as the different types of access methods it defines.

As the standard defines the MAC and PHY layers of the OSI model, there are several con-cepts of wave propagation and MAC layer constitution that must be learned before exploringthe standard. Sections 2.2 and 2.3 will present them.

In Section 2.4 some related work is presented, along with the introduction to a previouswork on the area.

Finally, Section 2.5 includes a summary of the chapter.

2.1 IEEE 802.16 Access Technologies

2.1.1 IEEE 802.16-2004 - Fixed WiMAX

In 1999, when the IEEE 802.16 group was created, it was intended to develop a line of sight(LOS) PMP wireless broadband communications system operating in the 10-66 GHz band. In2001, the first version of the IEEE 802.16 standard was released as result of the work of thisgroup. This version uses the 10-66 GHz spectrum band and only supports fixed line of sight(LOS) communication. Burst multiplexing is based on time division multiplexing (TDM) ortime division multiple access (TDMA) and a single carrier (SC) is used. The antennas aresupposed to be exterior and placed on the top of buildings and communications are point-to-multipoint (PMP) oriented, achieving data rates from 32 Mbps to 130 Mbps. Although thismay seem to be a nice alternative to the current broadband access technologies, such as digitalsubscriber line (DSL) or cable, there are two main problems: the use of frequencies in thelicensed spectrum and the absence of conformance with high performance radio metropolitanarea network (HiperMAN) European standard.

In December 2002, the IEEE 802.16 task group C came up with the IEEE 802.16c amend-ment, which aims to ensure interoperability with the existing local multipoint distributionservice (LMDS) and defines system profiling, in order to establish guidelines for vendors, toassure interoperability between them. Such profiles determine the mandatory and optionalfeatures of the equipments. Mandatory features include provisioned connections, IPv4 and

5

fragmentation support. As optional features there are different levels of security, which allowthe vendors to differentiate their products by price and functionality. Another specificationis that 802.16c is network topology independent, what means that it can run under asyn-chronous transfer mode (ATM), frame relay or Internet protocol (IP). The big disadvantageis that, due to the large bandwidth, it has a low coverage, around 5 Km only.

The most important of all the amendments was released in 2003 under the name of IEEE802.16a. As it works in lower frequencies (2-11 Ghz), it is able to reach up to 50 Km withup to 75 Mbps bitrates; this feature also enables it to work under non line of sight (NLOS)conditions. Mesh mode operation is another of the main points of this amendment that easesthe communication between subscriber stations (SS).

In June 2004, a compilation of the original standard and its amendments until that datewas released and some new features introduced. It is called IEEE 802.16-2004 or IEEE 802.16dand defines the operation in the 2-66Ghz band [3], providing support at the MAC and PHYlayers for distinct operation at low and high frequencies due to the different propagationproperties. It includes fixed LOS and NLOS communications, as well as PMP, mesh modeoperation and quality of service (QoS) support.

With all these amendments to the standard and configuration possibilities, the need fora certification entity grows, in order to assure the compatibility between all the equipmentson the market. This is where the WiMAX word takes meaning. WiMAX is a certificationentity, similar to Wi-Fi, that ensures that every two WiMAX certified products, runningIEEE 802.16, can work together.

2.1.2 IEEE 802.16e-2005 - Mobile WiMAX

The 802.16-2004 document was surely useful as the gathering of a lot of documents talkingabout different parts and corrections to the same technology. However, there were, indeed,some features, such as mobility support, to be introduced and corrections to be made, so inDecember 2005 a new amendment – IEEE 802.16e – was approved.

The major differences between 802.16-2004 and 802.16e-2005 are [2]:

• The establishment of the concept of mobility into the technology and, therefore, theintroduction of mobile stations (MS). From this point on the WiMAX subscriber stations(SS) do not need anymore to be fixed, so this is why 802.16e is often named as MobileWiMAX.

• Handover procedures were introduced in the MAC layer, once again to support mobil-ity/nomadism.

• Power save modes were introduced: sleep and idle mode.

• Scalable orthogonal frequency division multiple access (SOFMA) was introduced or, inother words, the OFDM PHY layer was modified.

• Security methods were updated.

• Multiple input multiple output (MIMO) and Adaptive Antenna Systems (AAS) wereenhanced. (This topic will be covered later on Section 3.2.1).

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• Multicast and broadcast services (MBS) were added.

• New QoS class was introduced: Extended Real-Time Polling Service (ertPS), which isdesigned for real-time traffic with variable data rate, such as VoIP with silence suppres-sion.

Table 2.1 summarizes the 802.16 standards family.

Table 2.1: 802.16 standards comparison

802.16 802.16-2004 802.16e-2005Release date December 2001 June 2004 December 2005

2-11GHz for fixedFrequency band 10-66GHz 2-11GHz 2-6GHz for mobile

Application Fixed LOS Fixed NLOS Fixed and mobileNLOS

MAC architecture PMP, mesh PMP, mesh PMP, meshData rate 32-135Mbps 1-75Mbps 1-75Mbps

MultiplexingBurstTDM/TDMA

BurstTDM/TDMA/OFDMA

BurstTDM/TDMA/OFDMA

Duplexing TDD and FDD TDD and FDD TDD and FDD

Channel bandwidths

20Mhz, 25MHz,28MHz

1.75MHz, 3.5MHz,7MHz, 14MHz,1.25MHz, 5MHz,10MHz, 15MHz,8.75MHz

1.75MHz, 3.5MHz,7MHz, 14MHz,1.25MHz, 5MHz,10MHz, 15MHz,8.75MHz

2.1.3 IEEE 802.16 Mesh mode

Mesh networks are networks where each node can connect either to all of its neighbors(full mesh) or a part of them (partial mesh). These networks offer a set of benefits, mainly thecapability to self-heal when a node goes down, due to the redundancy introduced by multipleconnections. Another main point of mesh networking is that not all the nodes of a meshnetwork need to be connected to the backbone, once they can communicate with each otheror, at least, some others and appropriately transfer the data to its destination. This propertyof mesh networks leads to the conception of two types of devices: mesh routers (MR), whichcan communicate with each other and with other devices, and mesh access points (MAP)which are connected to the backbone and thus can communicate with MRs and with thewired backbone. These devices have the same physical attributes as PMP base stations.

Applying this to reality, imagine that somewhere there is a small village far away fromeverything. Its inhabitants could never dream of having a wired Internet connection justbecause the cost of running there a wire for the operator would be too big comparing to thebenefits it would bring. Now imagine that, close to that village, there is another one. Runningthere a wire is equally expensive, but we can try doing it with a wireless mesh technology,such as WiMAX. Figure 2.1 illustrates this scenario.

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Figure 2.1: Mesh WiMAX to improve territory coverage

By using one MAP and two MRs, it is possible to grant Internet connection to these twovillages at a low cost. Using WiMAX mesh networking, only MAP1 needs to be connectedto the wired backbone.

Oppositely to Wi-Fi mesh, WiMAX is topology aware, what means that it uses informationfrom nodes 2 or 3 hops away to take decisions. This fact turns it more robust in terms ofminimizing the hidden and exposed node problems.

WiMAX mesh mode uses a channel for control communications and another for data.This reduces the risk of collision, once the control messages do not interfere with data.

Although it is a very promising technology, IEEE 802.16e mesh mode is not widely scal-able. In single radio architectures, it is proved that the signal degrades some hops away [4]with the increasing number of nodes. There are some ways to solve or reduce this problem,mainly:

• The usage of multi-radio and multi-channel systems. Using a radio for control trafficand another for data traffic can be a solution, though it is expensive.

• Locating techniques to prevent control packets, for instance, from traveling across thewhole network.

• Implementation of a hierarchical network scheme by deploying fixed nodes to act asrelay candidates.

The IEEE 802.16 standard defines the mesh frames as having two subframes: controlsubframe and data subframe. The length of the control subframe is given by MSH-CTRL-LEN × 7, where MSH-CTRL-LEN has 4 bits, so it ranges from 0 to 105 OFDM symbols.The data subframe is divided into minislots.

Figure 2.2 shows the two types of control subframes: network control (2.2(a)) and schedulecontrol (2.2(b)). The first one occurs in the frames sent periodically with a well defined periodfor each network. This type of subframe is intended for nodes gaining synchronization and

8

joining the network. Schedule control subframes are defined for centralized or distributedscheduling, which will be explained next.

The data subframe is used for the PHY transmission frames. It starts with a long pream-ble, for synchronization, followed by several MAC protocol data units (PDUs). Each MACPDU has a 6-byte MAC header, a 2-byte mesh subheader with the node ID, a variable lengthMAC payload field and an optional 4-byte cyclic redundancy check (CRC).

(a) Network control subframe

(b) Network schedule subframe

Figure 2.2: IEEE 802.16 mesh mode frames

As said before, there are two types of scheduling: centralized and distributed. The dis-tributed scheduling can be further divided into uncoordinated and coordinated.

Centralized Scheduling

In centralized scheduling, a designated node, the mesh BS, coordinates the data subframescheduling, resource allocation and grants for the other nodes in the mesh network. Theprocess for resource allocation is the following:

• Each SS determines the traffic estimation for him and its children and sends a messageup to its parent, until it reaches the mesh BS.

• When the mesh BS gathers all the necessary information, it determines the amountof granted resources and broadcasts this information in a message to all its neighbors,which pass it down to their children.

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Figure 2.3: Three-way-handshake process

Uncoordinated Distributed Scheduling

Uncoordinated distributed scheduling is used for temporary communications between twonodes. A three-way-handshake process, depicted in Figure 2.3, is used, based on mesh dis-tributed scheduling messages (MSH-DSCH), which contain information about slot availabil-ity, scheduling, requests and grants. In this type of scheduling, the MSH-DSCH messages aretransmitted in the data subframe. The three-way-handshake process works in the followingway:

• When a node wants to communicate, randomly selects an idle slot and sends a MSH-DSCH: Request message to acquire the resource. If there is a collision, it enters arandom backoff time and then sends the request again.

• When the granter receives the message requesting the resources, it evaluates the requestthrough a slot allocation algorithm. If the granter is able to allocate the resource, sendsout a MSH-DSCH: Grant to the requester.

• The requester copies the message and sends it as an acknowledge to the granter througha MSH-DSCH: Grant Confirmation message.

By listening to this message exchange, all the nodes are aware of the free resources.

There is, currently, no slot allocation algorithm defined in the IEEE 802.16 standard.This gives the deployer the opportunity to select an appropriate algorithm according to itsnetwork needs.

Coordinated Distributed Scheduling

In the same way as in uncoordinated distributed scheduling, the MSH-DSCH messageplays an important role in coordinated distributed scheduling. The difference here is that

10

it is transmitted in the control subframe. In this case, the three-way-handshake process(Figure 2.3) is used to create a communication link with a neighbor node. When this link issuccessfully created, the two nodes can communicate in the reserved slots.

Also, in this type of scheduling, no slot allocation algorithm is defined.

2.2 MAC Layer

The IEEE 802.16-2004 standard defines three sublayers for the MAC layer: convergencesublayer (CS), common part sublayer (CPS) and security sublayer, which will be presentedin this section. First we describe some fundamental concepts.

Connection Identifiers

A connection is a unidirectional MAC connection between a BS and a SS/MS or vice-versa.The purpose of a connection is to transmit the traffic of a service flow. Each connection canonly serve one type of service and is identified through a connection identifier (CID).

The CID is a 16-bit value, thus providing 64 000 connections for each downlink and uplinkchannel. There are many CIDs defined in the standard [5] with specific meanings.

Service Flows (SF)

A service flow (SF) is a unidirectional MAC transport service that defines the QoS pa-rameters for the PDUs exchanged on a connection. There are three types of SFs:

• Provisioned - is know by provision from the network management, for example.

• Admitted - the standard supports a two-phase activation model, just like in telephony.So, the resources are first admitted and then activated, once the negotiation processends. This is the SF for admitted connections.

• Active - service flow with the resources consigned by the BS.

A SF can transit from one state to another, according to Figure 2.4.

2.2.1 Convergence Sublayer (CS)

As stated before, the IEEE 802.16 MAC layer is divided into three sublayers, as shownby Figure 2.5. The CS is the top sublayer of the IEEE 802.16 MAC layer. It is responsiblefor the communication with the upper layers, in other words, it is responsible for sendingand receiving the PDUs to/from the upper layers. This sublayer also performs basic QoSoperations, such as classifying and mapping the MAC layer service data units (MSDUs) intothe proper CIDs.

