Cse 2008 7

Design and Analysis of Wireless-OpticalBroadband Access Networks (WOBAN)

By

SUMAN SARKARB.E. (Bengal Engineering and Science University, India) 2001

M.S. (University of California, Davis) 2005

DISSERTATION

Submitted in partial satisfaction of the requirements for the degree of

DOCTOR OF PHILOSOPHY

in

Computer Science

in the

OFFICE OF GRADUATE STUDIES

of the

UNIVERSITY OF CALIFORNIA

DAVIS

Approved:

Dr. Biswanath Mukherjee

Dr. Dipak Ghosal

Dr. Xin Liu

Committee in charge

2008

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To my father: Late Priyabrata Sarkar, and mother: Mrs. Sipra Sarkar.

–ii–

Abstract

The growing customer demands for bandwidth-intensive services are accelerating

the need to design an efficient “last mile” access network in a cost-effective manner. Tra-

ditional “Quad-play” applications (which refer to a bundle of services with voice, video,

Internet, and wireless) and premium rich-media applications (e.g., multimedia, interactive

gaming, and metaverse) need to be delivered over the access network to the end users in

a satisfactory and economical way. Thus, besides its enormous transport capacity, today’s

access infrastructure should bring operational efficiencies, namely mobility and untethered

convenience to end users. Hence, this dissertation proposes and investigates a novel hybrid

network paradigm – wireless-optical broadband access network (WOBAN) – a combination

technology of high-capacity optical access and untethered wireless access.

This dissertation begins in Chapter 1 with an introduction to traditional broad-

band access networks – both optical and wireless networks, and compiles the research con-

tributions and organization. Chapter 2 defines WOBAN, develops its architecture, and

provides a comprehensive outline of its research aspects, coupled with various design mod-

els, and pros and cons of efficient protocols to manage the network. It also argues why the

combination of optical and wireless technologies should provide an improved solution for

future network design, and touches upon its current business drivers.

Since both optical and wireless networks – two very diverse technologies – exist

in a WOBAN, a trade-off is needed while designing the network. This means neither the

optical nor the wireless part should be over- or under-provisioned to develop a cost-effective

solution. Thus, Chapter 3 and Chapter 4 present design aspects of WOBAN in detail. While

Chapter 3 focuses on heuristics – greedy algorithm and simulated annealing – to plan the

network, Chapter 4 explores the constraint programming model, coupled with Lagrangean

Relaxation, to achieve an optimal design solution.

Once the network is deployed, efficient protocols need to be devised by exploring

and exploiting WOBAN’s novel aspects. Consequently, Chapter 5 examines the novelty of

WOBAN’s connectivity and develops a “Delay-Aware Routing Algorithm”, called DARA.

Unlike standard optical access networks, WOBAN poses a new challenge for streaming

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media applications due to its higher delay budget. Thus, DARA is an effort to minimize

WOBAN’s delay budget to deliver premium applications on-time.

WOBAN, due to its hierarchical network architecture, can be subjected to multiple

failure scenarios. Thus, minimizing the failures and restoring the network quickly, in case

of failure, are important aspects to consider. Consequently, Chapter 6 develops a “Risk-

and-Delay Aware Routing Algorithm” (an extension to DARA), called RADAR, to exploit

the fault-tolerance behavior of WOBAN.

Therefore, this dissertation creates new knowledge by introducing a novel network

architecture for future access networks and makes important contributions by investigat-

ing design algorithms, network protocols, and business drivers behind the need for this

converged network model, that is WOBAN.

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Acknowledgments

The process of achieving the Ph.D. is a journey – and what an exhilarating journey

it has been for me over three-and-half years!

In this endeavor, my mentor, Dr. Biswanath Mukherjee, has been my greatest

inspiration. He is a champion for excellence, and his creativity, perfection, dedication, and

confidence have transformed my thinking and attitude towards work and life. Not only

he has taught me to think positive; more importantly, he has inspired me to become a

finer person in life. I have cherished every bits of numerous interactions with him over this

period – technical and not-so-technical alike. He has steered me through the challenge with

his wisdom, his support, and his encouragement. Over three-and-half years, it has been a

fantastic odyssey together which takes me towards maturity and strength. Thank you, Bis,

to keep faith on me.

Thanks to Dr. Sudhir Dixit from Nokia Siemens Networks for being a great sup-

porter of my research. He has encouraged me to think novel and ingrained the creativity

in me. I have enjoyed his companionship and admired his insights. His philosophy toward

work has taught me how to innovate.

I deeply admire Dr. Hong-Hsu Yen from Shih-Hsin University, Taiwan, for his

help and encouragement. Working along with him is a pleasant experience. Thanks to his

teaching, I become a mature researcher. Whenever I felt difficulties, he waded me through;

whenever I became skeptical, he cleared my doubts; whenever I needed a friend, he gave

me his hand.

I am grateful to Professor Biswanath Mukherjee for providing me the research

opportunity in his laboratory. I am also indebted to National Science Foundation (NSF),

Nokia, and Nokia Siemens Networks to fund my research. A special thank goes to Dr.

Sudhir Dixit for being our industry liaison.

I appreciate the help and support from my Ph.D. committee members – Dr. Dipak

Ghosal and Dr. Xin Liu. I have greatly benefitted from their insights through research

inputs. I learned from Dr. Ghosal how to become a humble yet efficient person. Dr. Liu

is instrumental in imparting the knowledge through her courses. Her analytical prowess is

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one of my early motivation for finer research. I would also like to take this opportunity to

express my gratitude to the faculty members who I have interacted with and taken courses

from.

It has been a great pleasure to be a member of Networks Laboratory at UC Davis.

This laboratory is arguably the best in its research domain, and takes the pride from its

state-of-the-art resources, friendly ambience, excellent research activities, and rich track

record for innovation. I like to thank Professor Biswanath Mukherjee and all the past

and present researchers from this group for providing me such an enlivening environment.

Special thank goes to Dragos Andrei, Amitabha Banerjee, Marwan Batayneh, Prantik Bhat-

tacharyya, Cicek Cavdar, Joon-Ho Choi, Pulak Chowdhury, Frederick Clarke, Davide Cuda,

Dr. Christoph Gauger, Dr. Anpeng Huang, Dr. Grace Huang, Shraboni Jana, Sung-Chang

Kim, Avishek Nag, Martin Nicholes, Dr. Young-il Park, Vishwanath Ramamurthi, Dr.

Smita Rai, Abu (Sayeem) Reaz, Rajesh Roy, Lei Shi, Dr. Narendra Singhal, Huan Song,

Dr. Lei Song, Dr. Massimo Tornatore, Ming Xia, Dr. Sunhee Yang, Dr. Jing Zhang,

Dr. Hongyue Zhu for their constant support. Here I would also like mention a few other

researchers at UC Davis whom I have interacted a lot: Paulo Afonso, Nicholas Heller,

Behrooz Khorashadi, Yali Liu, Payman Mohassel, Xiaoling Qiu, Jennifer Yick, Wei Wang,

and Daniel Wu.

Being a member of UC Davis community brings the special meaning to my edu-

cation and life. Besides its lively atmosphere, it has provided me all the resources which

I asked for and more. Also being a part of Computer Science department is an exciting

opportunity for me. Here I would like to thank all the staff members of CS community

who has helped in smoothing my transition as a graduate student. Special thanks to Babak

Moghadam, Kim Reinking, Staci Bates, and Virag Nikolics.

I am grateful to Dr. Prabir Burman, Department of Statistics, UC Davis, for his

help in my research, especially for the analysis of Greedy Algorithm in Chapter 3. I would

also thank all who have contributed toward this unforgettable journey for three-and-half

year actively and/or passively. Thanks to all friends and family members who have been in

constant touch with me.

Finally, no word can express the support I have got from my parents. Whenever

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everything seemed gloomy in this long process, I have derived all my inner strength from

them. Thank you, mom and dad, for all I have inherited from you.

Suman Sarkar

June 2008

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Contents

List of Figures xi

List of Tables xiii

1 Introduction 11.1 Recent Trends in Optical Access Networks . . . . . . . . . . . . . . . . . . . 11.2 Recent Trends in Wireless Access Networks . . . . . . . . . . . . . . . . . . 41.3 Radio-on-Fiber – A Precursor of WOBAN . . . . . . . . . . . . . . . . . . . 61.4 Wireless-Optical Broadband Access Networks (WOBAN) . . . . . . . . . . 71.5 Research Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

1.5.1 WOBAN Architecture and Research Challenges . . . . . . . . . . . . 81.5.2 Network Planning and Setup for WOBAN . . . . . . . . . . . . . . . 81.5.3 Constraint Programming Model for WOBAN Deployment . . . . . . 91.5.4 WOBAN Connectivity and Routing . . . . . . . . . . . . . . . . . . 91.5.5 WOBAN Fault Tolerance and Restoration . . . . . . . . . . . . . . . 10

1.6 Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2 WOBAN Architecture and Research Challenges 122.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

2.1.1 Hybrid Wireless-Optical Broadband Access Network Architecture . . 132.1.2 Why is WOBAN a Compelling Solution? . . . . . . . . . . . . . . . 15

2.2 WOBAN’s Early Incarnations . . . . . . . . . . . . . . . . . . . . . . . . . . 172.3 Network Setup: A Review of Placement Algorithms in WOBAN . . . . . . 19

2.3.1 Random and Deterministic Approaches . . . . . . . . . . . . . . . . 192.3.2 Greedy Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192.3.3 Combinatorial Optimization: Simulated Annealing Approach . . . . 202.3.4 Joint Optimization: Constraint Programming Approach . . . . . . . 20

2.4 Network Connectivity: A Review of Routing Algorithms in WOBAN . . . . 232.4.1 Minimum-Hop and Shortest-Path Routing Algorithms (MHRA and

SPRA) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 232.4.2 Predictive Throughput Routing Algorithm (PTRA) . . . . . . . . . 242.4.3 Delay-Aware Routing Algorithm (DARA) . . . . . . . . . . . . . . . 24

2.5 Fault Tolerance: Risk Awareness in WOBAN . . . . . . . . . . . . . . . . . 272.5.1 Risk-and-Delay Aware Routing Algorithm (RADAR) . . . . . . . . . 27

2.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

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3 Network Planning and Setup for WOBAN 293.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

3.1.1 Related Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293.2 Placement of Multiple ONUs in WOBAN . . . . . . . . . . . . . . . . . . . 32

3.2.1 Cost Metric for ONU Deployment . . . . . . . . . . . . . . . . . . . 333.2.2 Greedy Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333.2.3 Notations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343.2.4 Running Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343.2.5 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

3.3 Illustrative Numerical Examples: Greedy Algorithm . . . . . . . . . . . . . 383.3.1 Survey on Wireless Users in Wildhorse, Davis, California . . . . . . 39

3.4 Global Optimization of Placements of Multiple ONUs in WOBAN . . . . . 433.4.1 Simulated Annealing (SA) . . . . . . . . . . . . . . . . . . . . . . . . 443.4.2 Applying SA to Multiple-ONU Placement Problem of WOBAN . . . 443.4.3 Illustrative Numerical Examples: Greedy vs. SA . . . . . . . . . . . 46

3.5 Cost Comparison of WOBAN and PON Setup in Wildhorse . . . . . . . . . 493.6 Joint Optimization of WOBAN: Combined Heuristic (CH) . . . . . . . . . . 52

3.6.1 Illustrative Numerical Examples: CH . . . . . . . . . . . . . . . . . . 543.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

4 Constraint Programming Model for WOBAN Deployment 574.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 574.2 Design Criteria for WOBAN . . . . . . . . . . . . . . . . . . . . . . . . . . . 584.3 Mathematical Formulation for Optimal Placement of BSs and ONUs . . . . 60

4.3.1 Lagrangean Relaxation and Lower Bound of Primal Model (PM) . . 654.3.2 Primal Algorithm and Upper Bound of Primal Model . . . . . . . . 704.3.3 Computing Upper Bound (UB) and Lower Bound (LB) of Primal Model 71

4.4 Performance Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 734.4.1 PM vs. CH: Impact of Carrier-to-Interference (CI) Threshold, I . . 764.4.2 PM vs. CH: Impact of Wireless Channel Pool, F . . . . . . . . . . . 784.4.3 PM vs. CH: Impact of User Coverage Ratio, ρ . . . . . . . . . . . . 794.4.4 PM vs. CH: Impact of Non-Homogeneous Demography . . . . . . . 80

4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

5 WOBAN Connectivity and Routing 825.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

5.1.1 San Francisco WOBAN: A Community Wireless Mesh . . . . . . . . 845.2 Current Routing Approaches and Opportunities . . . . . . . . . . . . . . . . 86

5.2.1 Current Routing Approaches . . . . . . . . . . . . . . . . . . . . . . 865.2.2 Other Research Efforts . . . . . . . . . . . . . . . . . . . . . . . . . . 86

5.3 Delay-Aware Routing Algorithm (DARA) . . . . . . . . . . . . . . . . . . . 885.3.1 Achieving Load Balancing . . . . . . . . . . . . . . . . . . . . . . . . 905.3.2 Analysis of Link-State Predictions . . . . . . . . . . . . . . . . . . . 925.3.3 Analysis of Throughput . . . . . . . . . . . . . . . . . . . . . . . . . 94

5.4 Performance Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 955.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101

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6 WOBAN Fault Tolerance and Restoration 1026.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1026.2 Risk-and-Delay Aware Routing Algorithm (RADAR) . . . . . . . . . . . . . 1036.3 Analysis of RADAR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104

6.3.1 Risk Awareness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1046.3.2 Self Healing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1076.3.3 Delay Awareness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107

6.4 Performance Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1076.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110

7 Conclusion 1117.1 WOBAN Architecture and Research Challenges . . . . . . . . . . . . . . . . 1117.2 Network Planning and Setup for WOBAN . . . . . . . . . . . . . . . . . . . 1127.3 Constraint Programming Model for WOBAN Deployment . . . . . . . . . . 1127.4 WOBAN Connectivity and Routing . . . . . . . . . . . . . . . . . . . . . . 1137.5 WOBAN Fault Tolerance and Restoration . . . . . . . . . . . . . . . . . . . 113

Bibliography 115

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List of Figures

1.1 Typical tree-based passive optical network (PON). . . . . . . . . . . . . . . 31.2 Typical next-generation WDM-PON access network. . . . . . . . . . . . . . 3

2.1 A hybrid wireless-optical broadband access network (WOBAN) architecture. 14

3.1 Performance of various schemes of ONU placement in a WOBAN. . . . . . 383.2 Average distance (in meters) of ONUs from their primary users. . . . . . . 393.3 Map of wireless routers in Wildhorse. . . . . . . . . . . . . . . . . . . . . . . 403.4 Map of wireless routers by their signal strengths. . . . . . . . . . . . . . . . 413.5 Average distance (in meters) of ONUs from their primary users in Wildhorse. 423.6 Placement of 3 ONUs in Wildhorse WOBAN by Greedy (Top left cone:

ONU1, Bottom center cone: ONU2, Top right cone: ONU3. Colored dotsare residential wireless users). . . . . . . . . . . . . . . . . . . . . . . . . . . 43

3.7 Cost improvement (in meters) in WOBAN for individual ONU deploymentwith SA (in the test network). . . . . . . . . . . . . . . . . . . . . . . . . . . 47

3.8 Cost improvement (in meters) in Wildhorse WOBAN for individual ONUdeployment with SA. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

3.9 Relocation of 3 ONUs in Wildhorse WOBAN with SA compared to Greedy(Top left: ONU1, Bottom center: ONU2, Top right: ONU3). . . . . . . . . 48

3.10 WOBAN setup cost (normalized to one ONU unit cost) by Combined Heuris-tic (CH). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

4.1 Primal Algorithm schematic (“T” means True, “F” means False). . . . . . . 724.2 Impact of channel interference on normalized deployment cost (with ρ = 1

and |F | = 50 channels). If I ≥ 18 dB, no feasible solution exists for CH. . . 774.3 Impact of available channel pool on normalized deployment cost (with ρ = 1

and I = 12 dB). If |F | < 35 channels, no feasible solution exists for CH. . . 784.4 Impact of user coverage ratio on normalized deployment cost (with I = 12

dB and |F | = 50 channels). . . . . . . . . . . . . . . . . . . . . . . . . . . . 794.5 Impact of non-homogeneous user coverage ratio on normalized deployment

cost (with I = 12 dB and |F | = 50 channels). If ρ > 0.8, no feasible solutionexists for CH. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

5.1 A WOBAN’s upstream and downstream protocols. . . . . . . . . . . . . . . 835.2 San Francisco WOBAN and its front-end wireless mesh (SFNet). . . . . . . 855.3 Differential and asymmetric capacity assignment. . . . . . . . . . . . . . . . 905.4 Link-state predictions (LSPs) used at time intervals. . . . . . . . . . . . . . 93

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5.5 Average delay vs. load in SFNet. . . . . . . . . . . . . . . . . . . . . . . . . 955.6 Delay vs. load [for the furthest router/gateway pair (1, 25)] in SFNet. . . . 965.7 Comparing K-DARA (K > 1) path delays with PTRA delay [for the furthest

router/gateway pair (1, 25)] in SFNet. . . . . . . . . . . . . . . . . . . . . . 975.8 Average number of K-DARA (K > 1) paths under PTRA delays in SFNet. 975.9 Average hops vs. load in SFNet. . . . . . . . . . . . . . . . . . . . . . . . . 985.10 Hop distributions vs. load in SFNet. . . . . . . . . . . . . . . . . . . . . . . 985.11 Load balancing (or link congestion) vs. load in SFNet. . . . . . . . . . . . . 995.12 Actual vs. predicted packet intensities at high loads. . . . . . . . . . . . . . 1005.13 Actual vs. predicted packet intensities at low loads. . . . . . . . . . . . . . 100

6.1 An illustration of RADAR. . . . . . . . . . . . . . . . . . . . . . . . . . . . 1056.2 Packet loss for gateway failure. . . . . . . . . . . . . . . . . . . . . . . . . . 1086.3 Packet loss for ONU failure. . . . . . . . . . . . . . . . . . . . . . . . . . . . 1096.4 Packet loss for OLT failure. . . . . . . . . . . . . . . . . . . . . . . . . . . . 110

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List of Tables

2.1 A sample of municipal access networks. . . . . . . . . . . . . . . . . . . . . 182.2 Pros and cons of various placement schemes in WOBAN. . . . . . . . . . . 222.3 Pros and cons of various routing algorithms in the wireless part of a WOBAN. 26

3.1 Research activities on network placement. . . . . . . . . . . . . . . . . . . . 313.2 A small part of scanning results from Wildhorse. . . . . . . . . . . . . . . . 413.3 Wildhorse WOBAN user distributions (in hop count). . . . . . . . . . . . . 423.4 Simulation parameters for SA. . . . . . . . . . . . . . . . . . . . . . . . . . 473.5 Various components of WOBAN and PON expenditure. . . . . . . . . . . . 503.6 Device and fiber layout expenses (in normalized units). . . . . . . . . . . . . 503.7 ONU, WiFi, and WiMAX capacities. . . . . . . . . . . . . . . . . . . . . . . 513.8 WOBAN and PON setup expenditures (in normalized units). . . . . . . . . 513.9 Estimation of channel interference and number of BSs by CH. . . . . . . . . 53

4.1 WiMAX modulations vs. CI. . . . . . . . . . . . . . . . . . . . . . . . . . . 734.2 Device and fiber layout expenses (in normalized units). . . . . . . . . . . . . 754.3 Number of BSs and ONUs. . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

5.1 LSA’s bandwidth consumption. . . . . . . . . . . . . . . . . . . . . . . . . . 101

6.1 Risk List (RL) in a router. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1066.2 Updated Risk List for Gateway failure. . . . . . . . . . . . . . . . . . . . . . 1066.3 Updated Risk List for ONU failure. . . . . . . . . . . . . . . . . . . . . . . . 1066.4 Updated Risk List for OLT failure. . . . . . . . . . . . . . . . . . . . . . . . 106

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1

Chapter 1

Introduction

1.1 Recent Trends in Optical Access Networks

The dominant broadband access network that is emerging from today’s research

and development activities is a point-to-multipoint optical network, known as Passive Op-

tical Network (PON). The basic configuration of a PON connects the telecom central office

(CO) to businesses and residential users by using one wavelength channel in the downstream

direction [from Optical Line Terminal (OLT) at CO to Optical Network Units (ONU)], and

another wavelength channel in the upstream direction [from ONUs to OLT]. A PON does

not have any active element in the signal’s path from source to destination; hence, it is

robust. The only interior elements used in such a network are passive combiners, couplers,

and splitters.

A PON (Figure 1.1) provides much higher bandwidth for data applications [than

current solutions such as digital subscriber line (DSL) and cable modem (CM)] as well as

deeper fiber penetration. Based on current standards [B/G/GFP-PON standards (see below

for details of these abbreviations)], the PON can cover a maximum distance of 20 km from

the OLT to the ONU. While fiber-to-the-building (FTTB), fiber-to-the-home (FTTH), or

even fiber-to-the-PC (FTTPC) solutions have the ultimate goal of fiber reaching all the way

to end user premises, fiber-to-the-curb (FTTC) may be the more economical deployment

scenario today [1, 2].

The traditional single-wavelength PON (also known as the time-division-multiplexed

Chapter 1: Introduction 2

PON or TDM-PON) combines the high capacity of optical fiber with the low installation

and maintenance cost of a passive infrastructure. The optical carrier is shared by means

of a passive splitter among all the users, so the PON topology is a tree, as in most other

distribution networks, e.g., those for power, video, etc. As a consequence, the number of

ONUs is limited by the splitting loss, and by the bit rate of the transceivers in the OLT

and in the ONUs. Current specifications allow for 16 ONUs at a maximum distance of 20

km from the OLT and 32 ONUs at a maximum distance of 10 km from the OLT [3].

The per-user cost of such a network can be low as the bandwidth (for EPON,

this bandwidth is typically up to 1 Gbps in current practice and expected to increase to

10 Gbps in the future; and for GPON, it is 2.5 Gbps in current practice and expected

to be 10 Gbps in future) is shared among all the end users. But, as end users demand

for more bandwidth, the need for upgrading the existing PON architectures [viz., Ethernet

PON (EPON), Gigabit PON (GPON)1, Broadband PON (BPON, based on ATM), Generic

Framing Procedure PON (GFP-PON), etc.] to incorporate multiple wavelengths is essential.

Incorporating multiple wavelengths in PON [by means of wavelength-division multiplexing

(WDM)] provides excellent scalability because it can support multiple wavelengths over the

same fiber infrastructure, it is inherently transparent to the channel bit rate, and, depending

on its architecture, it may not suffer power-splitting losses. Please see [5] for a review of

WDM-PON architectures.

The basic idea behind the WDM-PON (Figure 1.2) is to increase the bandwidth

of the PON by employing wavelength-division multiplexing (WDM), such that multiple

wavelengths may be supported by either or both the upstream and downstream directions.

WDM-PON research has received quite a lot of attention in the literature, and most current

research focuses on the remote node (RN) and ONU architectures.

The straightforward approach to build a WDM-PON is to employ a separate wave-

length channel from the OLT to each ONU, both in the upstream and downstream direc-

tions. This approach creates a point-to-point (P2P) link between the OLT and each ONU,

which differs from the point-to-multipoint (P2MP) topology of the traditional PON. In the1With the recent progress of PON technology, Verizon’s GPON+TV services may support multiple wave-

lengths [4].


Figure 1.1: Typical tree-based passive optical network (PON).

Figure 1.2: Typical next-generation WDM-PON access network.


WDM-PON, each ONU can operate at a rate up to the full bit rate of a wavelength chan-

nel. Moreover, different wavelengths may be operated at different bit rates, if necessary;

hence, different types of services may be supported over the same network. This is clearly

an advantage of WDM-PON over the traditional PON [6,7].