As this sublayer deals with various types of technology on the upper layers, there mustbe different specifications for each type. Currently there are two types defined: ATM CSand packet CS, but more can be defined in the future. The first one is intended to deal withthe asynchronous transfer mode (ATM) technology, whereas the latter is for packet-basedtechnologies, such as IPv4, IPv6, PPP or IEEE 802.3 (Ethernet).

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Figure 2.4: Service Flow transitions

Figure 2.5: MAC Sublayers [5]

There is an optional function which is Payload Header Suppression. With this function,repetitive parts of the payload headers are suppressed at the sender and restored at thereceiver.

2.2.2 Common Part Sublayer (CPS)

The Common Part Sublayer (CPS) is the middle sublayer of the MAC layer. It is respon-sible for the bandwidth allocation, connection establishment and connection maintenance.

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The IEEE 802.16 standard defines a set of messages that must be exchanged to negotiateand establish a connection. After the negotiation process is finished and the bandwidth isallocated for a certain connection, transfer messages can be exchanged in order to receive/senddata.

2.2.3 Security Sublayer

The last sublayer, Security Sublayer, is responsible, as the name suggests, for the securityof the connections. It provides authentication, secure key exchanging, encryption and integritycontrol across the whole system. Several procedures for data encryption are included inthe standard. For secure key exchanging, the IEEE 802.16-2004 defines the Privacy KeyManagement (PKM) authentication protocol. The IEEE 802.16e-2005 amendment updatesthe encryption protocol, by defining the PKMv2, which is an evolution of the renamed PKMv1protocol.

2.3 PHY Layer

2.3.1 Modulation Schemes: OFDM

Orthogonal Frequency Division Multiplex (OFDM) is a frequency division multiplexingscheme where data is transmitted simultaneously in many narrow-band orthogonal frequen-cies called subcarriers. As these frequencies are orthogonal to each other, the problem ofinterference between channels is reduced or even eliminated.

The number of subcarriers into which each wideband signal is break is usually called N.As each channel has smaller bandwidth than in single carrier (SC) transmission, by (2.1) weknow that its transmission time will be as large as smaller its bandwidth is.

∆T =1

∆f(2.1)

So, if the wideband signal is broke into N subcarriers, the transmission time for each sub-carrier will be N times longer, as shown by (2.2), thus providing better multipath resistance.

∆T =N

∆f(2.2)

Multipath

Multipath is one of the major problems of wave propagation. This phenomenon resultsin radio signals reaching the receiver by more than one path and, therefore, at differenttimes. The causes of this phenomenon are various, and include refraction and reflection dueto the ionosphere, the terrain conditions or buildings in the way, weather conditions andthe presence of water bodies which cause reflection, among others. Figure 2.6 describes amultipath scenario: the base station is sending out a wave towards 1, but this wave alsoreaches building 2, which reflects it also towards 1. Both signals will reach the receiver, butat different times, as shown by Figure 2.7.

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Figure 2.6: Multipath example

Figure 2.7: Signal at the receiver in a multipath scenario

FFT and IFFT

The fast Fourier transform (FFT) is a matrix computation that allows computing thediscrete Fourier transform under certain mathematical conditions. The inverse fast Fouriertransform (IFFT) is the inverse computation which makes it possible to divide the widebandsignal into N orthogonal subcarriers. Although the FFT can be calculated for any numberof points, the operation is easier for a number of points which is a power of 2. As shown byFigure 2.8, after the division of the wideband signal, each subcarrier has a null value at themaximum of the others.

Cyclic Prefix and the guard band

The cyclic prefix technique is also used in OFDM to avoid inter symbol interference (ISI).This technique consists of repeating the end of each symbol at its beginning. The purpose ofthis is to allow for multipath to settle before the main data arrives at the receiver.

To see how it works, let us consider a maximum channel delay spread of v + 1 samples.By adding a guard band of, at least, v samples between OFDM symbols we can assure that

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Figure 2.8: OFDM subcarriers

each symbol is independent from its neighbors. Let x denote an OFDM symbol with lengthN

x =[x1 x2 . . . xN

](2.3)

After applying a cyclic prefix of length v, we get:

xcp =[xN−v xN−v+1 . . . xN−1 x0 x1 . . . xN−1

](2.4)

If h is a v + 1 length vector which describes the impulse response of the channel during theOFDM symbol, we can calculate the output of the channel through (2.5)

ycp = h ∗ xcp (2.5)

Thus, the output has L + 2v samples: the first v samples contain interference from thepreceding symbol and the last v from the last symbol, and so are discarded. This leavesexactly N samples, which is exactly what is needed to recover the N data symbols in x.These discarded symbols form the guard band, which guarantees the reduction of the Inter-Symbol Interference (ISI). We can assure that these resulting N symbols are equivalent to

y = h⊗ x, (2.6)

however it is out of the scope of this text to prove it. Further readings on this topic can befound in [6].

Looking at Figure 2.9 we are now able to distinguish the various steps of a transmissionin OFDM:

1. The IFFT of the X signal is calculated in order to break the wideband signal intovarious orthogonal subcarriers.

2. The parallel to serial (P/S) converts the resulting signal into a series of samples.

3. The cyclic prefix if added to each symbol.

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Figure 2.9: OFDM transmission

4. The channel is traversed and noise is added to the signal.

5. The cyclic prefix is removed and the reverse operation of step 1 is done.

6. The FFT of the signal is calculated.

7. A forward error correction (FEQ) algorithm is applied in order to regenerate erroneoussymbols.

One of the greatest advantages of OFDM is that it does not need complex equalizationfilters, once an OFDM symbol can be seen as many slowly modulated symbols, instead ofone rapidly modulated. This is what makes it possible to introduce a guard band betweensymbols, making it unnecessary to use complex filters to separate them.

Another interesting point is the possibility of adapting the transmission to the channelconditions. This can be done by deactivating subcarriers exposed to high interference orattenuation or applying stronger modulation and error correction to them, which turns themslower, but more robust.

There are two main disadvantages of OFDM relating to SC:

• It is very sensitive to frequency synchronization problems because the orthogonality ofthe symbols relies on their being correctly distinguished in the frequency domain.

• The peak to average power ratio (PAPR) is high, what can be a hard constraint to somedevices, namely RF amplifiers.

2.3.2 Modulation Schemes: OFDMA

OFDM is indeed a powerful modulation scheme, but in order to address the WiMAX needs,a multiple access technique is essential. Orthogonal Frequency Multiple Access (OFDMA) isthe answer. Through the assignment of subsets of subcarriers, called subchannels, to eachindividual user, the multiple access issue can be solved.

OFDMA is a combination of two popular channel access methods: time division multipleaccess (TDMA) and frequency division multiple access (FDMA) as shown in Figure 2.10.

On the uplink, one or more subchannels can be assigned to the transmitter, while on thedownlink a subchannel can be used for different receivers.

One major advantage of OFDMA is its potential to reduce the transmit power, becauseeach user is only assigned a subset of subcarriers, thus reducing the PAPR, as it increaseswith the bandwidth.

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Figure 2.10: OFDMA access method

2.3.3 Propagation Model

In order to calculate the area covered by a radio, we must use models that usually rely onphysical facts and statistics obtained from practical measures. For the NLOS case, especially,the latter are more accurate.

For the frequencies at which WiMAX radios operate, the SUI (Stanford University In-terim) model is the most appropriate [7]. In this model, three types of scenarios are con-templated: A, B and C. Type A is the most lossy, being appropriate for bumpy zones withmoderate to dense vegetation. Type C is applicable to plane places with light vegetation,whereas type B relies between these two.

Using the SUI model, the path losses can be calculated by (2.7):

Lp = 20 log(

4πd0

λ

)+ 10γ log

(d

d0

)+ Xf + Xh + s (2) (2.7)

where s ∈ [8.2, 10.6] dB is the statistical lognormal distribution which accounts for the fadingdue to obstacles, γ stands for the loss factor exponent, Xf for the frequency correction factorand Xh the antenna height correction factor. These parameters can be determined by (2.8),(2.9) and (2.10) respectively:

γ = a− b · hb +c

hb(2.8)

where hb is the height of the antenna in meters and parameters a, b, and c depend on the typeof terrain and are given by Table 2.2.

Table 2.2: SUI model parametersType A Type B Type C

a 4.6 4.0 3.6b 0.0075 0.0065 0.0050c 12.6 17.1 20.0

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Xf = 6.0 log(

f

2

)(2.9)

being f the frequency of operation in GHz.

Xh =

−10.8 log(

hr2

), for type A and B

−20 log(

hr2

), for type C

(2.10)

where hr stands for the antenna height.

Now that we are able to quantify the path losses, Equation 2.11 can be used to determinethe power at the receiver.

PR = PT + GT + GT − LS − LP [dBm] (2.11)

where PT is the transmitter power in dBm, GT and GR the transmitter and receiver gains,both in dBi, LS the inherent system losses in dB (transmitter + receiver) and LP , as previouslystated, the path losses, also in dB.

The next step is to calculate the receiver’s sensitivity SR using Equation 2.12:

SR = −101 + SNRRx + 10 log(

Fs · Nused

NFFT· Nsubchannels

16

)[dB] (2.12)

being FS the sampling frequency in MHz, Nused the number of used subcarriers, NFFT thetotal number of subcarriers, and Nsubchannels the number of subchannels. The minimum SNRfor each modulation is defined in the IEEE 802.16e-2005 standard [8] and presented in Table2.3.

Table 2.3: SNRRx for each modulationModulation SNRRx [dB]BPSK 1/2 3.0QPSK 1/2 6.0QPSK 3/4 8.5

16-QAM 1/2 11.516-QAM 3/4 15.064-QAM 2/3 19.064-QAM 3/4 21.0

It’s clear that for a receiver to be able to detect a signal (2.13) must be true.

PR ≥ SR (2.13)

So now that we can calculate both PR and SR, we can determine the maximum distance foreach modulation using (2.14), which results from the combination of (2.7), (2.12) and (2.13).

d = d0 · 10

(PT−LS+GR+GT−SR−Xf−Xh−s(2)−20 log

(4πd0

λ

))

10γ

(2.14)

Using this information, we can set the maximum reachable distance of each cell, thus providingthe needed information to determine the coverage area.

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2.4 Related Work

In what concerns PMP base station planning, in [7], Res presents a solution for basestation planning, explaining all the necessary aspects to take into account when planninga PMP base station, including user prediction, propagation models and antenna physicalparameters. Our work in the area of PMP planning will be based in its assumptions, soSection 2.4.1 will present a software developed in the scope of it.

Although mesh network planning is a topic that has not yet been much explored, there area few documents on the area. In [9], Alicherry et al. present an algorithm for slot allocation inmulti-radio, multi-channel Wireless Mesh Networks (WMN). Chandra et al. have formulatedin [10] the Internet gateway placement in WMNs under three wireless models. They havedeveloped an algorithm to minimize the amount of these devices, through optimal placementand have also taken into account robustness in WMNs, by including fault tolerance in theiralgorithm. In [11], Kodialam and Nandagopal have worked on capacity analysis in WMNs byconsidering the problem of determining the achievable rates in multi-hop wireless networkswith orthogonal channels. [12] addresses the problem of trading range for capacity in WMNswhile exploring the benefits of using relay nodes in networks. In [13], Pabst et al. proposetwo heuristic algorithms to solve the problem of Internet gateways placement in WMNs.

In [14], Amaldi, et al. propose a linear programming formulation of the problem of placingInternet access nodes in a network, which will be used in this work to account for the accuracyof a developed algorithm.

Most of the work in WMNs has been developed thinking on sensor networks. In [15],Bogdanov et al. analyze the problem of positioning data collector stations in terms of datarate and power efficiency for mesh sensors. In [16], Cheng et al. propose two algorithms tominimize the number of relay sensors in a mesh sensor network by improving their placement.With the same purpose, Khanna et al. propose in [17] a genetic algorithm.

Finally, in [18], Poduri et al. perform a very interesting work by evaluating the problemof representing 3-D scenarios in 2-D and prove that sometimes this generalization is not valid.

Although all the cited works in the area of mesh networks are very useful and contributedto the development of the algorithms and mechanisms presented in this work, none of thempresents a heuristic capable of being implemented in a planning software, independent of othercalculation platforms. Moreover, all the works in the area consider that the whole scenariois defined a priori, while our solution is able to evaluate a completely defined scenario or topropose the placement of the access devices, given the traffic parameters for a well definedzone.

2.4.1 WiMAX Base Station Planning Tool

This section will give a brief explanation about the WiMAX Base Station Planning Tool,whose development was started in the scope of [7] and its conclusion is one of the objectivesof this thesis.