There are various industry efforts to build PON architecture for commercial de-

ployment. In the United States, Verizon has introduced its “Fiber-to-the-Premises” archi-

tecture, called FiOS, to deliver high speed voice and data services to the home. FiOS service

consists of three consumer broadband speeds: up to 5 Mbps downstream and up to 2 Mbps

upstream (5 Mbps/2 Mbps), 15 Mbps/2 Mbps, and 30 Mbps/5 Mbps. FiOS network is

migrating from current BPON to future GPON architecture, thus moving towards higher

upstream/downstream speed and eliminating ATM [8]. Among other efforts, Novera Optics

has launched TurboLIGHT, a dense wavelength-division-multiplexed (DWDM) fiber-to-the-

X (FTTX) optical access technology, which allows flexible multimode transport capabilities

at different bit rates (125 Mbps to 1.25 Gbps) [9]. In Asia, a similar effort can be found

in WE-PON, which has a combined architecture of WDM (from CO to WDM device) and

TDM (from WDM device to ONU through splitters) with bit rates on the order of 100

Mbps [10].

1.2 Recent Trends in Wireless Access Networks

Another promising access solution is a wireless network. Recently, we have seen

tremendous growth in the research and deployment of various wireless technologies. There

are three major techniques that have been employed for wireless access networks worldwide,

viz., “Wireless Fidelity” (known as WiFi), “Worldwide Interoperability for Microwave Ac-

cess” (known as WiMAX), and “Cellular Network”. These technologies have their own

advantages and disadvantages.

WiFi is one of the most popular wireless technologies (standards: IEEE 802.11a/b/g) [11],

and it is mainly used for wireless local-area networks (WLAN). WiFi can operate in both

the “Infrastructure” and “Ad-Hoc” modes. In infrastructure mode, a central authority,


known as Base Station (BS) or Access Point (AP)2, is required to manage the network.

But, in ad-hoc mode, the users are self-managed and there is no concept of an adminis-

trator. WiFi technology can exploit the flexibility of “multi hopping”. WiFi offers low

bit rate (max 54/11/54 Mbps for 802.11a/b/g respectively) and limited range (typically

100 meters). In recent years, wireless mesh (standard: IEEE 802.11s) has evolved as a

cost-effective alternative (to fiber access network) in the federated and community network.

WiMAX (standard: IEEE 802.16) [12] is gaining rapid popularity. It is essentially

a point-to-multipoint broadband wireless access service. WiMAX can be used efficiently

for single-hop communication (for multi-hop, WiMAX suffers from higher delay and lower

throughput). It provides high bandwidth and uses less-crowded spectrum. Thus, WiMAX is

particularly suitable for wireless metropolitan-area networks (WMAN), because of its high

bit rate and long range. It can support data rates upto 75 Mbps in a range of 3-5 km, and

typically 20-30 Mbps over longer ranges. Transmission over longer distances significantly

reduces bit rates due to the fact that WiMAX does not work efficiently for non-line-of-sight

(NLOS) communications. WiMAX Base Stations (BS) can be placed indoor (installed

by customer) or outdoor (installed by network operator) to manage the wireless network.

Recently, WiMAX is being examined as an alternative for fixed wired infrastructures, viz.,

DSL and cable modem, to deliver “last mile” broadband access to users.

There are several industry efforts to build WiMAX architecture for commercial

deployment, and a few examples are stated below. In the United States, Sprint Nextel

holds the licence in 2.5 GHz band to build a nationwide wireless access network, which is

expected to cover 100 million US customers in 2008 [13]. Towerstream has deployed wireless

networks, which have bit rates of tens of Mbps, in several locations in the US [14]. Among

other regions, Intel WiMAX trials have been launched in several locations in Europe and

India in collaborations with local service providers [15].

Cellular technology is used for low-bit-rate applications (max 2 Mbps). A cellular

network is mainly used to carry voice traffic, and is not optimized for data traffic. In

addition, the data component of the cellular network, such as the High-Speed Downlink2Throughout this dissertation, we shall use the words Base Station (BS) and Access Point (AP) inter-

changeably.


Packet Access (HSDPA) and High-Speed Uplink Packet Access (HSUPA), jointly known

as High-Speed Packet Access (HSPA) in the 3G (3rd Generation) evolution can deliver

a downstream bandwidth of up to 14 Mbps and upstream bandwidth of up to 5 Mbps.

A more advanced version, namely HSPA+, will offer a downlink speed of up to 40 Mbps

and up to 10 Mbps in upstream direction. They use Federal Communications Commission

(FCC) regulated expensive spectrum (licensed band) with 3G [16] and B3G (Beyond 3rd

Generation), namely 4G (4th Generation) [17] standards. WiFi technology, on the other

hand, uses the free Industrial Scientific and Medical (ISM) band, while WiMAX uses both

licensed and ISM bands.

Several recent studies in the field of wireless access networks have focussed on the

integration of WiFi and cellular. This type of architecture exploits the advantages of both

WiFi and cellular [18]. An integrated cellular infrastructure with ad-hoc relaying at strategic

locations can provide better load balancing by diverting the traffic from a heavily-congested

cell to a neighboring relatively-less-congested cell, if possible. This type of architecture has

other benefits too. It is more flexible because it can extend the traditional cellular coverage.

It also helps in the interoperability of managing the two diverse technologies: ad-hoc and

cellular. Integration helps in improving the fault tolerance of the system, by improving its

reliability. It also improves the transmission rate by exploiting the additional bandwidth of

the ad-hoc network [19–21].

1.3 Radio-on-Fiber – A Precursor of WOBAN

Unlike WOBAN, which mainly focuses on the networking aspect of the wireless-

optical converged architecture, the radio-on-fiber (ROF) technology has its root in the

communication challenges of sending radio signals over fiber. The radio signals in ROF

can be effectively carried over an existing optical fiber infrastructure (saving “last mile”

costs) by means of the “Hybrid Fiber Radio” (HFR) enabling technology. Thus, challenges

with ROF (which are complementary to WOBAN’s research focus) are: (1) to design better

transmission equipments, (2) to improve the signal’s power gain, (3) to develop sophisticated

signal modulation/demodulation and up/down conversion techniques, etc.


Recent research works propose ROF-based technologies in millimeter-waveband

(mm-waveband) [22, 23], and demonstrate integrated broadband services in a ROF down-

stream link [24]. HFR helps to reduce the design complexity at the Remote Antenna

Units (RAU) (consequently leading to inexpensive and simple RAUs), because up/down-

conversion, multiplexing/demultiplexing, modulation/demodulation, etc. can be performed

at a central office (also known as HFR head end). It is also possible to transmit multiple

radio signals over the same fiber. The ROF-enabled access network may have different

topologies such as “optical star – radio point-to-point”, “optical tree – radio star”, “optical

star – radio cellular”, etc. Among various research efforts, the work in [25] proposes a dy-

namic wavelength allocation scheme for bursty traffic load for WDM fiber-radio ring access

networks. Reference [26] demonstrates simultaneous wireline (600 MHz) and wireless (5.5

GHz) data transmission in a hybrid fiber-radio access network over cable service interface

specification (DOCSIS), and a scheme for quantizing radio signals over fiber is investigated

in [27]. A good overview of cost-effective wireless-over-fiber technology is provided in [28].

For more information on ROF as well as for other topics on optical-wireless integration,

please refer to [29].

1.4 Wireless-Optical Broadband Access Networks (WOBAN)

The concept of a hybrid wireless-optical broadband access network (WOBAN) is

a very attractive one. This is because it may be costly in several situations to run fiber

to every home (or equivalent end user premises) from the telecom Central Office (CO);

also, providing wireless access from the CO to every end user may not be possible because

of limited spectrum. Thus, running fiber as far as possible from the CO towards the end

user and then having wireless access technologies take over may be an excellent compromise.

How far should fiber penetrate before wireless takes over is an interesting engineering design

and optimization problem.

We will elaborate the concept of WOBAN, its architecture, and protocols in details

in the following chapters.


1.5 Research Contributions

This dissertation makes five important contributions to the study and understand-

ing of hybrid wireless-optical broadband access networks. We briefly state these contribu-

tions in the following subsection.

1.5.1 WOBAN Architecture and Research Challenges

Chapter 2 first introduces an architecture and a vision for WOBAN, and artic-

ulates why the combination of wireless and optical presents a compelling solution that

optimizes the best of both worlds. While we briefly touch upon the business drivers, the

main arguments are based on technical and deployment considerations. Consequently, the

rest of the contribution reviews a variety of relevant research challenges, namely network

setup, network connectivity, and fault-tolerant behavior of WOBAN.

In network setup, we investigate the design of a WOBAN where the back end is a

wired optical network, the front end is managed by a wireless connectivity, and, in between,

the tail ends of the optical part [known as Optical Network Unit (ONU)] communicate

directly with wireless base stations (known as “gateway routers”). We outline algorithms

to optimize the placement of ONUs in a WOBAN, and tabulate the pros and cons of each

one of them. Then, we examine the WOBAN’s routing properties (network connectivity),

discuss the various routing algorithms in brief, and summarize the idea behind fault-tolerant

design of such hybrid networks. These aspects are developed in greater detail in the later

chapters.

1.5.2 Network Planning and Setup for WOBAN

In Chapter 3, we study the WOBAN deployment scenario and first investigate

a greedy algorithm to optimize the placement of multiple ONUs. To obtain some rep-

resentative data on locations of typical wireless users, we have conducted a survey on the

distribution and types of wireless routers in the Wildhorse residential neighborhood of North

Davis, CA. We also formulate the multiple-ONU deployment problem using a combinatorial

optimizer, viz., simulated annealing, and study the accuracy of this solution. Having found


the suitable locations for ONUs, we compare the expenditures of a WOBAN vs. a wired

access solution, namely Passive Optical Network (PON). To capture the challenges behind a

complete WOBAN setup, we propose and investigate a joint optimization algorithm (called

the combined heuristic), which considers design aspects of both the wireless front end, such

as avoiding interference among neighboring BSs/APs, and the optical back end, such as

minimizing expensive fiber layout.

1.5.3 Constraint Programming Model for WOBAN Deployment

Chapter 4 proposes and investigates the characteristics of an analytical model (by

means of constraint programming) for network deployment, namely optimum placements

of Base Stations (BS) and Optical Network Units (ONU) in a WOBAN (called the Primal

Model or PM). We develop several constraints to be satisfied: BS and ONU installation con-

straints, user assignment constraints, channel assignment constraints, capacity constraints,

and signal-quality and interference constraints. To solve this Primal Model (PM) with rea-

sonable accuracy, we use Lagrangean Relaxation to obtain the corresponding Lagrangean

Dual model. We solve this dual problem to obtain a lower bound of the primal problem. We

also develop an algorithm (called the Primal Algorithm) to solve the PM to obtain an upper

bound. Via simulation, we compare this PM to the joint optimization heuristic (called the

combined heuristic), proposed in Chapter 3, and verify that the placement problem is quite

sensitive to a set of chosen metrics.

1.5.4 WOBAN Connectivity and Routing

Chapter 5 explores a major research opportunity in developing an efficient routing

algorithm for the wireless front end of WOBAN. We propose and investigate the characteris-

tics of “Delay-Aware Routing Algorithm (DARA)” that minimizes the average packet delay

in the wireless front end of a WOBAN. In DARA, we model wireless routers as queues and

predict wireless link states periodically. Our performance studies show that DARA achieves

less delay and congestion, and improved load balancing compared to traditional approaches

such as minimum-hop routing algorithm (MHRA), shortest-path routing algorithm (SPRA),

and predictive throughput routing algorithm (PTRA).


1.5.5 WOBAN Fault Tolerance and Restoration

Chapter 6 explores WOBAN’s fault-tolerant behavior. Due to its hierarchical

architecture, WOBAN can be subjected to multiple failures which can disrupt the network.

We investigate how WOBAN can combat failures and propose a “Risk-and-Delay Aware

Routing Algorithm (RADAR)” (an extension to DARA) to minimize the packet loss if a

failure occurs.

1.6 Organization

Chapter 2 defines WOBAN, proposes its architecture, and provides a comprehen-

sive outline of its research aspects, coupled with various design models and pros and cons of

efficient protocols to manage the network. This work has been published in the IEEE/OSA

Journal of Lightwave Technology (JLT), November 2007 [30].

Chapter 3 focuses on heuristics – greedy algorithm and simulated annealing –

to plan the network, and envisions how the WOBAN can be deployed to serve a typical

residential neighborhood, e.g., the Wildhorse neighborhood of Davis. This work has been

accepted for publication in IEEE Journal on Selected Areas in Communications (JSAC) [31],

after presentations at the IEEE/OSA Optical Fiber Communications Conference (OFC),

March 2006 [32] and IEEE Conference on Optical Internet (COIN), July 2006 [33].

Chapter 4 explores the constraint programming model, coupled with Lagrangean

Relaxation, to achieve an optimal design solution for WOBAN, and estimates the cost of

WOBAN deployment. This work has been accepted for publication in IEEE/ACM Transac-

tions on Networking (ToN) [34], after presentations at the IEEE Wireless Communications

and Networking Conference (WCNC), March 2007 [35].

Chapter 5 examines the novelty of WOBAN’s connectivity and develops a “Delay-

Aware Routing Algorithm”, called DARA, which helps WOBAN to better serve delay-

sensitive applications better. This work has been published for IEEE Network Magazine,

May 2008 [36], after presentations at the IEEE International Conference on Communications

(ICC), June 2007 [37].

Chapter 6 develops a “Risk-and-Delay Aware Routing Algorithm” (an extension to


DARA), called RADAR, to exploit the fault-tolerant behavior of WOBAN. It also introduces

WOBAN’s self-healing property. This work has been presented in IEEE/OSA Optical Fiber

Communications Conference (OFC), March 2007 [38].

Chapter 7 concludes this dissertation.

12

Chapter 2

WOBAN Architecture and

Research Challenges

2.1 Introduction

Hybrid wireless-optical broadband access network (WOBAN) is a promising ar-

chitecture for future access networks. The WOBAN has been gaining increasing attention

and early versions of its wireless part are being deployed as municipal access solutions to

eliminate the wired drop to every wireless router at customer premises. This architecture

saves on network deployment cost because fiber need not penetrate to each end user, and

it extends the reach of emerging optical access solutions such as Passive Optical Networks

(PON).

The rest of this chapter is organized as follows. Section 2.1.1 introduces a novel

architecture for broadband access solution – WOBAN, which captures the best of both

the optical and wireless worlds, and Section 2.1.2 articulates the motivation behind it.

Section 2.2 summarizes (in Table 2.1) the business drivers deploying an early incarnation of

WOBAN all over the world. In Section 2.3, we briefly discuss and evaluate the algorithms for

WOBAN deployment (network setup). In Section 2.4, we discuss the routing characteristics

of a WOBAN, and study the pros and cons of various routing algorithms. Section 2.5

discusses the fault-tolerant behavior of a WOBAN and Section 2.6 concludes this chapter.

Chapter 2: WOBAN Architecture and Research Challenges 13

Details of these approaches are discussed in the later chapters.

Therefore, this chapter reviews in brief our research works on WOBAN. Sec-

tion 2.1.1 develops the WOBAN architecture. Section 2.3 discusses WOBAN’s network

setup scenarios (which will also be elaborated in Chapter 3 and Chapter 4). WOBAN’s

connectivity and routing algorithms are studied in Section 2.4 (which will also be elabo-

rated in Chapter 5); and WOBAN’s fault-tolerant properties are outlined in Section 2.5

(which will also be elaborated in Chapter 6).

Next we propose and investigate a novel architecture for WOBAN.

2.1.1 Hybrid Wireless-Optical Broadband Access Network Architecture

The WOBAN architecture can be employed to capture the best of both worlds —

(1) the reliability, robustness, and high capacity of wireline optical communication, and (2)

the flexibility (“anytime-anywhere” approach) and cost savings of a wireless network. A

WOBAN consists of a wireless network at the front end, and it is supported by an optical

network at the back end (see Fig. 2.1). Noting that the dominant optical access technology

today is the passive optical network (PON), different PON segments can be supported by

a telecom Central Office (CO), with each PON segment radiating away from the CO. The

head end of each PON segment is driven by an Optical Line Terminal (OLT), which is

located at the CO. The tail end of each PON segment will contain a number of Optical

Network Units (ONU), which typically serve end users in a standard PON architecture.

For the wireless portion of the WOBAN, the ONUs will drive wireless Base Stations

(BS) or Access Points (AP). The wireless BSs that are directly connected to the ONUs are

known as wireless “gateway routers”, because they are the gateways of both the optical and

the wireless worlds. Besides these gateways, the wireless front end of a WOBAN consists

of other wireless routers/BSs to efficiently manage the network. Thus, the front end of

a WOBAN is essentially a multi-hop wireless mesh network (WMN) with several wireless

routers and a few gateways (to connect to the ONUs and consequently, to the rest of the

Internet through OLTs/CO). The wireless portion of the WOBAN may employ standard

technologies such as WiFi or WiMAX. Note that providing wireless access from the CO

to every end user may not be possible because of limited spectrum. Thus, being driven


Figure 2.1: A hybrid wireless-optical broadband access network (WOBAN) architecture.

by high-capacity optical fiber infrastructure at the back end, a WOBAN can potentially

support a much larger user base with high bandwidth needs compared to traditional wireless

solutions.

In a typical WOBAN, end users, e.g., subscribers with wireless devices at individual

homes, are scattered over a geographic area. An end user sends a data packet to one of its

neighborhood wireless routers. This router then injects the packet into the wireless mesh of

the WOBAN. The packet travels through the mesh, possibly over multiple hops, to one of

the gateways (and to the ONU) and is finally sent through the optical part of the WOBAN


to the OLT/CO.

WOBAN is a multi-domain hybrid network. It is essentially an integrated tree-

mesh architecture. It assumes that an OLT is placed in a telecom central office, and it feeds

several ONUs. Thus, from ONU to the CO, WOBAN has a traditional fiber network; and,

from ONUs, end users are wirelessly connected (in single-hop or multi-hop fashion). Figure

2.1 captures a WOBAN architecture. The optical part of WOBAN assumes a tree, while a

mesh is envisioned in its front-end wireless part.

In this multi-domain architecture, the gateways (wireless routers that are physi-

cally connected to ONUs) are primary aggregation. Multiple wireless routers can associate

a single gateway. The gateways and wireless routers together form the front-end mesh. In

the back end, OLT acts as the parent of the tree with gateways as leaves and ONUs as

children in between. The ONUs are higher aggregation since multiple gateways can connect

to one ONU. Consequently, the OLT is the highest aggregation for WOBAN since it can

drive multiple ONUs before the traditional metro/core aggregation for rest of the network.

2.1.2 Why is WOBAN a Compelling Solution?

The advantages of a WOBAN over the wireline optical and wireless networks

have made the research and deployment of this type of network more attractive. These

advantages can be summarized as follows.

1. A WOBAN can be very cost effective compared to a wired network. The architec-

ture (see Fig. 2.1) demonstrates that we do not need expensive “fiber-to-the-home

(FTTH)” connectivity, because installing and maintaining the fiber all the way to

each user could be quite costly. (Note that, according to the 2001 U.S. census figures,

there are 135 million houses in the U.S., and the estimates are that to wire 80% of

the U.S. households with broadband would cost anywhere between 60 - 120 billion

US Dollars, whereas, with wireless, the estimates are that it would cost only 2 billion

US Dollars.) In WOBAN, a user will connect to its neighborhood ONU in a wireless

fashion, possibly over multiple hops through other wireless routers. At the ONU,

the wireless user’s data will be processed and sent to the OLT using the optical fiber


infrastructure.

2. The wireless part of this architecture allows the users inside the WOBAN to seam-

lessly connect to one another. So, a WOBAN is more flexible than the optical access

network. The “anytime-anywhere” approach is also applicable to the WOBAN. Thus,

WiFi is a convenient technology for the front end of the WOBAN, so that we can ex-

ploit its flexibility and multi-hopping capability. WiMAX is an alternative (to WiFi)

for the front end of WOBAN, in which, apart from its flexibility, we can also take

advantage of its higher bit rate compared to WiFi.

3. A WOBAN should be more robust than the traditional wireline network. In a tradi-

tional PON, if a fiber connecting the splitter to an ONU breaks (see Fig. 2.1), that

ONU will be down. Even worse, if a trunk from OLT to the splitter breaks, all the

ONUs (along with the users served by the ONUs) will fail. But, in a WOBAN, as the

users have the ability to form a multi-hop mesh topology, the wireless connectivity

may be able to adapt itself so that users may be able to find a neighboring ONU

which is alive. Then, the users can communicate with that ONU; and that ONU, in

turn, will communicate with another OLT in the CO.

4. Due to its high-capacity optical trunk, the WOBAN will have much higher capacity

than the relatively low capacity of the wireless network.

5. A WOBAN will be more reliable than the wireless network. This, in turn, will help

in reducing the problem of congestion and information loss in a WOBAN compared

to the current wireless network. Also, a user’s ability to communicate with any other

ONU in its vicinity, if its primary ONU breaks or is congested, gives the WOBAN a

better load-balancing capability.

6. The WOBAN is “self organizing” because of its fault-tolerant capability (Item #3

above) and because of its robustness with respect to network connectivity and load

balancing features (Item #5 above).

7. In many developing regions of the world, fiber is deeply deployed (within 20 km)

even in the rural areas, but the cost to provide wireline broadband connectivity is


prohibitively expensive, time consuming, and difficult to maintain. In such scenarios,

the governments may decide to either build or provide incentives to the operators to

deploy WOBAN-like architectures.

2.2 WOBAN’s Early Incarnations

Noting that a WOBAN is a high-capacity cost-effective broadband network, re-

cently its early incarnations (wireless front end of WOBAN) are being deployed as an access

solution in many cities around the world. We capture a sample of the current activities of

WOBAN in Table 2.1 [39–46]. Thus, a WOBAN deployment is an important development

in today’s network scenario.

In Table 2.1, we observe that different network operators deploy different archi-

tectures for the front end (wireless part) of WOBAN. The simplest architecture is the flat

deployment of wireless routers with a single radio and omni-directional antenna. The gate-

way routers are connected to the wired back haul and then to the rest of the Internet. Some

of these gateways also have Optical Carrier (OC) ingress ports to connect to the optical part

of the network. A few of the network operators deploy hierarchical or multi-layered infras-

tructure for the front end of WOBAN. Wireless routers and gateways may also be equipped

with multiple radios and directional antenna. Some of the routers are even equipped with

“spatially adaptive” MIMO-based antenna array. Advanced network features, viz., point-

to-multipoint (P2MP) fiber optic connections, L2 VLANs, and intermeshing through fiber,

etc., are often embedded in the back end of WOBAN.

Since WOBAN is a marriage of two powerful techniques, there are a lot of inter-

esting research and implementation challenges in network planning and operation, which

we will discuss next.


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2.3 Network Setup: A Review of Placement Algorithms in

WOBAN

The network performance largely depends on the deployment of ONUs, i.e., the

gateway routers where the optical and wireless parts meet. Proper deployment of ONUs is

critical to the cost optimization of WOBAN. To tackle this problem, we review placement

algorithms for deploying multiple ONUs in a WOBAN. Given the locations of the wireless

users, these algorithms focus on how to find the “good” placement of multiple ONUs in a

cost-effective manner. Below we briefly touch upon the various algorithms of ONU place-

ment and compare their pros and cons. Chapter 3 and Chapter 4 will examine this topic

in detail.

2.3.1 Random and Deterministic Approaches

Random placement of ONUs is the simplest way of deploying the network. This

is a trial-and-error method, where after dividing the network into multiple non-overlapping

regions, ONUs are sprinkled randomly in each region. This scheme does not return an

optimized-cost setup and may not ensure proper connectivity (this is because, while sprin-

kling randomly, ONUs may bunch up in parts of the network, leaving other parts void).

Deterministic placement, on the other hand, is a predetermined scheme, where

after dividing the network into multiple non-overlapping regions, ONUs are placed in the

“centers” of each region. Deterministic scheme works well for a symmetric network, and

has a much lower processing requirement. There is no prior optimization involved and it

does not fit well for a network with a non-uniform distribution of users.

2.3.2 Greedy Approach

The Greedy Algorithm (Greedy) is a divide-and-conquer method to partition the

network (see [32] for details). The goal of Greedy is to place ONUs in a WOBAN such

that the average cost over all users with respect to a neighborhood ONU is optimized. The

algorithm starts with a given distribution of wireless users. These users are primarily in

the residential and business premises, so they have little or no mobility. Greedy considers


a number of predetermined points as possible initial candidates to place the ONUs. Then,

it finds the distances of all ONUs with respect to a user (whose coordinates are known

beforehand). For each user, Greedy forms an ordered set (in ascending order), with the

user’s distances from ONUs as the set’s elements. Then, it identifies the primary ONU,

which is the closest (minimum distance from the user). Finally, Greedy obtains a set of

users for primary ONUs (call these users “premium users” for that ONU), and optimizes

the placement of each primary ONU with respect to its premium users.