This is a flexible and very user-friendly application which, after the definition of someparameters, calculates the best solution for a WiMAX base station.

19

This tool contains two main windows: one for configuration and parameter setting andthe other to present the results. Within the first window there are three panels, as shown byFigure 2.11.

The first panel, “Constants” (Figure 2.11(a)) allows the configuration of VoIP, data, IPTV,media stream, online gaming, P2P and Video Conference bandwidth requirements and alsothe type of terrain where the BS is to be placed: rural, urban or suburban. According to thisselection, an appropriate propagation model will be selected. Default values for each field aresuggested and presented in Table 2.4. In the second panel (Figure 2.11(b)) the user can define

Table 2.4: ConstantsService Bandwidth

VoIP 80KbpsData - Residential 1000KbpsData - Business 2000Kbps

IPTV 2000KbpsMedia Stream 20KbpsOnline Gaming 85Kbps

P2P 500KbpsVideoconference 385Kbps

the clients’ needs in terms of utilizations of the services considered during two periods: dayand night. There are two types of users: residential and business, that can coexist, havingdifferent bandwidth needs. For each type of users it is also possible to define the density ofusers. Typical values, suggested by the application, are also presented in Tables 2.5 and 2.6.

Table 2.5: Typical usage values for the residential profile

Day NightService Nr. of utiliz. Dur. (min) Nr. of utiliz. Dur. (min)

VoIP 7 5 2 5Data 6 40 4 20IPTV 2 60 1 100

Media Stream 1 20 0 0Online Gaming 1 60 0 0

P2P 1 30 0 0Video conference 1 30 0 0

Table 2.6: Typical usage values for the business profile


VoIP 30 2 5 2Data 20 10 0 0

Video conference 3 60 0 0

20

(a) Constants

(b) Profiles

(c) Technology

Figure 2.11: WiMAX Base Station Planning Tool

21

The third panel (Figure 2.11(c)) contains technology parameters that must be config-ured according to the type of network being planned and the physical characteristics of theequipment.

When the application is executed, the boxes are filled with the presented typical valueswhich can and shall be modified by the user in order to accurately define his scenario.

After all the specifications have been introduced, the program is able to perform thenecessary calculations to present an optimized solution to the proposed problem. Whenthese calculations end, the second window (Figure 2.12) appears showing the results. Theblue circles around the base station represent the different modulations allowed by the IEEE802.16 standard and the distances covered by each of them. There are still some parametersthat the user can modify, in order to choose the configuration that best suits his problem,such as the channel width and the number of sectors of the antenna. Whenever there is afield whose text is red, as happens in the figure, it means the solution is not feasible and theuser must change the parameters.

Figure 2.12: WiMAX Base Station Planning Tool: calculation result

2.5 Summary

After an overview of the evolution of the IEEE 802.16 standard and the different accessmethods it defines, especially the mesh mode, as well as the basics of propagation, modulationand the IEEE 802.16 MAC layer, we are now ready to explore some features of the standard.

The introduced topics on propagation and modulation schemes will be applied to thedevelopment of the necessary algorithms to solve WiMAX planning problems.

The explanation about the Base Station Planning Tool will be useful for the comprehensionof the next chapter, once some of the studied/developed topics will be implemented in thattool.

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Chapter 3

IEEE 802.16 Performance andEnhancement Techniques

As said before, one of the purposes of this work was to finish the development of a WiMAXbase station planning tool. Mobile WiMAX along with the enhancement techniques discussedhere were some of the features that were not yet implemented, so we performed the necessarystudies to include them in the application.

First, Section 3.1 presents a performance comparison between Fixed and Mobile WiMAXby analyzing the type of terrain and sectoring influence in each technology and also inspectingthe trade between range and capacity. Section 3.2 gives an overview of the enhancementtechniques along with the necessary calculations to be performed and, Section 3.3 showssome results and comparisons in terms of range and capacity and their relation with the typeof terrain.

Finally, Section 3.4, presents the chapter conclusions.

3.1 Performance Comparison: Fixed and Mobile WiMAX

As stated before, there are two distinct access technologies within the IEEE 802.16 stan-dard: Fixed and Mobile WiMAX. These two technologies have already been described anddistinguished in the last chapter, so our purpose now is to demonstrate its implementation inthe base station planning tool and the practical differences in terms of coverage and capacity.

3.1.1 Primary comparison

For a primary comparison, we left all the values in the tool as the default suggests (Tables2.4, 2.5 and 2.6) except for the density of users which we considered to be 2.5 usr/Km2. Onlythe residential traffic profile has been activated and the channel is 20 MHz wide.

The results are shown in Figure 3.1 and Table 3.1. As we can see, for the same scenario,Mobile WiMAX performs better than Fixed WiMAX, mainly due to the SOFDMA technique.

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(a) Fixed Wimax BS (b) Mobile Wimax BS

Figure 3.1: Comparison between Fixed and Mobile WiMAX

Table 3.1: Results for Fixed and Mobile WiMAXNumber of users Range (Km)

Fixed WiMAX 55 2.91Mobile WiMAX 62 3.11

3.1.2 Type of Terrain influence in Fixed and Mobile WiMAX

As explained in Section 2.3.3, the SUI model is used to account for the propagation pathlosses in three terrain types. This section compares the influence of the type of terrain inFixed and Mobile WiMAX. We have considered the maximum range for each technology andtype of terrain with a fixed 3.5 MHz wide channel regardless of the density of users. Table3.2 and Figure 3.2 show the comparison between Fixed and Mobile WiMAX.

Table 3.2: Results for the three types of terrain

Type of Terrain Range for Fixed (Km) Range for Mobile (Km)Rural 7.81 11.23

Suburban 5.36 7.52Urban 3.60 4.88

As we can see, once again Mobile WiMAX proves to perform better, but the greatestdifference is indeed in the rural type of terrain, where the difference is of 3.42 Km, which isalmost the range of Fixed WiMAX performing in urban scenarios.

3.1.3 Sectoring in Fixed and Mobile WiMAX

Sectoring is a powerful antenna design technique, which allows dividing one antenna intovarious sectors, each one operating at a different frequency thus maximizing the transmittingpower while minimizing interference. The radiation patterns of a sectorized antenna beam

24

1 2 30

2

4

6

8

10

12Type of Terrain and Max. Range

Max

. Ran

ge (

Km

)

Type of Terrain

Fixed WiMAXMobile WiMAX

Rural UrbanSurburban

Figure 3.2: Influence of the type of terrain in Fixed and Mobile WiMAX

Figure 3.3: Horizontal and vertical radiation patterns

are shown in Figure 3.3.We used the same scenario to account for the growth of capacity of a base station with

the number of sectors in Fixed and Mobile WiMAX. The results are shown in Table 3.3 andFigure 3.4.

Table 3.3: Capacity growth with the number of sectors in Fixed and Mobile WiMAX

Nr. Sectors Cap. Fixed (Mbps) Cap. Mobile (Mbps)1 5.68 14.402 11.36 28.803 17.04 43.204 22.72 57.605 28.40 72.006 34.09 86.40

25

1 2 3 4 5 60

10

20

30

40

50

60

70

80

90

Number of sectors

Cap

acity

(M

bps)

Number of sectors and Capacity


Figure 3.4: Number of sectors and capacity for the same scenario

As we can see, the capacity grows linearly with the number of sectors, so, as each MobileWiMAX sector has more capacity than each Fixed WiMAX sector, it is clear to see that inimplementations where sectoring is possible, Mobile WiMAX has better performance.

3.1.4 Range vs. Capacity

We will now account for the trade between range and capacity of a base station in threetypes of terrain (Rural, Suburban and Urban) for Fixed and Mobile WiMAX. We have con-sidered a 5 MHz wide channel and 1 sector only antennas to calculate the range and capacityof the cells. All the parameters remain the same during the simulation, except for the tech-nology, which can be Fixed or Mobile and the type of terrain. Figures 3.5(a), 3.5(b) and3.5(c) show the results.

Watching the results, we can say that, although the differences in range and capacityare obvious for the two variants of WiMAX as we had seen before, the relation of the tradebetween range and capacity for Fixed and Mobile WiMAX does not vary with the type ofterrain considered.

3.2 Enhancement Techniques

The IEEE 802.16 standard defines some techniques to enhance the WiMAX transmission.This section will start by describing two possible implementations of MIMO in Section 3.2.1followed by SIMO in Section 3.2.2. Section 3.2.3 presents the adaptive beamforming approachand finally Section 3.3.1 shows some results and comparisons concerning these methods.

3.2.1 MIMO

MIMO stands for Multiple Input Multiple Output, what, in wireless communications,means multiple transmitters and multiple receivers. MIMO can be used to achieve higher

26

2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.56

8

10

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22Range vs Capacity for Rural scenarios

Range (Km)

Cap

acity

(M

bps)


(a) Rural scenario

2 2.5 3 3.5 4 4.5 56

8

10

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22Range vs Capacity for Suburban scenarios

Range (Km)

Cap

acity

(M

bps)


(b) Suburban scenario

1.5 2 2.5 38

10

12

14

16

18

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22Range vs Capacity for Urban scenarios

Range (Km)

Cap

acity

(M

bps)


(c) Urban scenario

Figure 3.5: Range vs. Capacity for the different types of scenarios

(a) MIMO scheme (b) SIMO scheme

Figure 3.6: Multiple antenna techniques

data rates or to fight adverse channel conditions depending on the chosen method.

Spatial Diversity

MIMO can be used to improve the receiver sensitivity through the spatial diversity scheme.By using multiple transmitters sending the same information and multiple receivers, we can

27

achieve a gain in sensitivity called diversity gain. If NT and NR are, respectively, the numberof antennas at the transmitter and at the receiver, the theoretical gain can be calculated by(3.1) [2].

SRgain = 10 ∗ log(NT ×NR) [dB] (3.1)

Spatial Multiplex

On the other hand, if there is no need to improve the sensitivity on the receiver, MIMOcan be used to achieve high data rates through spatial multiplexing. Spatial diversity consistson sending, at the same time, different information on each antenna. Using this scheme wecan improve the capacity of the link. Theoretically, the capacity grows linearly with theminimum of NT and NR [2], as shown by (3.2).

Cgain = min(NT , NR) (3.2)

Figure 3.6(a) shows an example of MIMO.

3.2.2 SIMO

Single input multiple output (SIMO) is a particular case of MIMO, represented in Figure3.6(b), where the transmitter has multiple antennas and the receiver only has one. Spatialdiversity is the only application of SIMO once the receiver only has one antenna and thuscannot receive two different symbols at the same time. For a SIMO system the theoreticalgain can be determined by (3.3).

SRgain = 10 ∗ log(NT ) [dB] (3.3)

3.2.3 Adaptive Beamforming

Adaptive beamforming consists on directing the antenna beam to the best position inorder to assure the best channel conditions, range and power saving at the SS/MS side. Inorder to perform beamforming, the antenna must have at least two elements. As shown inFigure 3.7, using an appropriate algorithm and some extra hardware the antenna can focusits power in a certain point, thus providing better channel conditions and higher range there.As the transmit power is increased, the MT’s effort to receive the signal is less, needing lesspower to operate. Gains in the uplink and downlink are different, but significant in both, andcan be calculated based on the number of elements of the antenna (N) by (3.4) and (3.5).

ULgain = 10 ∗ log(N) [dB] (3.4)

DLgain = 20 ∗ log(N) [dB] (3.5)

3.3 Enhancement Techniques comparison: Scenarios and Re-sults

Now that we are able to distinguish the various WiMAX enhancement techniques, andthey are introduced in the planning tool, some results are shown here.

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Figure 3.7: Adaptive beamforming

3.3.1 Primary Comparison

We will start by comparing the benefits of each enhancement technique, so all the trafficparameters will be left with their default values, indicated in Tables 2.4, 2.5 and 2.6. Theconsidered channel size is 20 MHz, the antennas only have one sector and the implementationis Mobile WiMAX in a rural environment. The results are presented in Table 3.4.

Table 3.4: Results for the various enhancement techniques

Enhancement Nr of users Cell radius (Km)– 46 3.46

MIMO 4x2 Spatial Diversity 82 4.61MIMO 4x2 Spatial Multiplex 93+1 3.46Adaptive Beamforming 3 el. 90 4.83

SIMO 2x1 54 3.72

By looking at Figure 3.8 and Table 3.4 we can see that, while with MIMO spatial multiplexonly the number of users is greatly increased, the other enhancement techniques allow theincreasing of the number of users and the radius of the cell. This happens because MIMOspatial multiplex only increases the capacity of the link, without interfering with the sensitivityof the transmitter/receiver. It is good if we have good channel conditions, but in case wehave bad channel conditions, improving the sensitivity may be preferable, by using one of theother techniques. We can conclude that, for this general scenario, Adaptive Beamforming isthe most effective technique, but the implementation heavily depends on the place where weare settling the BS.