2.3.3 Combinatorial Optimization: Simulated Annealing Approach

The Greedy Algorithm is a heuristic, which performs local optimization of an

individual ONU after the identification of premium users for that ONU. The solution is not

globally optimal. For improved solution, a better approach is needed. Next, we summarize

how the ONU placement problem can be retrofitted to a combinatorial optimizer, viz.,

simulated annealing (SA) [47,48].

In SA, the initial placement of ONUs is obtained by the Greedy Algorithm as in [32]

(known as Initialization Phase of SA). The purpose of this global optimization is to find the

minimum average cost for all the users (not only the premium users) with respect to multiple

ONUs. So, SA relocates the ONUs with a small random amount (Perturbation Phase of

SA). After perturbation, the algorithm calculates the new cost of ONU placement (Cost

Calculation Phase of SA) and observes how the new cost of ONU deployment changes with

respect to the old cost. If the new cost of deployment is lower, SA accepts the relocation of

ONUs; else it accepts the relocation with a certain probability (Acceptance Phase of SA). SA

iterates the same process until there is no further cost improvement (Update Phase of SA).

Then, the algorithm is said to be in the “equilibrium state”, where no more perturbation will

reduce the cost of deployment any further. Details of the Simulated Annealing Algorithm

can be found in Chapter 3.

2.3.4 Joint Optimization: Constraint Programming Approach

A joint optimization approach considers the design-interplay between both optical

and wireless domains together. A proper pre-deployment optimization strategy can actually


save expensive optical and wireless resources (and, in turn, dollars) needed for this type of

network. Thus, a constraint programming model, called Primal Model (PM) is investigated

in Chapter 4.

PM focuses on the optimum simultaneous placement of BSs and ONUs in the front

end, and the fiber layout from BSs to ONUs and from ONUs to OLT/CO in the back end. It

explores an analytical model that considers the cost of ONUs and BSs, and the cost of laying

fiber. This is a pre-deployment network-optimization scheme, where the cost of WOBAN

design (e.g., in dollars) is minimized by placing reduced number of BSs and ONUs, and

planning an efficient fiber layout. In order for proper operations of WOBAN, PM considers

several constraints to be satisfied: BS and ONU installation constraints, user assignment

constraints, channel assignment constraints, capacity constraints, and signal-quality and in-

terference constraints. The network operators can derive their costs of WOBAN deployment

from the proposed model.

We summarize the performances of various placement algorithms in WOBAN in

Table 2.2.


Tab

le2.

2:P

ros

and

cons

ofva

riou

spl

acem

ent

sche

mes

inW

OB

AN

.

Pla

cem

ent

Sch

eme

Obje

ctiv

eSolu

tion

Quality

Pro

cess

ing

tim

eC

om

men

ts(i

nbri

ef)

Random

Pla

cem

ents

ofO

pti

cal

Net

work

Unit

s(O

NU

)in

WO

BA

N.

Wors

eC

onst

ant

Sim

ple

.Tri

al-and-e

rror

met

hods

may

be

use

d.

Det

erm

inis

tic

Bet

ter

Const

ant

Work

sw

ellfo

rsy

mm

etri

cto

polo

gy.

Pre

-det

erm

ined

pla

cem

ent.

No

pri

or

opti

miz

ati

on.

Gre

edy

Alg

ori

thm

Good

Lin

ear

(in

pra

ctic

alca

ses)

Low

com

ple

xity.

Div

ide-

and-c

onquer

heu

rist

ic.

Good

solu

tion

for

uniform

dis

trib

uti

on

ofuse

rs.

Sim

ula

ted

Annea

ling

Impro

ved

over

Gre

edy

Dep

ends

on

conver

gen

cecr

iter

iaC

om

bin

ato

rialopti

miz

er.

Impro

ved

solu

tion

over

Gre

edy.

May

not

conver

ge

for

dis

conti

nuous

cost

model

.

Pri

malM

odel

Opti

mum

setu

pof

ON

Us/

BSs.

Opti

mal

Ver

yhig

hC

om

ple

xanaly

tica

lso

luti

on.

Consi

der

sse

ver

alco

nst

rain

ts.

Model

pre

dic

tsse

tup

cost

sin

dollars

.


2.4 Network Connectivity: A Review of Routing Algorithms

in WOBAN

Once the WOBAN is setup, how to efficiently route information (data packets)

through it is an important and challenging problem. Note that the characteristics of a

WOBAN’s front end wireless mesh is different from that of the traditional wireless mesh.

In a traditional wireless mesh, the connectivity changes due to users’ mobility and a wireless

link goes up and down on-the-fly. On the other hand, since the WOBAN primarily is a

network of residential and business users, its connectivity pattern in the wireless front end

can be pre-estimated.

An end user sends a data packet to one of its neighborhood routers. This router

then injects the packet into the wireless mesh of the WOBAN. The packet travels through

the mesh, possibly over multiple hops, to one of the gateways/ONUs and is finally sent

through the optical part of the WOBAN to the OLT/CO and then to the rest of the

Internet. In the downstream direction, from OLT/CO to an ONU (back end optical part), a

WOBAN is a broadcast network, and from ONU/gateway to a user (front end wireless part),

a WOBAN is a unicast network. In the upstream direction, from a user to a gateway/ONU

(front end wireless part), WOBAN is an anycast network, and from ONU to OLT/CO

(back end optical part), WOBAN follows the traditional multipoint access control protocol

to carry packets. Next we briefly review the routing algorithms in the front end wireless

mesh of WOBAN. These algorithms run inside each wireless router and gateway in the

network. Chapter 5 will examine this topic in detail.

2.4.1 Minimum-Hop and Shortest-Path Routing Algorithms (MHRA and

SPRA)

The minimum-hop routing algorithm (MHRA) and the shortest-path routing algo-

rithm (SPRA) are widely used in the wireless part of a WOBAN (because they are easy to

implement), where the link metric in MHRA is unity, and in SPRA, it is generally inversely

proportional to the link capacity. MHRA and SPRA work on the shortest-path principle

without generally considering other traffic demands on the network. Therefore, MHRA


and SPRA could suffer from several routing limitations, viz., increased delay, poor load

balancing, and high congestion in a link or along a segment (consisting of multiple links).

2.4.2 Predictive Throughput Routing Algorithm (PTRA)

Recent approaches also consider solution providers’ patented routing algorithms.

Predictive-throughput routing algorithm (PTRA) is one such protocol (where PTRA is

similar to “Predictive Wireless Routing Protocol (PWRP)” [39]). We use the name “PTRA”

instead of “PWRP” in the study because the wording in PTRA is more expressive.

Unlike MHRA and SPRA, PTRA is not based on the shortest-path routing prin-

ciple. PTRA is a link-state based routing scheme, and it chooses the path (from a set of

possible paths between a user-gateway pair) that satisfies the overall throughput require-

ments, as explained below. PTRA takes measurement samples of link rates periodically

across wireless links. Given a user-gateway pair, the algorithm computes available paths.

Based on the history of samples, PTRA dynamically predicts link condition and then es-

timates the throughput of each path. It chooses the path that gives a higher estimated

throughput [39]. Although PTRA is proposed and implemented for only carrying packets

in the wireless part of a WOBAN, the major problem in PTRA is that the packet may end

up traveling inside the mesh longer than expected (as PTRA does not take into account

packet delay). So, PTRA is not suitable for delay-sensitive services as the corresponding

packets can take longer routes (as long as the route satisfies the throughput criteria).

2.4.3 Delay-Aware Routing Algorithm (DARA)

The routing in the wireless part of a WOBAN mesh deals with packets from a

router to a gateway (and vice versa). A wireless routing path consists of two parts: (1) the

associativity of a user to a nearby wireless router in its footprint, and (2) the path from

this (ingress) router to a suitable gateway (through the wireless mesh). Delay-aware routing

algorithm (DARA) is a proactive routing approach that focuses on the packet delay (latency)

in the front end (wireless mesh) of the WOBAN, i.e., the packet delay from the router to

the gateway (attached to a ONU) and vice versa. The packet delay could be significant


as the packet may travel through several routers in the mesh before finally reaching the

gateway (in the upstream direction) or to the user (in the downstream direction).

The larger the mesh of the WOBAN, the higher is the expected delay. DARA ap-

proximately models each wireless router as a standard M/M/1 queue [49] and predicts the

wireless link states (using link-state prediction or LSP) periodically. Based on the LSP infor-

mation, DARA assigns link weights to the wireless links. Links with higher predicted delays

are given higher weights. Then, DARA computes the path with the minimum predicted

delay from a router to any gateway and vice versa. While traveling upstream/downstream,

a router/gateway will send its packet along the computed path only if the predicted delay

is below a predetermined threshold, referred to as the delay requirement for the mesh; oth-

erwise, DARA will not admit the packet into the mesh. DARA shows how choosing a path

from a set of paths (whose delays are below the delay requirement) can alleviate congestion

and achieve better load balancing. The details of DARA can be found in Chapter 5.

We briefly summarize the performance of the various routing algorithms in Ta-

ble 2.3.

In the optical back end, traditional multipoint control protocol (MPCP) can be

used in the upstream direction (from ONUs to OLT). Wireless gateways continue to send

the packets to an ONU, and the ONU, after accumulating several packets from gateways,

will send a REPORT message to the OLT (indicating its volume of accumulated packets).

The OLT, on getting this REPORT, grants a portion of the shared upstream bandwidth to

the ONU through a GATE message. On the other hand, the downstream of optical back

end in WOBAN (OLT to ONUs) can be a broadcast network, where a packet from OLT

is broadcast to all the ONUs in its downstream tree, but only the destination ONU will

“selectively” process the packet while other ONUs will discard it, as in a traditional PON

architecture [2].


Tab

le2.

3:P

ros

and

cons

ofva

riou

sro

utin

gal

gori

thm

sin

the

wir

eles

spa

rtof

aW

OB

AN

.

Rou

ting

Obje

ctiv

eLin

kA

lter

nati

vePer

form

ance

algo

rith

mpr

edic

tion

path

Del

ayT

hrou

ghpu

tH

opco

unt

Loa

dba

lanc

ing

Ris

kus

edus

edH

LH

LH

LH

Law

aren

ess

MH

RA

Hop

min

imiz

atio

n;N

oN

o√×√

×√

√×

×N

oun

ity

link

wei

ght.

SPR

ASh

orte

stpa

th;

No

No

√×√

×√

√×

×N

oin

vers

e-ca

paci

tylin

kw

eigh

t.P

TR

AT

hrou

ghpu

tN

oY

es××√

√×

×√

√N

oop

tim

izat

ion.

DA

RA

Del

ayY

esY

es√√√

√√

×√

√N

om

inim

izat

ion.

RA

DA

RM

inim

ize

dela

yY

esY

es√√√

√√

×√

√Y

esan

dpa

cket

loss

.H

:H

igh

load

(0.5

-0.9

5),L:Low

load

(0.0

-0.4

9)√

:A

lgor

ithm

perf

orm

sw

ell,×

:A

lgor

ithm

perf

orm

spo

orly

.


2.5 Fault Tolerance: Risk Awareness in WOBAN

The network architecture of a WOBAN has an important characteristic of risk

awareness. It can combat network failures by healing itself quickly. Failures in WOBAN

(and consequently the loss of packets) may occur due to multiple reasons, viz., (1) wireless

router/gateway failure, (2) ONU failure, and (3) OLT failure. Failures may also occur due

to fiber cut, which results in the failure of gateways (if a fiber between an ONU and a

gateway gets cut), ONUs (if a fiber between a splitter and an ONU is cut), and OLTs (if a

fiber between an OLT and a splitter is cut).

Below we review the fault-tolerant aspects of a WOBAN and briefly touch upon

the algorithm to cope up with these failures. Chapter 6 will examine this topic in detail.

2.5.1 Risk-and-Delay Aware Routing Algorithm (RADAR)

The fault-tolerant property of a WOBAN may handle most of these failure scenar-

ios efficiently. If a gateway fails, then the traffic can be redirected to other nearby gateways.