3.3.2 Range Improvement for Fixed and Mobile WiMAX in Rural Scenar-ios

Now that we have an idea of the improvements that each enhancement technique brings,we will compare the efficiency, in terms of range, of SIMO and MIMO spatial diversity in

1This number could be higher, once the resources are not pushed to the limit, but we would need to modifythe parameters of simulation, what would not be good for the comparison of the techniques, so we preferredto represent the maximum number of users for this conditions.

29

3.4 3.6 3.8 4 4.2 4.4 4.6 4.8 545

50

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65

70

75

80

85

90

95Enhancement Techniques comparison

Cell radius (KM)

Num

ber

of u

sers

MIMO 4x2 SM

No enhancement

MIMO 4x2 SD

Adaptive Beamforming 3 el.

SIMO 2x1

Figure 3.8: Enhancement techniques comparison

Fixed and Mobile WiMAX. Adaptive beamforming has not been considered, once it is onlyavailable for Mobile WiMAX, thus we could not compare the results with Fixed WiMAX. Wehave considered a rural environment, because in this type of terrain the differences are moreevident, and a channel width of 3.5 MHz. The results are displayed in Table 3.5 and Figure3.9.

Table 3.5: Results for range improvement in Fixed and Mobile WiMAX

Fixed WiMAX (Km) Mobile WiMAX (Km)SISO 7.81 11.23

SIMO 2x1 8.53 13.46MIMO 4x2 S.D. 23.21 33.36

As we had already seen, from the three techniques tested, MIMO 4x2 spatial diversity isthe most effective in range improvement. For Mobile WiMAX the achievements in terms ofrange proved to be even higher than for Fixed WiMAX, for every case tested, although theresults are more evident in MIMO.

3.3.3 Range Improvement vs. Type of Terrain

It is important to take into account the type of terrain where we are settling a BS beforechoosing an enhancement technique to improve the performance. We will now compare thevarious techniques performance according to the type of terrain. For this to be possible weused the Mobile WiMAX technology, which proved to achieve higher improvements, as statedin the last section with a 3.5 MHz wide channel. The antennas have 3 sectors.

30

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35Range Improvement in Rural Scenarios

Enhancement Technique

Max

imum

Ran

ge (

Km

)


SISO SIMO 2x1 MIMO 4x2 S.D

Figure 3.9: Comparison between the implementation of the enhancement techniques in Fixed andMobile WiMAX

1 2 30

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30

35

Type of Terrain

Max

imum

Ran

ge (

Km

)

Range improvement vs Type of Terrain

SISOSIMO 2x1MIMO 4x2 S.D.Adapt. Beamforming 3 el.

Rural Suburban Urban

Figure 3.10: Range improvement of each technique for each type of terrain

As we can see in Figure 3.10, it is in the rural type of terrain that the achievements interms of range are higher. If we take a look at Figure 3.11, we can conclude that, althoughthe achievements due to the implementations of SIMO 2x1 do not depend on the type ofterrain, for MIMO 4x2 spatial diversity and Adaptive Beamforming with 3 elements, thereare variations with the type of terrain, especially in MIMO. Therefore, these results must betaken into account when choosing the technique to implement.

At a first look, these results may seem contradictory when compared to the ones presentedin Figure 3.8: in that figure, Adaptive Beamforming has higher improvement in terms of rangethan MIMO 4x2 S.D.; however, it is important to remember that in this section we presentthe maximum absolute ratings for each technique in terms of range, regardless of the number

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250

300

Type of Terrain

Impr

ovem

ent i

n %

Improvement related to SISO in %

SIMO 2x1MIMO 4x2 S.D.Adapt. Beamforming 3 el.

Rural Suburban Urban

Figure 3.11: Range improvement of each technique for each type of terrain compared to SISO in %

of users, and in Section 3.3.1 we are limited to the number of users, or, by other words, thecapacity.

3.3.4 Capacity Improvement for Fixed and Mobile WiMAX in Rural Sce-narios

The only enhancement technique that improves capacity is MIMO spatial multiplex, sothis section is dedicated to the comparison between the improvements in Fixed and MobileWiMAX. Once again we used a rural scenario, because it is where the differences are greaterand a channel width of 3.5 MHz. The comparison is made based on the variation of thecapacity for the QPSK 1/2 modulation. This choice has not been done for any special reason,because we can observe the improvement in any of the modulations.

1 20

5

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25

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45Capacity improvement for Fixed and Mobile WiMAX

Enhancement Technique

Cap

acity

(M

bps)


SISO SIMO 4x2 S.M.

Figure 3.12: Capacity improvement with MIMO 4x2 S.M. in a rural environment

32

Figure 3.12 shows that once again, the achieved improvements are greater in MobileWiMAX.

3.3.5 Capacity Improvement vs. Type of Terrain

The results presented in the last sections would lead us to think that in the same waythat the type of terrain influences the improvement in range, it would affect capacity also.We have performed some tests for capacity improvement with MIMO 4x2 spatial multiplexin the various types of terrain, which we will now present. The conditions of calculation arethe same of last section, except for the type of terrain, which is now our variable.

1 2 30

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45

50

Type of Terrain

Cap

acity

(M

bps)

Capacity Improvement for each Type of Terrain

SISOMIMO 4x2 S.M.

Suburban UrbanRural

Figure 3.13: Capacity improvement with MIMO 4x2 S.M. for each type of terrain

1 2 30

50

100

150

200

250Improvement related to SISO in %

Type of Terrain

Impr

ovem

ent i

n %

MIMO 4x2 S.M.

Suburban UrbanRural

Figure 3.14: Capacity improvement with MIMO 4x2 S.M. related to SISO for each type of terrain in%

33

As we can see in Figures 3.13 and 3.14, although the improvement in capacity with MIMO4x2 spatial multiplex is affected by the type of terrain, this difference is not so significant.

3.4 Conclusions

From Section 3.1 we can conclude that generally Mobile WiMAX performs better thanFixed WiMAX in terms of coverage and possible number of users in a cell. Moreover, it hasthe capability to allow users to move, which is a great evolution.

We have also presented the various enhancement techniques and distinguished the benefitsof each one. We concluded that in a generic scenario, the Adaptive Beamforming technique isthe most effective. Since the behavior of these techniques is heavily dependent on the terrainconditions, we compared some results for different types of terrain. In terms of range, SIMOproved to perform almost equally for every type of terrain, while MIMO 4x2 S.D. reportedlarge differences. Adaptive Beamforming shows some slight difference between each type ofterrain, but indeed less than the last one. In what concerns capacity, the type of terrainproved not to interfere much with the MIMO spatial diversity technique.

These studies will certainly be useful for a possible deployment of a BS, and proved thatin order to get the best performances, a previous study on the terrain is really important.

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Chapter 4

802.16 Mesh Mode Planning

This section presents an algorithm to perform mesh planning that will be implemented inthe software exposed in Chapter 5. There are, at least, two ways to solve a mesh planningproblem: by exact linear programming and by heuristics. Linear programming solutions areexact, but require extensive knowledge of modeling languages. The reason for the developmentof this algorithm, which is a heuristic for the mesh planning problem, is to provide a way ofsolving the problem, without relying on linear solvers, and difficult programming, which leadto inflexible and hard to configure scenarios.

This Chapter will give you an overview of the problems of mesh planning in Section 4.1,followed by the explanation of our interference-aware model in Section 4.2. Section 4.3 definesthe two basic elements of our mesh planning problem formulation and their properties, alongwith the way internal/external traffic is handled. In Section 4.4 we present our representationof scenarios, which will be used in the whole work. Section 4.5 introduces the developedalgorithm and Section 4.6 presents a mechanism to optimally place candidate sites. In Section4.7, we present our approach to integration WiMAX-Wi-Fi and Section 4.8 is dedicated to thelinear programming model that will be used to compare our algorithm’s results and accountfor its accuracy.

Finally, Section 4.9 shows the conclusions.

4.1 Challenges of mesh planning

Mesh planning is still a quite difficult topic to deal with. Once traffic travels around thenetwork until it can find its way and can follow many different routes which will be congestedor not, according to the users’ utilization of the network, it turns difficult to know how muchbandwidth will be needed for a specific connection. Though this can be a problem, we canestimate the bandwidth of the connection by taking into consideration all the nodes that willpossibly interfere with it.

As referred in Section 2.1.3, there are different types of elements in a mesh network. MAPsrequire a connection to the wired backbone and so are more expensive than MRs. Using asless MAPs as possible can turn the deployment of a network cheaper, which is one of themain objectives of the deployer. Having a good overview of the traffic on the distinct areasof the network and a good positioning of the MRs and MAPs can help this objective. This

35

is the aim of the algorithm presented in this section, as well as the software produced in thisscope and described in Chapter 5.

4.2 Interference-aware model

As stated before, the IEEE 802.16 standard only considers two hop interference in meshmode operation. This means that both traffic and control messages more than two hops awayfrom each node are not taken into account when planning the network. This may not be aproblem when talking about a little network with a few nodes, but in large networks, this willcertainly be a trouble. To address this issue, we can try to take into account all the nodeswhen planning this type of networks. It will lead to more robust networks that can provideservice to everyone, under the most severe conditions planned.

Figure 4.1 illustrates a network planned with a two hop interference-aware model. Imaginethat the network is in normal operation: traffic from MR1, MR2 and MR3 is routed throughMR1 and finally MAP1 to the wired backbone. MAP1 is supposed to be able to handle MR1,MR2 and MR3’s traffic, but it does not know anything about MR4 because it is more thantwo hops away. If MR4 users push their connections to the limit, MR2 and MR3 will haveto route its traffic through MR1 and MAP1, but MAP1 may not be capable of handling somuch traffic.

Figure 4.1: Two hop interference-aware model

We will use some numbers to explain the problem. MR2 and MR3 are the limits ofknowledge of the network of MAP1, so when planning the network, MAP1 will be provided,on the limit, a 3 Mbps connection to MR1, which is the sum of the traffic coming from MR1,MR2 and MR3. The other connections are planned according to the same method and so weget the network depicted in Figure 4.2. Now imagine that MR4 users suddenly start pushingtheir connections to the limit (2 Mbps). Consider also that MR2 and MR3 are using 0.3Mbps of their maximum of 0.5 Mbps and MR1 1 Mbps out of 2 Mbps. This would give us1.3 + 1.3 + 2 Mbps at MR1, so the connection between MR1 and MAP1 would need to have3.6 Mbps.

The solution for this is, as said before, to take all the nodes into account when planningthe network. This will ensure that every element of the network can handle the traffic sent to

36

Figure 4.2: Two hop interference-aware model with some possible values

it and route it appropriately. Figure 4.3 shows a possible configuration for the network usingthe proposed model.

Figure 4.3: Interference-aware model considering all nodes’ traffic with some possible values

The presented model is used in the developed software, presented in Chapter 5.

4.3 Traffic and Candidate sites definition and classification

There are two distinct data elements in a mesh network planning problem: Traffic Points(TP) and Candidate Sites (CS), which will be explained in detail in Sections 4.3.1 and 4.3.2.In order to keep these elements organized and to establish the proper relations between them,some ordered lists are also useful, mainly a list of Traffic Points, a list of Candidate Sitesand also a sorted list of candidate sites, whose sorting method may vary, according to theplanning requirements.

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4.3.1 Traffic Points

Traffic points are areas where users are expected to be and which have their own bandwidthrequirements based on the users’ needs. In order to correctly address these bandwidth needs,two traffic profiles can be defined for a traffic point: residential and business, as the trafficdemands are different and both can exist in the same place. Within each of these profiles, itmust still be possible to separate day and night, because bandwidth demands may have greatdifferences in these two periods.

We consider seven popular services in order to describe the users’ bandwidth requirements:VoIP, data, IPTV, media stream, online gaming, P2P and video conference. For each of theseservices, some typical bandwidth usage values are suggested in Table 4.1.

Table 4.1: Typical bandwidth usage for some services

Service BandwidthVoIP 80Kbps

Data - Residential 1000KbpsData - Business 2000Kbps

IPTV 2000KbpsMedia Stream 20KbpsOnline Gaming 85Kbps

P2P 500KbpsVideoconference 385Kbps

Besides the bandwidth, there are delay, jitter and other requirements that need to beachieved, for each technology. The WiMAX QoS scheme ensures that by separating thetraffic into five classes: UGS, rtPS, nrtPS, BE and ertPS [5] [8]. Table 4.2 shows the trafficclass for each considered service.

Table 4.2: Traffic class for each considered serviceService Traffic Class

VoIP UGSData BEIPTV rtPS

Media Stream rtPSOnline Gaming rtPS

P2P nrtPSVideoconference rtPS

As said before, there are two traffic profiles: residential and business. For each of theseprofiles the bandwidth requirements are defined in terms or number of utilizations and dura-tion of each one over two periods: day and night. Some typical values are also proposed onTable 4.3 and Table 4.4.

We consider that the utilizations of each service follow a Poisson distribution with rateλ and have a service time exponentially distributed of 1

µ . This way, as the system can be

38

Table 4.3: Typical usage values for the residential profile


VoIP 7 5 2 5Data 6 40 4 20IPTV 2 60 1 100

Media Stream 1 20 0 0Online Gaming 1 60 0 0

P2P 1 30 0 0Video conference 1 30 0 0

Table 4.4: Typical usage values for the business profile


VoIP 30 2 5 2Data 20 10 0 0

Video conference 3 60 0 0

modeled by a M/M/1 queue system, we can calculate the traffic intensity ρ, for each servicex as

ρx =λx

µx(4.1)

Finally, we need to calculate the traffic intensity in terms of capacity,

Cx = ρx ∗Rx (4.2)

where Rx is the bandwidth requirement for service x.

The size and shape of a traffic point are also important properties to characterize it, thattogether with the downlink:uplink (DL:UL) ratio and the density of users allow us to calculatethe amount of traffic a TP holds. Typical values for the density of users are presented onTable 4.5.

Table 4.5: Typical values for density of users

Density (usr/Km2)Residential 1.5Business 0.5

Let DB and DR be the density of business and residential users, respectively, and H andW the height and width (in case of a rectangular shape) or R the radius (in case of a circularshape) of the traffic point. The number of users Nu of a TP is then given by Equation 4.3.

Nu =

{DB ∗H ∗W + DR ∗H ∗W for a rectangular shapeDB ∗ π ∗R2 + DR ∗ π ∗R2 for a circular shape

(4.3)

39

The position of the TP is the attribute that will define to which CS it will be assignedand shall, therefore, be correctly determined.

A list of closer CSs is useful for the possible algorithm to operate, being the only way toinfer which CSs can reach a TP. Finally, by having a list of connected CSs we can keep trackof all the CSs to which a TP is assigned.

In resume, the essential items to describe a traffic point are:

• The bandwidth requirements of the most popular services in a network,

• Detailed information about the users’ utilization of the connection,

• The size and shape of the TP,

• The DL:UL ratio,

• The positions of the TPs.

4.3.2 Candidate Sites

Candidate sites are places where a MR or a MAP can be installed. These must be correctlyconfigured with the different types of technology allowed by the IEEE 802.16 standard, inorder to best suit the users and the place where they are installed.

For each candidate site we must choose between Fixed or Mobile WiMAX. Using theenhancement techniques introduced in Chapter 3 must be considered and the physical pa-rameters must be correctly configured. Table 4.6 shows some typical values for the candidatesite physical configuration.

Table 4.6: Typical configuration values for a candidate site

Parameter SpecificationImplementation Fixed WiMAX

Enhancement Techniques SISO, SectoringHeight Tx Antenna 45mHeight Rx Antenna 3m

Frequency 3.5GHzTx Power 35dBmTx Gain 18dBiRx Gain 16dBi

Available BW 27MHzSectors 1

Number of radios 2

The implementation parameter allows the selection of Fixed WiMAX or Mobile WiMAX.As stated in Chapter 2, there are many differences between the two types of technology, sowe must make the right decision, based on the purpose of the network deployment, beforewe proceed on the planning. Using any of the enhancement techniques presented or evenmore than one may be a solution for obtaining higher spectral efficiency, thus leading to less

40

operating devices. The height of the transmitter antenna Tx and the receiver Rx influence thepropagation model, as referred in Section 2.3.3, so these must also be a case of study. The Tx

power and Tx and Rx gains are specifications of the equipment in use and can, sometimes beslightly changed. The operation frequency and available bandwidth values must be selectedaccording to each country’s regulations. It is highly recommended that each mesh node hasat least two radios: one for backhaul and another for access, since one radio solutions haveproved to perform poorly [19].

Once again, the position of the CS is a determining attribute. It will define which TPscan be served by him and to which CSs it can communicate.

As for the TPs, it is also useful to have a list of closer CSs that will be the way to knowwho can reach who. It is equally appropriate to keep track of the connected TPs as well asthe connected CSs, so for each CS we must have a list of connected TPs and CSs.

4.3.3 Internal and External Traffic

As for most of the networks, in a mesh network the traffic can be divided into internaland external. In this case, internal traffic is the one that is intended to reach a location inthe same network and so will not need to reach a gateway to another network: a MAP. Inopposition, external traffic will need to be delivered to a MAP.

In our work we consider that the rate of internal/external traffic must be a configurableparameter, once each network has its characteristics. We account for this distinction in traffic,by assuming, without loss of generality, that a part of all the traffic that arrives at a CS willnot need to be retransmitted and is intended to be delivered to its directly connected clients.Thus, if IC represents the incoming traffic and IT ∈ [0 . . . 1] the amount of internal traffic,the traffic that needs to be retransmitted can be calculated by (4.4).

RT = IC × (1− IT ) (4.4)

We except from this generalization the CSs that have no TPs connected, in which weconsider that, naturally, all the traffic must be forwarded.

4.4 Scenario Description and Configuration

In this section we will propose a way to represent the scenarios and results of a meshplanning problem, which will be used throughout the rest of this work.

Figure 4.4 shows the various types of symbols that will be used to describe the scenarios.

41

(a) Planning scenario (b) Results

Figure 4.4: Scenario representation

4.5 Mesh Planning Algorithm

After all the CSs and TPs are defined, we can perform the necessary calculations to obtaina solution for the problem. We have developed an algorithm, which can be resumed in thefollowing four steps as shown in Figure 4.5:

1. Set closer CSs

2. Fill ordered list of CSs

3. Assign access

4. Assign backbone

which will be explained after a brief familiarization with the variables used.

Let us consider a network scenario. Let S = {1, .., m} denote the set of candidate sitesand I = {1, .., n} the set of traffic points. A special node N represents the wired backbonenetwork. The cost associated to installing a MR in CS j is denoted by cj , while the additionalcost required to install a MAP in CS j is denoted by pj , j ∈ S. Therefore, the total cost ofinstalling a MAP is given by (cj + pj). In our case, we consider that installing a MAP is tentimes more expensive than a MR, but this value can be readjusted.

The traffic generated by each TP i is given by parameter di, i ∈ I, whereas the traffichandled by CS j is given by tj . The traffic capacity of the wireless link between CSs j and lis denoted by ujl, j, l ∈ S, while the capacity of the radio access interface of CS j is denotedby vj , j ∈ S. The amount of internal traffic is denoted by IT and can range from 0 to 1. Foreach TP i in I and CS k in S, STP

i = {j(i)1 , j

(i)2 , . . . , j

(i)

LTPi} denotes the ordered subset of CSs

that can cover i, and SCSk = {j(k)

1 , j(k)2 , . . . , j

(k)

LCSk

} the ones that can cover i where the CSs are

ordered according to the non-increasing received signal strength. O = {j1, j2, . . .} stands for

42

Figure 4.5: Algorithm scheme

43

the ordered list of CSs sorted from the most desirable CS to be turned into a MAP to theleast desirable.

The connectivity parameters define which network elements can be connected throughwireless links. These connectivity parameters must be determined a priori, based on thepropagation parameters for each case. There is a coverage parameter:

aij =

{1 if a MAP or MR in CS j covers TP i

0 otherwise,(4.5)

for each pair i ∈ I, j ∈ S, and the wireless connectivity parameters:

bjl =

{1 if CS j and l can be connected with a link0 otherwise,

(4.6)

for each j, l ∈ S.

Decision variables of the problem include the TP assignment variables xij , i ∈ I, j ∈ S:

xij =

{1 if TP i is assigned to CS j

0 otherwise,(4.7)

installation variables zj , j ∈ S:

zj =

{1 if a MAP or MR is installed in CS j

0 otherwise,(4.8)

wired backbone connection variables wjN , j ∈ S (if zj = 1, wjN denotes if j is connected tothe wired network N, i.e, if it is a MAP or a MR:

wjN =

{1 if a MAP is installed in CS j

0 otherwise,(4.9)

wireless connection variables yjl, j, l ∈ S:

yjl =

{1 if there is a link between CS j and l

0 otherwise,(4.10)

gij , j ∈ S, i ∈ I denotes the distance between elements i and j ∈ S ∪ I, Cik the total capacity,

Rik the free capacity and hi

k the range for modulation k, k ∈ {1 . . . 7} for CS i, as depictedin Figure 4.6 and explained in Section 2.3.3. These distances can be determined according toexpression (2.14).

Finally flow variables fjl which denote the traffic flow routed on link (j, l), where thespecial variable fjN denotes the traffic flow on the wired link between MAP j and the backbonenetwork.

44

Figure 4.6: Modulation scheme

4.5.1 Step 1: Set closer CSs

In this step, the lists of closer CSs, SCSj ∀j ∈ S and STP

i ∀i ∈ I, are filled based onthe distance and reachability. Actually, the list is filled with all the candidate sites that arereachable from that location and in the end is sorted by distance. The reachability is decidedhaving for base the calculations presented in Section 2.3.3. As told before, aij accounts forthe reachability between TP i and CS j and bjl for the reachability between CSs j and l. aij

can be defined by expression 4.11 and bjl by 4.12. It is also important to know which CSscan each one reach, in order to determine who is able to route the others traffic.

aij =

{1 gij ≤ hi

7

0 gij ≤ hi7

∀i ∈ I, j ∈ S (4.11)

bjl =

{1 gjl ≤ hj

7

0 gjl ≤ hj7

∀i, j ∈ S, j 6= l (4.12)

In the case of traffic points, we consider that once the center of the traffic point is covered,all of it is covered too. Remember that to have an accurate planning, the traffic points mustbe as little as possible, in order to correctly address the users’ requirements. For instance, itis preferable to have 5 TPs of 20m2, which describe the traffic for 5 people each than a TPwith 100m2 which considers that the 25 people all have the same bandwidth requirements.

Figure 4.7 and Algorithm 4.1 depict the process.

In the beginning, the first TP in the list of TPs is selected. All the CSs will be tested tocheck if they can reach this TP, so, each one will be selected at a time and added to the TPs

45

closer CSs list in case it is able to reach it. When all the CSs are tested, the list is sorted,putting the closer ones at the top. These actions will be repeated until all the TPs are tested,then it will be repeated for every CS (right side of Figure 4.7).

foreach i ∈ I doforeach j ∈ S do

if gij < hj7 then

add CS i to STPi

endsort STP

i in the ascending order with distanceend

endforeach j ∈ S do

foreach k ∈ S, j 6= k doif gjk < hj

7 thenadd k to SCS

j

endsort SCS

j in the ascending order with distanceend

endAlgorithm 4.1: Algorithm for step 1

4.5.2 Step 2: Fill ordered list of CSs

There is a list of all the CSs that, depending on the planning characteristics, may beordered in two ways: by the distance to each of its closer CSs or by the number of CSs it canreach. This list and its sorting method have a great role in the final solution, because theywill determine which are the most desirable CSs to be turned into MRs or MAPs. So, for thesolution to be optimal, we must watch our network and decide which of the sorting methodswill serve us better, or, alternatively, try both methods and check what solution is the best.

Distance based method

Using this sorting criterion, the distance D to every TP a CS can reach is calculatedand summed. Then, it is divided by the number of reachable TPs, which gives us the meandistance to every TP, as stated by (4.13). The list is ordered using this value, putting theones with lower mean distance ate the top.

Dj =∑

i

gij ∗ aij

LTPj

∀i ∈ I (4.13)

Number of reachable TPs method

Another possibility is to sort the list by the number of TPs every CS can reach, puttingthe ones with the highest value at the top. Let Ek be the number of TPs CSk can reach,

Ek =∑

i

aki ∀i ∈ I (4.14)

46

Figure 4.7: Step 1 scheme

47

The list is sorted based on the Ek value for each CS k.