Similarly, if an ONU fails, and as a consequence, one or multiple gateways fail, the packets

will be rerouted to other “live” gateways that are connected to a “live” ONU. An OLT

failure (and as a consequence, the failure of all ONUs connected to that OLT) is the most

severe. In this case, packets from a large portion of the WOBAN will need to be rerouted.

Thus, to tackle these problems, a “Risk-and-Delay Aware Routing Algorithm

(RADAR)”, which is an extension to DARA, has been developed (the details of which

can be found in Chapter 6). RADAR can handle the multiple failure scenarios. RADAR

differentiates each gateway in the WOBAN by maintaining a hierarchical risk group that

shows which PON group (ONU and OLT) a gateway is connected to. Each gateway is

indexed, which contains its predecessors (ONU and OLT indices as well) to maintain the

tree-like hierarchy of WOBAN. ONUs and OLTs are indexed in similar fashion. To reduce

packet loss, each router maintains a “Risk List (RL)” to keep track of failures. In the

no-failure situation, all the paths are marked “live”. Once a failure occurs, RL will be

updated and paths that lead to the failed gateway(s) will be marked “stale”. Thus, while

forwarding packets, the router will only choose a “live” path. The pros and cons of RADAR


are captured in Table 2.3.

2.6 Summary

In this chapter, we introduced an architecture and a vision for WOBAN, and

articulated why the combination of wireless and optical presents a compelling solution that

optimizes the best of both worlds. While it briefly touched upon the business drivers, the

main arguments focussed on design and deployment considerations.

We discussed network setup, network connectivity, and fault-tolerant character-

istics of the WOBAN. In network setup, we proposed and investigated the design of a

WOBAN where the back end is a wired optical network, the front end is configured by

wireless connectivity, and, in between, the tail ends of the optical part [known as Optical

Network Units (ONUs)] communicate directly with the wireless base stations (known as

“gateway routers”). We summarized algorithms to optimize the placement of ONUs in a

WOBAN deployment scenario. We also evaluated the pros and cons of the various routing

algorithms (network connectivity) in a WOBAN, including its fault-tolerant characteristics

and presented some novel concepts that are better suited for such hybrid networks.

29

Chapter 3

Network Planning and Setup for

WOBAN

3.1 Introduction

The network performance of WOBAN depends on its proper deployment. A

WOBAN deployment is more challenging than only an optical or a wireless access net-

work deployment. This is because of the design interplay between two very diverse access

technologies (optical and wireless). In addition, the network designer has to ensure that

both parts are well designed and neither part is over-designed (with excess resources) nor

under-designed (a resource bottleneck). However, the research on traditional access network

setup is an excellent pointer to begin with.

3.1.1 Related Literature

Although a few research activities are reported on WOBAN design [31–35], re-

search on traditional access network placements can be a good starting point. Thus, Ta-

ble 3.1 summarizes the research on network setup, where the architecture is mainly focused

on the wireless network. We observe that the placement research can be broadly divided

into two categories: indoor and outdoor locations. For both categories, several techniques

have been employed, e.g., iterative methods (viz., quasi-Newton in [50], linear regression

Chapter 3: Network Planning and Setup for WOBAN 30

and least square in [56], etc.), pruning-searching techniques (viz., Hooke-Jeeves in [50],

Nelder-Mead in [51], etc.), and combinatorial optimizers (viz., genetic algorithm in [54],

tabu search in [58], etc.). Various metrics have been used for network optimization, ranging

from distance (in [52]) to signal strength (in [53]). Some studies also focus on the trial-and-

error deployment of BSs so that no void region (a region with little or no signal coverage)

exists. A campus-wide access network setup is captured in [55].


Tab

le3.

1:R

esea

rch

acti

viti

eson

netw

ork

plac

emen

t.

Res

earc

hW

ork

Obje

ctiv

eSet

ting

Cost

Model

Optim

izati

on

Contr

ibuti

ons

(in

bri

ef)

Sher

ali

etal.

[50]

Optim

um

pla

cem

ents

ofB

ase

Sta

tions

for

min

imiz

ing

the

tota

lco

stof

net

work

setu

p.

Outd

oor

Sig

nalst

rength

Hooke-

Jee

ves

,Q

uasi

-New

ton,H

ill-cl

imbin

gM

inis

um

,M

inim

ax,co

mbin

ati

on

model

;C

aptu

res

single

and

multip

leT

xpro

ble

ms.

Wri

ght

[51]

Indoor

Sig

nalpro

pagati

on

Nel

der

-Mea

dD

irec

tSea

rch

Gen

eric

model

wit

hatt

enuati

on;

Fin

ds

loca

lopti

mum

.M

olina

etal.

[52]

Outd

oor

Dis

tance

Gre

edy,

Gen

etic

,G

reed

y+

Gen

etic

Optim

ized

cellula

rco

ver

age;

Com

bin

ato

rialappro

ach

.H

url

ey[5

3]

Outd

oor

Sig

nal,

Dis

tance

,Tra

ffic

Sim

ula

ted

Annea

ling

Mult

iple

cost

sappro

ach

;C

ellhandover

consi

der

ed.

Nagy

etal.

[54]

Indoor

Motl

ey-K

eenan

path

loss

Gen

etic

Alg

ori

thm

Em

pir

icalm

odel

;H

igh

com

ple

xity.

Hills

[55]

Indoor

Sig

nalst

rength

Tri

al-and-e

rror

Cylindri

caldes

ign

appro

ach

;D

eplo

yed

inC

arn

egie

Mel

lon.

Chen

etal.

[56]

Indoor

Bahl’s

path

loss

Lin

ear

Reg

ress

ion,Lea

stSquare

Both

signal-st

rength

and

loca

tion

aw

are

nes

s;G

ener

icm

odel

wit

hatt

enuati

on.

Kam

enet

skym

etal.

[57]

Outd

oor

Sig

nalst

rength

Uniform

,P

runin

g,Sim

ula

ted

Annea

ling

Em

pir

icalm

odel

;M

inis

um

and

Min

imax

appro

ach

es.

Batt

itiet

al.

[58]

Outd

oor

Sig

nalst

rength

Tabu,H

ill-cl

imbin

g,Sim

ula

ted

Annea

ling

Loca

liza

tion

+si

gnal-co

ver

age

model

;Show

str

ade-

off

bet

wee

ntw

om

etri

cs.


The rest of this chapter is organized as follows. In Section 3.2, we propose and

investigate the characteristics of an algorithm, which finds suitable locations to deploy

multiple ONUs in a WOBAN. This is a Greedy Algorithm based on “local optimization”.

To obtain some representative data on locations of wireless users in a typical residential

neighborhood, we have conducted a survey on the distribution and types of wireless routers

in the Wildhorse neighborhood of North Davis, CA. Section 3.3 reports a small part of

this data and illustrative numerical examples to show the performance of our algorithm.

In Section 3.4, we study the multiple-ONU placement problem using a global optimization

technique, namely Simulated Annealing algorithm. We find that the results from local

optimization are quite close to those obtained from the global optimizer. After determining

suitable locations to deploy ONUs, we explore the expenditure of WOBAN setup, and

compare this with a wired access solution, viz., a PON all the way to each user. Noting

that WOBAN is based on complex interactions of the design inter-play between two diverse

technologies, Section 3.6 captures the design aspects of both the wireless front end and the

optical back end, and proposes a joint optimization algorithm. Section 3.7 summarizes this

chapter.

To begin, we first develop a simple (greedy) algorithm to deploy multiple ONUs

in WOBAN, with much lower processing requirements than iterative solvers and optimizers

discussed below. Unlike some of the approaches in Table 3.1, where discontinuous design

models may lead the iterative solvers and optimizers to get trapped, our algorithm does not

suffer from any non-convergence. Next, we introduce the design methodology of Greedy

Algorithm, and later we will build on it to achieve a complete WOBAN deployment (see

Section 3.5 and 3.6 for details).

3.2 Placement of Multiple ONUs in WOBAN

The network performance largely depends on the deployment of ONUs. Proper

deployment of ONUs is critical to minimize the overall expenditure of a WOBAN setup.

To tackle this problem, we first investigate a greedy algorithm (Greedy) (see Algorithm 1)

with no backtracking for placing multiple ONUs in the network. Given the locations of


the wireless users, our goal is to find the suitable placement of multiple ONUs to minimize

the average distance between wireless users and their closest ONU. So, Greedy is mainly

focused on the average distance (wireless users to ONU) optimization in the WOBAN’s

wireless front end (and the results from Greedy will be used for our joint optimization

algorithm later in Section 3.6). Next, we introduce the cost metric for ONU deployment.

3.2.1 Cost Metric for ONU Deployment

Our primary goal is to place multiple ONUs (say N of them) properly in a ge-

ographic area where the users’ locations are known, e.g., in a residential neighborhood.

Assume that (Xi, Yi) is the position (“cartesian” coordinates) of i-th ONU, which will serve

users at (xj , yj), where j ∈ (1, 2, ..., k′i). We model the cost to deploy the i-th ONU as the

average “Euclidean” distance from that ONU to its users as follows:

CONUi =1k′i∗

k′i∑j=1

√(xj −Xi)2 + (yj − Yi)2

. (3.1)

3.2.2 Greedy Approach

We start with a given distribution of wireless users. Since WOBAN is primarily

a broadband access solution for residential and business premises, user mobility is not a

major concern. We consider a number of locations as possible candidates to place the

ONUs. These initial locations could be chosen randomly or deterministically. The initial

deterministic placement could be achieved by dividing the neighborhood into multiple non-

overlapping regions and then placing the ONUs at the “centers” of each region. Then,

we find the distances of all ONUs with respect to a user (whose coordinates are known

beforehand). For each user, we form an ordered set (in ascending order), with the user’s

distances from ONUs as the set’s elements. Then, we identify the primary ONU, which is

the closest (minimum distance from the user). We obtain a set of users for primary ONUs;

we call these users “premium users” for that ONU, and suitably place each primary ONU

with respect to its premium users. The details of the algorithm are shown in Algorithm 1.


3.2.3 Notations

We list the notations used as follows:

• (xj , yj): User j’s X/Y-coordinates,

• k: Total users in the network,

• (Xi, Yi): ONU i’s X/Y-coordinates,

• N : Number of ONUs,

• dij : Distance between ONU i and user j,

• SDj : Set of ONUs for user j (The elements of this set are the distances between

user j and all the initial ONU locations in the network. This is an ordered set where

elements are in ascending order. From this set, we will choose a user’s primary ONU.),

• ONUPrimaryij : Primary ONU i (minimum-distance ONU) for user j,

• SUi: Set of premium users for a primary ONU i (the elements of this set are the

coordinates of users with minimum distance from ONU i, compared to any other

ONU), and

• k′i: Premium users for ONU i (where∑N

i=1 k′i = k).

Based on the model in Section 3.2.1, this algorithm tries to achieve an efficient

solution in polynomial time.

3.2.4 Running Time

We determine the running time of this algorithm as follows. Running time for line

2 of phase 1 (finding distances) is k ∗ O(N). Running time for line 3 of phase 1 (sorting

ONUs) is k ∗O(NlogN). Running time for line 4 of phase 1 (finding minimum) is k ∗O(1).

Running time for line 1 of phase 2 (finding users) is N ∗ O(k′i). Running time for line

2 of phase 2 (finding mean) is N ∗ O(1). So, the total running time of our algorithm is

k ∗O(N) + k ∗O(NlogN) + k ∗O(1) + N ∗O(k′i) + N ∗O(1) = O(kN + kNlogN + Nk′i) =


Algorithm 1 Greedy Algorithm (for suitable deployment of multiple ONUs in a WOBAN)Input : Locations of users, (xj , yj).Output : Locations of ONUs, (Xi, Yi).Phase 1: Identify Primary ONUs

1. Given locations of k users, (xj , yj),∀j ∈ (1, 2, 3, ..., k), consider N ran-dom/deterministic points, (Xi, Yi),∀i ∈ (1, 2, 3, ..., N), as candidates for initial ONUplacements.

2. Find the distances between a user j and all the ONUs, dij =√(xj −Xi)2 + (yj − Yi)2,∀i ∈ (1, 2, 3, ..., N). Repeat the same for all other

users, i.e., ∀j ∈ (1, 2, 3, ..., k).