Example

In the scenario depicted in Figure 4.8, where we consider that the whole traffic can behandled by one CS and the reachable area of each CS is represented by the ellipses, the firstmethod would put the CSs in the following order: A, C, B, thus turning CS A into the mostdesirable to the most desired to be a MAP. This happens because A, along with C, have thesmallest mean distance to their closer CSs. However, this is not the correct decision, once thisway, CS C must also be activated, because the users on the right cannot be reached. Usingthe second method, the order would be: B, A, C, because CS B can reach all the TPs, whereasA and C can only reach one each. This, way, only B would be activated, which gives us onlyone device, instead of 2, given by the other method. This is an example of the differencebetween both methods where the second method has a better performance, but other casesexist where the first one is the best.

Here are the sorting parameters calculated for each case:

DA =0.5 ∗ 1 + 0.5 ∗ 0

1= 0.5 EA = 1 + 0 = 1

DB =2 ∗ 1 + 2 ∗ 1

2= 2 EB = 1 + 1 = 2

DC =0.5 ∗ 1 + 0.5 ∗ 0

1= 0.5 EC = 0 + 1 = 1

Figure 4.8: Sorting method example

4.5.3 Step 3: Assign access

As illustrated by Figure 4.9 and Algorithm 4.2, in this step, every TP will be assigned to,at least, one CS. As said before, every TP maintains a list of its closer CSs, so the processis done iteratively: for each TP, the list of ordered CSs and the list of closer CSs are crossedto check which is the best and closer CS to handle its traffic. After this, the selected CS isqueried to check if it can handle the whole traffic. If it does, the traffic is assigned and theprocess ends for this TP and is repeated for the next. If not, the lists are consulted againand the remaining traffic is assigned to another CS and so on, until all the traffic is assigned.In case there is a TP whose traffic cannot be handled, more CSs must be introduced in thatzone, until there is a solution. When this operation is completed for all the TPs, the traffic

48

parameters of every CS must be recalculated, so that the occupied bandwidth at every CS isknown.

double U // unassigned trafficint rforeach i ∈ I do

U = di

r = 0while U != 0 do

if Rj(i)r

k ≥ U thenAssign U to j

(i)r

elseAssign the possible traffic to j

(i)r

U = U - Rj(i)r

k

r++end

endend

Algorithm 4.2: Algorithm for step 3

4.5.4 Step 4: Assign backbone

Now that all the access traffic is assigned, it is time to find a way for all the CSs traffic,which means, all the control traffic and the TPs’ traffic handled by each CS, to be deliveredto its destination. For this to be possible, some CSs must be turned into MRs and others intoMAPs.

When the process starts, the first CS in the list of ordered CSs is turned into a MAP.Once this is, according to the user’s choice, the first in the ordered list, it is clear that thismust be the best candidate to be our first MAP. From now on, every CS will be checked to seeif its traffic, or at least a part of its traffic, because some traffic is internal, can be routed to aMAP through its neighbors. As said in the beginning of this section, tj denotes the amountof traffic CS j handles and Ri

k the free capacity of CS i in modulation k. Through expression(4.15) we can calculate Fj , the CS j’s neighbors free capacity

Fj =∑

i

Rik ∀i ∈ STP

j (4.15)

k is assumed to be the right modulation, which can be determined by the distance betweenthe two CSs, gij .

Using this value, we can decide if it is possible to route CS i’s traffic through its neighborsor not, as described by equation (4.16).

wjN =

{1 tj > Fj

0 tj ≤ Fj

∀j ∈ S (4.16)

If it is possible, the traffic is assigned to the corresponding CS or CSs, and, consequently,the free bandwidth at the CS’s will be reduced. In case it is not possible, and only in that

49


50

case, the CS is turned into a MAP. It is clear to see that the CSs’ bandwidth is continuouslybeing reserved and that no new MRs or MAPs are created unless it is strictly needed. Likewhen you are filling a bottle with water, no spaces are left behind. This operation repeatsuntil all the traffic intended to go out of the network from every CS can be routed to thebackbone network, as shown by Figure 4.10 and Algorithm 4.3.

wO1N = 1foreach j ∈ S do

if Fj ≥ tj thenT = CS j’s received trafficif CS j has no TPs connected then

assign T to CS j’s neighborselse

assign T × (1− IT ) to CS j’s neighborsend

elsewjN=1

endend

Algorithm 4.3: Algorithm for step 4

4.6 Optimally Placing Candidate Sites

The previous sections considered that CSs would be placed manually. This section willpresent a mechanism to place the candidate sites in an optimized position. In this sense, theoptimal planning deals also with the optimal placement of CSs.

First let us define the following variables: mc is the mean capacity of each CS, nl theminimum distance for two TPs to be considered neighbors and sp the space between CSsserving the same set of TPs.

We start by defining sets of TPs: Tk, k ∈ 0 . . . Nsets. These sets are formed accordingto the distance between TPs, so each set of TPs will only contain TPs not farther than nl.Then we calculate the total traffic generated by the TPs within each set, uk, k ∈ 1 . . . Nsets.Now we can calculate how many CSs will be needed for each set of TPs by (4.17).

Nk = ceil(uk

mc) k ∈ 0 . . . Nsets (4.17)

Now that we know how many CSs are needed for each set, we can place them accordingto Figure 4.11. This Figure represents a hypothetic situation, where TPs would be placedin a circular shape (the outer ones) and 8 CSs would be needed to serve this set of TPs.The CSs are placed according to the figure, always in opposite directions like the numberssuggest. This way, the interference would be minimized and the coverage area maximized.This disposition is calculated based on a predefined distance - sp. This distance must bechosen according to the scenario considered. If we have very sparse sets of TPs, we will needa bigger sp than when we have condensed sets of TPs. The value of sp must be chosen withgood sense, once this can highly influence the placement. As a first approach, it is reasonable

51


52

Figure 4.11: Order for optimized CS placing

to consider the 15 of the maximum distance between TPs within a set, but this value must be

tuned according to each scenario.

In case only one CS is needed to cover a set of TPs, it is placed in the center of it.

4.7 Integration WiMAX-Wi-Fi

Integration of WiMAX and Wi-Fi is an interesting topic, once one of the possibilities ofthe WiMAX technology is to work as a backhaul service technology for Wi-Fi access points.

Our approach is to create special traffic points, called Wi-Fi hotspots, in which users canhave the same traffic requirements as WiMAX users, but will be served by Wi-Fi technology,thus being limited by the features and capabilities of that technology. The way we account forthe capabilities of Wi-Fi is by limiting the capacity of each hotspot according to the versionof 802.11 used.

We consider three versions of the IEEE 802.11 standard: a, b and g. IEEE 802.11a andIEEE 802.11g have a maximum capacity of 54 Mbps per channel, while IEEE 802.11b onlysupports transmissions up to 11 Mbps per channel. We also know that IEEE 802.11a has 12non-overlapping channels, while IEEE 802.11b and g only have 3. Using these values, we cancalculate the maximum capacity of each Wi-Fi hotspot using (4.18), where Cx stands for thecapacity of IEEE 802.11x and c for the capacity per channel.

Cx = nchannels × c (4.18)

The values for the capacity of each version of the set of standards of IEEE 802.11 arepresented in Table 4.7. It is important to note that these are theoretical values.

53

Table 4.7: Capacities of the IEEE 802.11 set of standards

Standard Capacity (Mbps)IEEE 802.11a 1242IEEE 802.11b 33IEEE 802.11g 163

4.8 Optimal Mesh Planning: Integer-Based Mathematical Ap-proach

Integer-based Linear Programming

Linear Programming (LP) is a type of mathematical programming that aims to describeand find and optimal solution for a certain mathematical problem. It consists of two parts: alinear function to be minimized or maximized (objective function) and the constraints. Giventwo variables, X1 and X2, represented in the axis of Figure 4.12, the solution can be boundedto a polygon by the constraints denoted by the lines; each line represents a constraint. Bymoving around the limits of the polygon, one can find the minimum or the maximum for theobjective function and the value of some variables at the optimal point.

Figure 4.12: Linear programming

If the variables to be discovered are pure integers, which is our case, we have an Integer-Based problem.

A Mathematical Programming Language (AMPL) is a mathematical programming lan-guage, which helps describing linear programming models, that can then be solved usingseveral solvers, in our case, CPLEX.

In order to evaluate the accuracy of the developed algorithm, a modified third-partyAMPL mathematical model was used. This section will give you a brief explanation aboutthe model and the way it was used. Further readings may be found in [14].

Given the parameters and variables introduced in Section 4.5, the mathematical modelcan be formulated as follows:

54

min∑

(cjzj + pjwjN ) (4.19)

standing for ∑

j∈S

xij = 1 ∀i ∈ I (4.20)

xij ≤ zjaij ∀i ∈ I, ∀j ∈ S (4.21)∑

i∈I

dixij +∑

l∈S

(flj − fjl)− fjN = 0 ∀j ∈ S (4.22)

flj + fjl ≤ ujlyjl ∀j, l ∈ S (4.23)∑

i∈I

dixij ≤ vj ∀j ∈ S (4.24)

fjN ≤ MwjN ∀j ∈ S (4.25)

yjl ≤ zj , yjl ≤ zl ∀j, l ∈ S (4.26)

yjl ≤ bjl ∀j, l ∈ S (4.27)

zj(i)l

+Li∑

h=l+1

xij

(i)h

≤ 1 ∀l = 1, . . . , Li − 1, ∀i ∈ I (4.28)

xij , zj , yjl, wjN ∈ {0, 1} ∀i ∈ I, ∀j, l ∈ S (4.29)

The objective function (4.19) accounts for the total cost of the network including instal-lation costs cj and pj related to the connection of MAPs to the wired backbone. Constraint(4.20) provides full coverage for all TPs, while constraint (4.21) is a coherence constraintassuring, respectively, that a TP i can be assigned to CS j only if a device is installed in jand if i is within the coverage set of j.

Constraint (4.22) defines the flow balance in node j. The term∑

i∈I dixij is the total trafficrelated to assigned TPs,

∑l∈S flj is the total traffic received by j from neighboring nodes,∑

l∈S fjl is the total traffic transmitted to the wired backbone.

Constraint (4.23) imposes that the total flow on the link between device j and l does notexceed the capacity of the link (ujl). Constraint (4.24) imposes for the entire mesh client’straffic serviced by a network device not to exceed the capacity of the wireless link, whileconstraint (4.25) forces the flow between device j and the wired backbone to zero if device jis not a MAP. The parameter M is used to limit the capacity of the installed MAP.

Constraints (4.26) and (4.27) define the existence of a wireless link between CS j and CSl, depending on the installation of nodes in j and l and wireless connectivity parameters bjl.The constraint expressed by (4.28) forces the assignment of a TP to the best CS in which aMAP or MR can be installed according to the proper sorting criterion, while constraint (4.29)restricts the decision variables to take binary values.

55

This is the original model [14]. By replacing constraint (4.24) with the following, we canget a formulation closer to our algorithm, which will let us compare the results and accountfor its accuracy. ∑

hjk−1≤gij<hj

k

dixij

Ck≤ 1 ∀k ∈ {1 . . . 7}, ∀j ∈ S, i ∈ I (4.30)

This constraint assures that, for each modulation, the capacity is respected. Having thismathematical model, it’s now possible to write an AMPL model, but in order to solve it,some data is needed too. This data file can be manually written, but that can take muchtime. As this algorithm we will be testing will be integrated in a software, it is possible toautomatically generate the file. The next Chapter will explain how this file is generated andhow to do the proper comparisons.

4.9 Conclusions

This chapter presented our formulation and solution, through an algorithm, of mesh plan-ning problems. We defined the two main elements of the problem: Candidate Sites and TrafficPoints, along with the necessary properties to store about each one. We have also explainedthe way we handle internal/external traffic and introduced a representation for scenarios thatwill be used throughout this work.

We have developed an algorithm which is able to solve mesh planning problems, while italso allows its implementation in a user-friendly application, which will be presented in thenext chapter. To account for the accuracy of our algorithm, we have adapted a third-partylinear programming formulation to suit our requirements.

A mechanism to optimally place CSs, which will also be a door to optimal planning, hasalso been developed, and a method of performing integration between WiMAX and Wi-Fiwas explored.

56

Chapter 5

Mesh Planning Evaluation

One of the objectives of this work was to develop a software to assist mesh planning.Section 5.1 will introduce the Mesh Planning Software (MPS), which is based on all theknowledge and principles presented in the earlier sections. Afterwards, Section 5.2 will makethe comparison between the MPS’ and the linear programming model, presented in Section4.8, and Section 5.3 brings some interesting results on mesh planning, such as the influenceof the internal/external traffic rate and the density of users. Finally, Section 5.4 presents theconclusions.

The symbols used in this software to represent the different elements are the same usedon the rest of the work and presented in Section 4.4.

5.1 Mesh Planning Tool

This tool is intended to aid the planning of WiMAX mesh networks, and so it is able toevaluate a solution proposed by the user, in terms of the places where each element of thenetwork shall be. Moreover, it is also capable of, given the traffic parameters for a certainscenario, propose a solution. The scenario is built on the top of a map, which can be previouslycreated by the user. This feature makes it easy to design the scenarios, which usually is adifficult task.

It includes a wide set of configuration options, which allow the user to accurately definea scenario. All the interaction with the user is done through easy steps and a user-friendlygraphical user interface.

5.1.1 Starting

In order to use this application, a few steps must be followed:

• Load map

• Enter CSs, and TPs or Wi-Fi hotspots

• Define options

• Calculate

• Watch the results

57

The next sections will explain how to perform these actions.

5.1.2 Loading map

Loading a map is the first step to do. A well detailed map will certainly help with thepositioning of the elements.

To load a map, we must go to Map → Load Image and select a GIF, JPEG, PNG orBMP image whose size does not exceed 400x400 pixels. After the image is selected, we willbe asked to enter the map limits in Km. These limits are what give meaning to the map byensuring the proper scaling, so they must be correctly introduced. By going to Map → SetLimits they can be readjusted, however, we must take into account that modifying the limitswill not scale any elements that have already been positioned.

5.1.3 Entering Traffic Points

As explained earlier, TPs are places where clients are supposed to be, so when enteringthem into the map, the user must define their traffic parameters. Inserting a TP can bedone by going to Traffic Point → Add Traffic Point. A dialog will pop up showingthe configuration panel. Figure 5.1(a) shows the first tab of the traffic point configuration:Constants. In this tab, the constants for the services a user can have are defined. Theavailable services are: VoIP, Data, IPTV, Media Stream, Online Gaming, P2P and Videoconference. For each service, typical values are suggested (Table 4.1), however, the user canmake changes in order to adapt them to his network. It is also possible, in this tab, to definethe download/upload (DL:UL) ratio.

The second tab, displayed in Figure 5.1(b), allows the user to choose which traffic profilesare present (residential and business), and configure the number of utilizations and durationalong two time periods: day and night. For residential users, VoIP, Data, IPTV, MediaStream, Online Gaming and P2P are available, while for business users, it’s only possible toconfigure VoIP, Data and Video conference. On the bottom, one can also define the densityof clients. Typical values are suggested for all these fields, and can be found on Tables 4.3and 4.4.

In the last tab, exhibited in Figure 5.1(c), it is possible to define the shape of the trafficpoint as being a square or circle and enter its dimensions, as well as its position. The positionfields can be left blank and in this case, the TP will appear in the left-top of the map andcan then be dragged to its place.

When all the parameters are entered we are ready to click OK. A blue square will appearin the place we chose or in the left-top in case no coordinates were entered. The TP can bedragged to its position to complete the placement, as shown in Figure 5.2.

5.1.4 Entering Wi-Fi hotspots

A Wi-Fi hotspot can be added to the scenario by going to Traffic Point → Add Wi-Fihotspot. A window similar to the one in Figure 5.3 will pop-up, so that the hotspot can beconfigured. We can add as many users as we want, with different traffic requirements andselect between 802.11 a/b/g. As the users are entered, a feasibility check is performed and incase of no feasibility the total amount of traffic field turns red.

58

(a) Constants

(b) Profiles

(c) Shape

Figure 5.1: Traffic point configuration

59

Figure 5.2: Dragging a TP to its position

Figure 5.3: Wi-Fi hotspot configuration

The feasibility is checked based on theoretical values for multi-channel Wi-Fi access, asdescribed in Section 4.7.

5.1.5 Randomly generating Traffic Points

It is possible to randomly generate traffic points by going to Traffic Point → Randomlygenerate Traffic Points. A pop-up window will ask for the number of traffic points togenerate and then a window, similar to the one in Section 5.1.3 will pop-up so that they canbe configured. It is important to note that each set of traffic points generated this way willshare the same configuration.

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5.1.6 Entering Candidate Sites

Inserting a CS can be done by going to Candidate Site → Add Candidate Site. LikeTPs, CSs must also be configured, so a window will pop-up showing two tabs.

The first tab, shown in Figure 5.4(a) allows the user to set the technology parameters,mainly to choose between mobile and fixed WiMAX, the characteristics of the base stationand some enhancement techniques, such as SISO, SIMO, MIMO, sectoring and adaptivebeamforming. When SIMO or MIMO are selected, it’s possible to select the number ofantennas for reception and transmission and to decide between spatial diversity and spatialmultiplexing (Please refer to Chapter 3).

In the next tab, (Figure 5.4(b)) it is possible to choose the type of terrain from rural,suburban and urban. As explained in Section 2.3.3, there are three types of terrain accordingto the SUV model. After these parameters are entered, you are ready to click Next.

After clicking next, a window like Figure 5.4(c) will rise presenting a preview of theconfigured base station, in terms of reachable area and achievable capacity. In this window,one can also configure the number of radios and sections, as well as the channel size. By slidingthe horizontal trackbar, it is possible to see the different capacities for each modulation.

Clicking OK, the CS will appear in the map and, in the same way as TPs, it can bedragged to its position.

5.1.7 Randomly generating candidate sites

In the same way as TPs, CSs’ can also be randomly generated. The process is identical: apop-up window will ask for the number of CSs to be generated and then another one will risein order to perform their configuration. Once again, all the CSs generated within the sameset share the same properties.

5.1.8 Auto-Placing Candidate Sites

CSs can be automatically placed, using the mechanism presented in Section 4.6. This canbe done by going to Candidate Site → Auto Place.

It is important to note that to use this feature, some parameters must first be defined inthe options panel. Some default values are defined upon the program loading; however, thesemust be adapted to each situation.

5.1.9 Removing Traffic Points or Candidate Sites

To remove a TP or a CS we must put the cursor on the top of it and check its ID. Then wemust go to either Traffic Point → Remove Traffic Point or Candidate Site → RemoveCandidate Site and enter its ID.

When a TP or CS is removed, the IDs of the remainder are changed.

5.1.10 Options

There are some options that can be set before and after the calculation is made. Theoptions panel, depicted in Figure 5.5 can be accessed through Options → Preferences. In

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(a) Technology

(b) Position

(c) Preview

Figure 5.4: Candidate site configuration

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Figure 5.5: Options panel

this panel, we can choose the sorting method which is explained in Section 4.5.2. When usingauto-placing CSs, some important parameters must be defined here, such as the minimumdistance for TPs to be considered as neighbors, an estimation of the capacity of each CS orthe minimum space between CSs serving the same set of TPs.

It is also important to define the amount of internal traffic of each node or, by otherwords, the traffic that stays inside the network and does not need to be routed to the wiredbackbone, as explained in Section 4.3.3.

These preferences can be changed even after the calculation is made, but will only takeeffect in the next calculation.

5.1.11 Calculate

After all the data is entered and the options are set, it is time to click Calculate. Agreen bar will indicate you the progress of the calculation at the same time as the task beingperformed appears in the status bar. In the end, a dialog will appear, indicating how muchtime was spent to calculate the solution.

As referred earlier, unused CSs turn black, MRs yellow and MAPs red.

5.1.12 Saving Scenario

The scenarios can be saved into a file, so they can be loaded after, by going to File→ Save Scenario and choosing the location where the file will be placed. A “.scn” file isgenerated containing the scenario.

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5.1.13 Loading Scenario

When it is required to load a previously saved scenario, it can be done through File →Load Scenario. In order to have a successful loading, the map image must be in the sameplace as it was when the scenario was saved. Keeping the “.scn” and image files together isalways a good choice to avoid errors.

5.2 Comparison with the optimal linear programming approach

In order to account for the accuracy of the application, along with the accuracy of thealgorithm presented in Section 4.5, we performed some simulations using the same scenario onthe MPS and the mathematical model. This is possible because MPS is prepared to producea “.dat” file that contains the description of the scenario in an understandable format forAMPL before making its calculations. This way, we can have the results from both for thesame scenario and compare them.

As the comparison criterion here is the number of CSs and MAPs created, which is ourobjective to minimize, we used the default values presented in Chapter 4 to perform thesimulations.

5.2.1 Scenario 1

Figure 5.6(a) represents the most simple scenario tested. The dimensions of the consideredarea are 50Km x 50Km.

Table 5.1: Results for scenario 1 with both toolsMPS AMPL

MAPs 1 1MRs 0 0

Although this scenario appears to be too simple, it brings up a great question: why didMPS chose the upper CS while AMPL chose the middle one?

Though the purpose of both tools is quite the same, they work in a very different manner.This difference in the solution can exist simply due to the order by which the input data isanalyzed. In this case, the results can be considered coherent, once being the upper or themiddle CS turned into MAP is exactly the same.

5.2.2 Scenario 2

In this scenario, we disposed the CSs over a line that could connect the TPs, to test theperformance of the algorithms searching for the best way to serve two TPs. Figure 5.7(a)represents this scenario, whose dimensions are 10Km x 10Km.

Contrarily to the last scenario, this one is a case where the best solution is to turn the CSin the middle into a MAP, once this is the most central and this away can serve both TPs.

As we can see, the results are the same. Once again we can infer that the two tools arecoherent in their solutions.

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(a) Scenario 1

(b) Scenario 1 MPS result (c) Scenario 1 AMPL result

Figure 5.6: Scenario 1 and results for both tools


MAPs 1 1MRs 0 0

5.2.3 Scenario 3

This time we wanted to test whether the algorithms would select the CS closer to thebiggest amount of traffic to be a MAP, when two devices are needed. The dimensions of thearea represented by the map are 20Km x 20Km. Figure 5.8(a) depicts this scenario.

As we can see, the CS selected to be a MAP was the closer to the highest concentrationof traffic, as it was expected.

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(a) Scenario 2




MAPs 1 1MRs 1 1

The results were exactly the same for both tools, so once again we can assume that theirresults are consistent.

5.2.4 Random scenarios

As stated before, the MPS has the capability to generate random CSs and TPs, thatcombined can be used to create random scenarios. In this section we have created manyrandom scenarios with the same number of TPs and CSs, but random positions and terrain

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(a) Scenario 3



limits. Then we simulated the same scenario in each tool and watched the results.As proved by Table 5.4 and Figure 5.9, the results of both tools in terms of number

of MAPs and MRs are similar. The slight differences that occur in some cases are due todifferences in the solving method and because our method is a heuristic, which, althoughhaving very similar results to the linear programming method, is an approximation. Despitethese differences, we can conclude that, statistically, the results are very close.

In terms of time, Figure 5.10 proves that, although MPS is, in average, four times slowerthan AMPL/CPLEX, it is perfectly acceptable, once we are talking of less than a second.Once again this difference is due to the way the calculations are done: although working toget the same solution, the two approaches work in a very different manner.

After regarding these results, we considered that the developed algorithm’s results arevalid, so we could proceed to more elaborated scenarios.

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Table 5.4: Results for both tools with the same random scenarioMPS AMPL

MAPs MRs Time MAPs MRs Time1 3 0.77 1 1 0.451 2 0.80 1 2 0.111 0 0.80 1 1 0.081 2 0.86 1 1 0.271 3 0.83 1 1 0.121 2 0.65 1 2 0.091 2 0.73 1 1 0.071 0 0.71 1 1 0.202 1 0.73 2 1 0.081 2 0.75 1 2 0.10

1.10 1.70 0.76 1.10 1.30 0.16

MPS AMPL

0

0.5

1

1.5

2

2.5

3

Num

ber

of M

Rs

Number of MRs for each tool

Tool

(a) Number of MRs

MPS AMPL

1

1.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8

1.9

2

Num

ber

of M

AP

s

Number of MAPs for each tool

Tool

(b) Number of MAPs

Figure 5.9: Number of MRs and MAPs for both tools

5.3 Scenarios and Results

This Section will present the results obtained for some scenarios and different technol-ogy configurations. Real images were loaded into the tool to achieve more realistic trafficdistributions.

We mention every value we changed in the software, so all the values that are not referencedwere left as suggested by the default.

5.3.1 CS placing methods comparison

In this scenario, depicted in Figure 5.11, we have tried to model the center of Aveiro interms of possible WiMAX users, and then placed the CSs in the map according to the threepossibilities the software offers: manually, randomly and automatically.