3. For user j, sort the distances in ascending order and put them in the sets, SDj ={dij : dij ≤ di′j ,∀(i, i′) ∈ (1, 2, 3, ..., N), i 6= i′}. Repeat the same for all other users,i.e., ∀j ∈ (1, 2, 3, ..., k).

4. Identify a primary ONU for each user, where ONUPrimaryij = mini{SDj}.

Phase 2: Find Placement of Primary ONUs

1. Obtain the set of users (call them “premium users”) for each primary ONU for whichthe distances between the ONU and its users are minimum (compared to all otherONUs), SUi = {(xj , yj) :

√(Xi − xj)2 + (Yi − yj)2 is min}, ∀j ∈ (1, 2, 3, ..., k′i), k

′i ≤

k, for a particular ONU i and ∀i ∈ (1, 2, 3, ..., N).

2. For a set of premium users, (xj , yj),∀j ∈ (1, 2, 3, ..., k′i), place ONUi at the mean of

the users’ X/Y-coordinates. Therefore, (Xi, Yi) =

(∑k′ij=1 xj

k′i,

∑k′ij=1 yj

k′i

). Repeat the

same for all other ONUs, i.e., ∀i ∈ (1, 2, 3, ..., N).


O(kNlogN). It is expected that, in a WOBAN, the number of ONUs will be relatively

small compared to the number of users. Since k >> N in practical cases, this algorithm

will run in O(k) time, which is linear in the number of users in the network.

3.2.5 Analysis

In this algorithm, we start with a divide-and-conquer approach to partition the

network. After identifying the primary ONUs, we try to minimize the average distance

between the ONU and its users. This problem of finding the minimum distance does not

have any closed-form solution. But we will show that, for a uniform random distribution of

wireless users, the chance of obtaining the minimum cost is higher for the Greedy Algorithm

(compared to other schemes of ONU placements, viz., random and deterministic placements)

with a placement which considers the mean as the optimization metric.

The cost of this algorithm is defined as the average distance for all the ONUs over

those users for whom the ONUs are the primary ONUs. So, the cost for a particular ONU

at (X, Y ) can also be expressed as:

CX,Y = E[√

(x−X)2 + (y − Y )2] (3.2)

=∫ (√

(x−X)2 + (y − Y )2)

f(x, y)dxdy, (3.3)

where E[.] stands for the expected value and f(x, y) denotes the probability density function

of (x, y).

Now, CX,Y will be minimized at (X ′, Y ′) = (∫

xfx(x)dx,∫

yfy(y)dy), the mean of

the distribution, where f is random and uniformly distributed around (x, y), and fx(x) and

fy(y) are the marginal distributions of x and y, respectively.

If users at (xj , yj),∀j ∈ (1, 2, 3, ..., k′i) are independent and identically distributed

(i.i.d.) with density f , then C∗X,Y = 1

k′i∗(∑k′i

j=1

√(xj −X)2 + (yj − Y )2

)approximates

CX,Y for any (X, Y ). Therefore, if (X∗, Y ∗) minimizes C∗X,Y , then (X∗, Y ∗) is close to

(X ′, Y ′) (the mean of the distribution) in probability for large k′i. This justifies our choice

to place the ONU at the mean of the X/Y-coordinates of its premium users.

Now, we will show that the local minimum of our cost function is also the global


minimum in this case. To do so, we need to find the second-order partial derivatives of our

cost function, C∗X,Y :

DXX =∂2C∗

X,Y

∂2X(3.4)

=1k′j

k′j∑i=1

(yi − Y )2

((xi −X)2 + (yi − Y )2)32

(3.5)

Similarly,

DY Y =∂2C∗

X,Y

∂2Y(3.6)

=1k′j

k′j∑i=1

(xi −X)2

((xi −X)2 + (yi − Y )2)32

(3.7)

And

DXY = DY X =∂2C∗

X,Y

∂X∂Y(3.8)

=1k′j

k′j∑i=1

−(xi −X)(yi − Y )

((xi −X)2 + (yi − Y )2)32

(3.9)

So,

DXX + DY Y > 0 (3.10)

DXXDY Y −D2XY ≥ 0 (3.11)

Since the matrix of second-order partial derivatives of C∗X,Y is non-negative definite

(n.n.d.), our cost function is a convex function in (X, Y ) (necessary and sufficient condition

for a function to be convex). So, the local minimum here coincides with the global minimum.

To find the exact global minimum, we need to solve the Jacobian of the equations,

which will not produce a closed-form solution. This is where Greedy is useful in finding

the location of ONUs close to a global minimum point. Thus, we can achieve an efficient

placement through Greedy. Our analysis is supported by our performance study reported in

the following section, where Greedy outperforms other simple schemes of ONU placements


Figure 3.1: Performance of various schemes of ONU placement in a WOBAN.

(e.g., random, deterministic, etc.).

3.3 Illustrative Numerical Examples: Greedy Algorithm

We conduct performance studies for a range of inputs, varying the number of ONUs

(2, 3, 4, 5), area in sq-meters (1000x1000, 2500x2500, 5000x5000, 7500x7500, 10000x10000),

and number of wireless users (100, 250, 500, 750, 1000). Each configuration is repeated 25

times (for a total of 2500 experiments), and average results are computed. We compare

four placement schemes: Random placement (after dividing the network into multiple non-

overlapping regions, ONUs are placed randomly in each region), Deterministic placement

(after dividing the network into multiple non-overlapping regions, ONUs are placed in the

“centers” of each region), Greedy with initial random placement (ONUs are placed accord-

ing to the Greedy Algorithm with initial random starting points returned by the random

placement scheme), and Greedy with initial deterministic placement (ONUs are placed ac-

cording to the Greedy Algorithm with initial deterministic starting points returned by the

deterministic placement scheme).


Figure 3.2: Average distance (in meters) of ONUs from their primary users.

Figure 3.1 shows that Greedy with initial deterministic placement produces the

best result (lower Euclidean cost) for 1647 out of 2500 cases. So, nearly 66% of the trials

have Greedy with deterministic placement as the best result. Overall, in nearly 88% (2158

out of 2500) of the trials, Greedy (with initial deterministic/random placement) returns the

lower cost.

Figure 3.2 shows the typical cost (using our chosen metric) for several schemes of

ONU placements (for a test network of 10000x10000 sq-meters, 1000 users, and 3 ONUs),

where Greedy with initial deterministic placement returns the lower network cost. Here,

the cost of random placement has not been shown as its cost is very high (nearly 57%

higher than the cost for Greedy with initial deterministic ONU placement) compared to

other schemes (e.g., Deterministic, Greedy, etc.).

3.3.1 Survey on Wireless Users in Wildhorse, Davis, California

Next, we apply the Greedy Algorithm to a more realistic scenario. We conducted

an extensive survey on the wireless devices in the Wildhorse neighborhood of North Davis.


Figure 3.3: Map of wireless routers in Wildhorse.

The neighborhood has only residential homes in an area of approximately 1150 × 950 sq-

meters. We collected information on the types of wireless devices in these homes (based on

IEEE standards), their configurations (bit rates, channels used, frequencies of operation,

positions on the earth, etc.) and encryption standards. We set our own wireless device (for

data-collection purpose) in “reference mode” (i.e., our device received signals from other

wireless devices nearby). We refreshed the scanning operation from our wireless device once

every second, and finally logged the data. We mapped the wireless routers (see Figures 3.3

and 3.4) in Wildhorse, and a small portion of the collected data is shown in Table 3.2.

These results on wireless users distribution help us to better understand the needs of the

WOBAN architecture.

We ran the same set of schemes discussed at the beginning of this section, but

now with the scanned input of 310 Wildhorse wireless users. Emerging services indicate

that a future digital home will need a peak bandwidth of 70 Mbps [59]. Also, it is expected

that future ONUs will support 10 Gbps of bandwidth (per wavelength channel). Thus, we

need three ONUs to support the future demands of Wildhorse users at peak hours. We run


Figure 3.4: Map of wireless routers by their signal strengths.

Table 3.2: A small part of scanning results from Wildhorse.

Mode: “Infrastructure”, Antenna: “Omni-directional”Note: (E/B)SSID: (Extended/Basic) Service Set Identifier

WEP: Wired Equivalent Privacy, WPA: WiFi Protected AccessLocation=(Lat(N),Long(W)): (Latitude(North), Longitude(West))

ESSID BSSID Bit-Rate Security Lat(N),Long(W)belkin54g 00:30:BD:FC:B6:5F 54 Mbps WPA 38.564026,-121.7233582WIRE192 00:0D:72:6C:4D:A9 22 Mbps WEP 38.563850,-121.722694Big Momma 00:30:AB:1E:49:A1 11 Mbps None 38.564327,-121.721260iRJMZ 00:11:24:0B:78:EB 11 Mbps WPA 38.564732,-121.7208862WIRE680 00:0D:72:CE:49:F9 11 Mbps WEP 38.564110,-121.721100Go Kings 00:09:5B:C9:3B:E4 36 Mbps WPA 38.563934,-121.720978Home1 00:D0:9E:F9:D1:A9 22 Mbps WEP 38.563744,-121.721298NETGEAR 00:09:5B:4E:67:54 11 Mbps WEP 38.567661,-121.716934Unwired00 00:09:5B:AA:E6:6C 11 Mbps WEP 38.567108,-121.723747NETGEAR 00:09:5B:66:1C:02 54 Mbps None 38.568245,-121.725220We also measured relative signal and noise levels (not reported here to conserve space).


Figure 3.5: Average distance (in meters) of ONUs from their primary users in Wildhorse.

Table 3.3: Wildhorse WOBAN user distributions (in hop count).

Node DistributionHop Counts

1 2 3 4 TotalONU1 46 55 15 2 118ONU2 56 24 5 0 85ONU3 60 24 21 2 107Total 162 103 41 4 310

the experiment to place three ONUs in the Wildhorse WOBAN. As expected, Greedy with

initial deterministic placement performs better as we can see the typical cost for three-ONU

placements for various schemes in Figure 3.5.

Figure 3.6 shows the placement of the three ONUs (black triangles) in Wildhorse

WOBAN (as returned by Greedy). Their locations are (Latitude, Longitude): (38.5650N,

-121.7197W), (38.5677N, -121.7254W), and (38.5690N, -121.7171W).

Given this optimal placement, we find the single-hop and multi-hop distribution of

wireless users (see Table 3.3) with respect to each ONU (assuming a WLAN radio capability

of 100 meters).


Figure 3.6: Placement of 3 ONUs in Wildhorse WOBAN by Greedy (Top left cone: ONU1,Bottom center cone: ONU2, Top right cone: ONU3. Colored dots are residential wirelessusers).

The problem of finding the optimum solution with the cost metric as in Eqn. (1)

does not have any closed-form solution. Although Greedy produces lower costs, it may

be a sub-optimal solution. Thus, in the next section, we reformulate our problem as a

global optimization problem and resort to a popular combinatorial optimization algorithm

— “Simulated Annealing (SA)” — to solve the problem. We show that, although SA can

improve the chances of reaching the global optimum, Greedy performs quite well at a lower

processing requirement, compared to global optimizer.

3.4 Global Optimization of Placements of Multiple ONUs in

WOBAN

Greedy (Algorithm 1) is a heuristic, which performs local optimization of an in-

dividual ONU after the identification of premium users for that ONU. The solution is not

globally optimal. For global optimal, we need a better approach.

Our next approach is to optimize the placement of ONUs globally. We study

a combinatorial-optimization technique, Simulated Annealing. We start with an initial


solution returned by Greedy and then perturb it by slightly displacing one of the high-cost

ONUs (viz., the ONU with overall maximum average distance to the users it serves, i.e., the

premium users) to see if there is an overall cost improvement. After multiple “successful”

iterations, we hope to achieve the “system equilibrium”. Then, no more perturbation is

expected to produce better results. We compare the solution produced by Greedy with

those produced by SA.