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MPS AMPL

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8T

ime

(s)

Time for each tool

Tool

Figure 5.10: Time required for each tool

In order to correctly define the traffic characteristics, we considered the population densitywhich is approximately 368 person/Km2 [20]. We speculated that 1/10 of the populationwould have WiMAX connection at home and that 1/100 of the population would be alsousing this technology at work.

Table 5.5: Density of users

Density (usr/Km2)Residential 36.8Business 3.68

The map represents a part of the city with 727m x 727m that we considered to be an urbanscenario, as it is in the center of the city. The DL:UL rate is 4:1. The traffic parameters wereleft with the default values, which are considered reasonable for daily utilization.

The technology used was Fixed WiMAX with no enhancement techniques.

Manual Placing CSs: case 1

In this first step we positioned the CSs in the corners of the map, as shown by Figure5.12(a). The reason to choose this placement was to cover the entire place and have a goodperformance.

As we can see from Figure 5.12(b) and Table 5.10, 4 CSs were activated: 1 MAP and3 MRs. As we thought this was too much for the traffic requirements, we tried a different

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Figure 5.11: Scenario 1 - Traffic Points

(a) Placed CSs (b) Result

Figure 5.12: Manual Placing CSs: case 1

Table 5.6: Manual Placing CSs: case 1 - Results

Number of MAPs Number of MRs1 3

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approach.

Manual Placing CSs: case 2

Now we placed the CSs considering a better positioning than in the last step. As we cansee in Figure 5.13(a), the CSs are placed in the middle of the TPs.


Figure 5.13: Manual Placing CSs: case 2

Table 5.7: Manual Placing CSs: case 2 - Results


In Figure 5.13(b) and Table 5.7 we can see that from the scenario we created by placingthe CSs in the chose positions we got 3 CSs activated: 1 MAPs and 2 MRs. This result isbetter than the previous, but guessing the correct place for the CSs is not an easy task. So, inorder to get the best positions in an automatic way, we will use the auto placing mechanism,presented in Section 4.6 and implemented in the application.

Auto Placing CSs

Using the auto placing feature configured according to Table 5.8 we came to the positionsrepresented in Figure 5.14(a).

Figure 5.14(b) and Table 5.10 show the results after the calculation for the automaticplacement: 1 MAP and 2 MRs. Although these results are similar to the ones obtained inthe last essay, the process of placing the CSs was much easier, because the application did itall by itself. The only problem with this mechanism is that it does not take into account thepossibility of installation of a device in a certain place.

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Table 5.8: Configuration of the Auto Place feature

Option ValueConsider TPs as neighbors within 0.65KmCapacity of each CS (estimation) 20Mbps

Space between multiple CSs 0.1Km


Figure 5.14: Auto Placing CSs

Table 5.9: Auto Placing CSs - Results


Randomly placing CSs

Randomly placing CSs is another possibility, so we will perform a test using this technique.Figure 5.15(a) shows the result of a random CS placement.

Table 5.10: Randomly Placing CSs - Results


Although the results shown in 5.15(b) are as good as the auto place ones in terms ofnumber of devices, we cannot always grant that the results provided by a random placingtechnique are good, just because it is random. Another point is that random placing doesnot take into account the possibility of installation of a device in a certain place, neither theoptimality of the solution.

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Figure 5.15: Randomly Placing CSs

5.3.2 Reaching remote locations without wire

The scenario illustrated in Figure 5.16 represents a remote location (on the left), whereone could probably not be able to have a wired connection. The dimensions of the map are15 x 15 Km. We have considered a low population density (1.5 person/Km2) and only theresidential traffic profile. The environment is rural.

Figure 5.16: Reaching remote locations without wire - Traffic Points

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Figure 5.17: Reaching remote locations without wire

Table 5.11: Reaching remote locations without wire - Results


By placing an MR in the middle of the way between a zone where there is as wiredconnection (on the right) and the isolated place, we could guarantee access to the network,as shown by Figure 5.17(b). Table 5.11 shows the number of necessary MAPs and MRs.

5.3.3 WiMAX-Wi-Fi Integration Example

As referred earlier, in Section 4.7, WiMAX-Wi-Fi integration is interesting, as WiMAXcan provide backhaul connectivity to Wi-Fi hotspots.

In this scenario we have considered a zone of Aveiro where lots of students live. So, weequally divided the traffic into the residential and business profiles, once the needs of a studentmight compare to the business requirements sometimes. We have considered a density of 20usr/Km2 for both profiles. Fixed WiMAX was the chosen implementation and we placed aCS in the middle of the area that we wanted to cover with the Wi-Fi hotspots. Figure 5.18shows the scenario while 5.19(a) and 5.19(b) show the placed CS and the calculation result.

Table 5.12: WiMAX-Wi-Fi Integration - Results


Watching the results, we can infer that WiMAX is able to work as a backhaul technologyfor Wi-Fi, since it can serve, in this case, 4 Wi-Fi hotspots, which will not need cable instal-lations, just power to work. Using this technology, the deployment of Wi-Fi networks can be

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Figure 5.18: WiMAX-Wi-Fi Integration - Traffic Points


Figure 5.19: WiMAX-Wi-Fi integration

eased, at least to the point that access points will not need an Ethernet cable. There wasonly need for one MAP, as shown by Table 5.12.

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5.3.4 External and Internal traffic

The amount of internal and external traffic is an important characteristic of a meshnetwork that will determine the number of necessary MAPs, once internal traffic will notneed to find a gateway to the Internet.

In this section we will use the scenario shown in Figure 5.20, which has TPs with highamounts of traffic and many CSs, shown in Figure 5.21 and vary the amount of internal trafficfrom 10% to 90%. This will give us an idea of the influence of the internal/external trafficrate in a mesh network.

Figure 5.20: Scenario 2 - Traffic Points

Figure 5.22 shows the results. As we can see, we start with near 40 MAPs and 15 MRswhen only 10% of the traffic is internal and end in an opposite situation, with almost 50 MAPsand 3 MRs when 90% of the traffic is internal. The transition point, where the number ofMRs starts to be higher than the number of MAPs is around 65%. It’s important to noticethat although this scenario is generalist, this value will change according the each scenario.

5.3.5 Fixed and Mobile WiMAX

Now we will account for the difference of using Fixed and Mobile WiMAX in a meshnetwork. We have used the same scenario as in the last section (Figure 5.20), which has ahigh number of traffic points with the residential traffic profile enabled and a density of 36.8users per Km2, and many CSs. Then we have tested the scenario using Fixed and MobileWiMAX, both with a 3.5 MHz wide channel and SISO.

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Figure 5.21: Scenario 2 - Placed CSs

10 20 30 40 50 60 70 80 900

5

10

15

20

25

30

35

40

45

50Variation of the Number of MAPs and MRs with the amount of internal traffic

Amount of internal traffic (%)

Num

ber

of d

evic

es

MAPsMRs

Figure 5.22: Relation between the internal/external traffic rate and the number of MAPs and MRs

Table 5.13: Fixed and Mobile WiMAX - ResultsTechnology Number of MAPs Number of MRs

Fixed WiMAX 29 14Mobile WiMAX 24 11

Figures 5.23(b) and 5.23(c) and Table 5.13 show that, as it was expected due to thepreviously acquired knowledge that Mobile WiMAX has more capacity than Fixed WiMAX,the number of necessary devices when we used Mobile WiMAX was less than with FixedWiMAX.

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(a) Fixed and Mobile WiMAX - Traffic Points

(b) Result for Fixed WiMAX (c) Result for Mobile WiMAX

Figure 5.23: Fixed and Mobile WiMAX

5.3.6 Growth of the amount of devices with the density of users

Here we have used the urban scenario represented in Figure 5.24, which has numerous TPsand CSs to account for the growth of the amount of necessary devices with the increasingnumber of users. Only the residential traffic profile has been activated and we varied thedensity of users of all de TPs from 1 to 16. The technology is Fixed WiMAX with noenhancements.

From Figure 5.25 we can see that the growth in the total number of devices (MAPs +MRs) is stepped. This makes sense, once that, generally, when a device is activated, notall its capacity is used, so it will allow more users until it has reached the maximum, which

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Figure 5.24: Urban Scenario

0 2 4 6 8 10 12 14 160

5

10

15

20

25

30Growth of the amount of devices with the density of users

Density of residential users (usr/Km2)

Num

ber

of d

evic

es

MAPsMRsMAPs + MRs

Figure 5.25: Growth of the amount of devices with the density of users

corresponds to activation of a new device.

We can also note that the number of MAPs and MRs increases and decreases in anirregular way. This is due to our consideration that it is better to activate a MR than a MAP.Sometimes when the creation of a MAP is strictly necessary, this can make the number ofMRs go down. Although less, decreases in the number of MAPs also occur. The explanation

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for this is the place where MRs are, that may allow one MAP to be deactivated.

5.4 Conclusions

Section 5.1 introduced the MPS, which is the result of part of the research performed inthis work.

In Section 5.2, we have compared our mesh planning algorithm, which is implementedin the MPS, with a linear programming model and performed several essays that allowedus to infer that our solution is accurate, comparing to that one. Through the simulationof many random scenarios we could also conclude that although the MPS is slower than thelinear solver, it is capable of proposing a solution for a mesh network deployment in a perfectlyreasonable time. Moreover, the simplicity of creating and changing a scenario in our software,contrarily to the linear solvers, is one of its interest points that certainly compensates thishigher delay.

Finally, we have presented some interesting results that were obtained using the MPS.We distinguished the various CS placing methods available, and discussed which would bethe most appropriated for each situation. We have also demonstrated, in Section 5.3.2,that WiMAX is a promising technology to reach remote locations and shown results onWiMAX-Wi-Fi integration. Among others, we have accounted for the differences in Fixedand Mobile WiMAX mesh mode, concluding, once again that Mobile WiMAX is the mosteffective solution.

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Chapter 6

Conclusion and Future Work

The work developed in this thesis focused on WiMAX planning techniques for both modesof operation: PMP and Mesh, which is still an area of research. By automating these planningtechniques, we are able to deploy solid networks at the lowest cost, thus providing cost-effectivesolutions to the deployer in a reasonable time.

We have compared Fixed and Mobile WiMAX, by performing some simulations, which al-lowed us to elect Mobile WiMAX as the most effective technique. We have also explored somepossible implementations of enhancement techniques described in the IEEE 802.16 standard,introduced them in the base station planning tool and compared their benefits in terms ofcoverage and capacity. Once again we accounted for the benefits brought by these techniquesto Fixed and Mobile WiMAX, concluding that for some the benefits are higher in Mobilethan in Fixed WiMAX.

A mesh planning algorithm, along with a CS optimal placing mechanism, were developed,as well as a third-party linear programming solution has been adapted to our problem, inorder to be used for accuracy accounting. The mesh planning algorithm is able to evalu-ate a proposed scenario, deciding which elements are needed and what type (MAP or MR)must they be, while the optimal placing mechanism automatically places CSs, in optimizedpositions, for a given scenario.

A mesh planning tool for Microsoft WindowsTMwas created, using the previously devel-oped algorithms and mechanisms. As this application is able to generate a data file to beused with the linear programming model, it was possible to generate several scenarios andeasily compare the results of both methods. This way we could account for the accuracy ofthe tool and, consequently, of the algorithm, which proved to obtain correct results.

Once the tool was ready, we could create some scenarios, so we could get interestingresults about WiMAX mesh mode planning. We have proved what had stated in the be-ginning: that WiMAX would be a promising technology to reach remote locations withoutwire. WiMAX-Wi-Fi integration has also been tested and demonstrated to be a hopefulsolution, once WiMAX is able to provide wideband backhaul connections, while Wi-Fi is acommon technology, which is perfect for the last-mile connection. The performance of Fixedand Mobile WiMAX in mesh networks has also been tested and, once again, we concludedthat Mobile WiMAX performs better.

As future work we leave some improvements on the CS optimal placement mechanismthat can still be achieved, mainly estimation techniques to avoid the necessity of doing this

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manually and through trial processes.

IEEE 802.16 mesh networks are still an area of research and, as said before, there are manychallenges when planning this type of networks, so performance tests on real test beds mustbe carried out to evaluate the accuracy of the planning algorithms along with the behaviorof the technology in real scenarios.

The research made in the scope of this thesis demonstrated that the IEEE 802.16 technol-ogy, especially mesh mode, will probably rise in the next few years as promising competitorto the actual wireless network technologies.

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