3.4.1 Simulated Annealing (SA)

First proposed by Kirkpatrick, Gellat, and Vecchi [48] in 1983, SA is one of the

most-widely-used combinatorial optimization techniques. Researchers from many diverse

fields have successfully been using SA for optimization. SA is a generalization of the “Monte-

Carlo” method and is known as a probabilistic meta-algorithm (because the basic block of

this algorithm is derived from the Monte-Carlo method). The concept of SA comes from

how liquid freezes or metal recrystalizes, known as the annealing process. This algorithm

has five phases: (1) Initialization, (2) Perturbation, (3) Cost calculation, (4) Acceptance,

and (5) Update, as shown in Algorithm 2 [60].

SA Convergence:

- Inner loop criteria: Given T (temperature), number of iterations until new solutions

stops improving.

- Outer loop/ stopping criteria: when T ∼ 0 (ground/frozen state).

3.4.2 Applying SA to Multiple-ONU Placement Problem of WOBAN

– Initialization Phase: Our search space will be the users’ network, where

objects are equivalent to ONUs, and Sini is the initial placement of ONUs, e.g., the one

returned by the Greedy Algorithm (see Algorithm 1). The purpose of this global optimiza-

tion is to find the minimum average distance of all the users (not only the premium users)

with respect to multiple ONUs. So, apart from an individual ONU cost, we also define the


Algorithm 2 Simulated Annealing (for optimal deployment of multiple ONUs in aWOBAN)

- Initialization Phase: T = Initial annealing temperature, B = Boltzmann’s constant,Sini = Initial placement of objects, F = Cost function, Cini = Initial cost, andk ∈ [0, 1] = Rate of cooling.

- Perturbation Phase: Generate moves randomly, i.e., relocate objects from one placeto another randomly. After perturbation, we get Snew = New placement of objects.

- Cost-Calculation Phase: Calculate new cost of placements after perturbation,where Cnew = New cost after perturbation.

- Acceptance Phase:

1. Change of cost, dC = Cnew − Cini.

2. Cost function, F (dC, T ) = min(1, e−

dCB∗T

).

3. If dC is negative, F = 1; ACCEPT new placement, Snew. Update initial cost bynew cost, Cini = Cnew.If dC is positive, F ∈ [0, 1]. Generate a random number, r ∈ [0, 1].If r < F , ACCEPT new placement, Snew, update cost, Cini = Cnew; else RE-JECT.

- Update Phase: Update T by its rate of cooling, T = k∗T . Go back to PerturbationPhase for a new move.


overall network cost as follows:

CiniONUi

= CPrimaryONUi

, (3.12)

Cinioverall =

N∑i=1

CiniONUi

. (3.13)

Equation 3.12 captures the cost of ONUi, which is the cost of premium users

(same as in Eqn. 3.1, CONUi = CPrimaryONUi

) for which the ONU will act as a primary ONU.

Therefore, the overall cost of placing multiple ONUs (Eqn. 3.13) is the sum of the individual

costs of all N ONUs.

– Perturbation Phase: We will relocate the ONUs by a small random input. We employ

a rectangular grid to model the neighborhood’s geography, and move an ONU at most a

single unit only to up, down, left, and right of its coordinates. No diagonal move is allowed.

After perturbation of ONUs, we get Snew = New random placement ONUs.

– Cost-Calculation Phase: We calculate the new cost of ONU placements after pertur-

bation, i.e., CnewONUi

and Cnewoverall.

– Acceptance Phase: 1) If CnewONUi

≤ CiniONUi

, then F = 1, and we ACCEPT the new place-

ment of ONUs, Snew, and update the network costs, CiniONUi

= CnewONUi

and Cinioverall = Cnew

overall.

2) If CnewONUi

> CiniONUi

, then F ∈ [0, 1]. So, we accept the ONU relocation with a certain

probability.

– Update Phase: We update the annealing temperature, T .

3.4.3 Illustrative Numerical Examples: Greedy vs. SA

We study the multiple-ONU placement problem in WOBAN using SA to observe

how the optimum placement could be achieved globally, and how Greedy performs compared

to SA. We have the same range of inputs, varying the number of ONUs (2, 3, 4, 5), area

in sq-meters (1000x1000, 2500x2500, 5000x5000, 7500x7500, 10000x10000), and number of

nodes (100, 250, 500, 750, 1000). Each configuration is repeated 25 times (for a total of

2500 experiments). Simulation parameters for SA have been chosen as shown in Table 3.4

(using our experience after experimenting with various values for these parameters).

In all experiments, SA returns lower WOBAN deployment cost than Greedy (which


Table 3.4: Simulation parameters for SA.

Initial Annealing Temperature, T 10Boltzmann’s Constant, B 0.01Cooling Rate, k 0.95Inner Loop 100 per TGround State Temperature, T 0.005Outer Loop 190Total Iteration 19000 per move (perturbation)

Figure 3.7: Cost improvement (in meters) in WOBAN for individual ONU deployment withSA (in the test network).

is expected since SA’s starting point is Greedy’s solution). The cost reduction (from SA to

Greedy) for individual ONUs typically varies from 10% to as low as 0.05%. The overall cost

improvement from SA over Greedy is typically under 3%. Thus, we infer that, although SA

returns better solution, Greedy produces quite accurate results as well.

We present an illustrative numerical example of how much SA could improve the

deployment cost vs. Greedy in a test network with 100 users (uniformly and randomly

located) over 1000x1000 sq-meters of area. We deploy four ONUs in the network. Figure 3.7

shows that SA indeed improves the cost of individual ONU deployment (e.g., largest cost

saving from SA is for ONU #1, 6.38% improvement, and smallest cost saving is for ONU

#2, 0.05% improvement) as well as the overall cost of deployment (2.70% improvement).


Figure 3.8: Cost improvement (in meters) in Wildhorse WOBAN for individual ONU de-ployment with SA.

Figure 3.9: Relocation of 3 ONUs in Wildhorse WOBAN with SA compared to Greedy(Top left: ONU1, Bottom center: ONU2, Top right: ONU3).


Figure 3.8 captures a similar result for Wildhorse WOBAN, where SA improves the

costs of deployment of three individual ONUs (and, in turn, the overall cost of deployment)

over Greedy. However, the cost improvement of SA over Greedy is marginal.

Figure 3.9 plots the three ONUs placed by SA compared to Greedy in the Wild-

horse WOBAN as described in Section 3.3.1. We observe the solution returned by Greedy

(ONUs indicated by cones) and how the ONUs are relocated by SA (ONUs indicated by

squares) in a practical scenario of 310 residential Wildhorse users (indicated by colored dots).

Locations of ONUs returned by SA are (Latitude, Longitude): (38.5649N, -121.7198W),

(38.5677N, -121.7254W), and (38.5689N, -121.7172W).

Knowing the placement of multiple ONUs by Greedy Algorithm, next we will

compute the expenditure of a WOBAN setup, and compare this with a fully wired access

solution, viz., PON all the way to each user. We argue that WOBAN is a cost-effective

alternative compared to other approaches.

3.5 Cost Comparison of WOBAN and PON Setup in Wild-

horse

Having found the proper placements for multiple ONUs, we compare the expendi-

tures of WOBAN vs. PON solutions in Wildhorse, Davis.

We consider that an OLT is placed in the city’s (Davis) hot-spot, which is located

at the center of the city with an area of nearly 16 square miles. Optical fiber installation is

expensive (USD 100,000 per mile in metropolitan area), and it is reported that about 85%

of this amount is tied to trenching and installing a new duct [61]. The rest (about 15%) of

the expense involves the cost of new fiber and raw materials.

A WOBAN setup involves OLT at the hot-spot, and fiber is laid out from the

OLT to ONUs (which, in turn, drive wireless BSs/routers). ONUs are placed in Wildhorse

according to our Greedy algorithm. In the United States, an estimated 95% of localities are

within 1-1.5 km of fiber-optic infrastructure [61]. So, the Wildhorse locality of Davis is taken

to be within an estimated distance of about 1 km of fiber duct infrastructure. Thus, from

OLT in Davis’ hot-spot to ONUs in Wildhorse, most of the fiber duct is assumed to exist,


and the new duct in about the last half of a mile (toward ONUs) only needs to be trenched.

From ONUs, wireless technology (either WiFi or WiMAX) provides the connectivity to the

end users in Wildhorse (so, no need for fiber at all).

In a PON setup, OLT-to-ONU infrastructure is similar to a WOBAN. However,

unlike WOBAN, fiber is laid out from ONUs to each user’s home (end-to-end fiber solution).

Again, we consider, from OLT to ONUs, most of the fiber duct has already been trenched,

and the new duct only in about the last half of a mile (toward ONUs) needs to be trenched.

Besides, from ONUs, all new fiber duct needs to be trenched for the fiber drop to each

user’s home.

Table 3.5 shows major components involved for WOBAN and PON setup in brief,

and Table 3.6 shows the expenses of various devices and fiber layout, normalized to the cost

of one ONU unit, which is taken to be USD 100 at the time of this article.1

Table 3.5: Various components of WOBAN and PON expenditure.

Solution Major ComponentsWOBAN OLT + ONUs + Wireless routers (WiFi/WiMAX) + OLT-to-ONU/Gateway fiber.PON OLT + ONUs + OLT-to-ONU fiber + ONU-to-user fiber.Fiber cost (100%) = new fiber (15%) + trenching new duct (25%) + labor/installation (60%).

OLT-to-ONU fiber (for both WOBAN and PON): half-a-mile new trenching.ONU-to-user fiber (for PON only): all new trenching.

Table 3.6: Device and fiber layout expenses (in normalized units).

Device Cost (1 ONU unit)ONU 1 [62]OLT 50 [62]Fiber (trenching + material + labor and installation) 1000/mile [61]WiFi BS/Router 60 [63]WiMAX BS/Router 100 [64]Customer Premise Equipment (CPE) 1Note: The expenditure reported here is normalized to the cost of one ONU unit.

Note: At the time of writing this dissertation, one ONU unit costs 100 USD.Number of Wildhorse users = 310

Table 3.7 shows present and future (expected) capacities of ONU, WiFi, and

WiMAX devices, and Table 3.8 captures the expenditure of corresponding WOBAN and1The normalized cost is less sensitive to ups and downs of the absolute cost.


PON solutions. Note that, in a WOBAN solution, we explore the possibilities of both the

WiFi front end and the WiMAX front end. The cost of each solution (for a single Wildhorse

user for one Mbps of bandwidth) reported here is normalized to an estimated present cost

of PON solution.

Table 3.7: ONU, WiFi, and WiMAX capacities.

Device Capacity (Mbps)Present Future

ONU 2500 10000WiFi 54 100 [65]WiMAX 100 1000 [66]

Table 3.8: WOBAN and PON setup expenditures (in normalized units).

Mode Cost (Present PON solution/user/Mbps)Present Future

WOBAN (with WiFi) 0.3304 0.1498WOBAN (with WiMAX) 0.3084 0.0661PON 1.0000 0.2996

Our computation estimates the cost of present PON solutionper Wildhorse user per Mbps of bandwidth to be USD 2.27.

Thus, the present cost of WOBAN with WiFi front endsolution would be USD 0.3304× 2.27 = USD 0.75

for a single user to get one Mbps of bandwidth, and so on.Note: The computations are based on our design criteria,

and the device prices we have chosen.

From Table 3.8, based on our assumptions, we observe that WOBAN is a cost-

effective solution compared to a full PON solution, and it will continue to remain so in the

future. Between WOBAN solutions, as expected, WOBAN with WiMAX setup emerges as

a better choice compared to WOBAN with WiFi.

We also studied the feasibility of an emerging WiMAX access solution from the

CO (an all-wireless access solution, unlike WOBAN and PON), the results of which are not

reported here. We found that, in current scenario, PON is a better solution than WiMAX

access. But, in future, the expenses for WiMAX and PON are found to be similar (not

reported here) [WiMAX (IEEE 802.16m) and PON capacities are expected to reach 1 and

10 Gbps, respectively]. Note that, due to its higher transport capacity, PON will still be

preferred among users with higher bandwidth requirement. Keeping this in mind, we expect


WOBAN solution (cost effective as well as higher capacity) to dominate the last-mile access

in the future.

A WOBAN is a marriage of two powerful techniques. Thus, to capture the chal-

lenges behind a complete WOBAN setup, we propose and investigate a joint optimization

algorithm, which considers design aspects of both the wireless front end, such as avoiding

interference among neighboring BSs, and the optical back end, such as minimizing expensive

fiber layout, simultaneously.

3.6 Joint Optimization of WOBAN: Combined Heuristic (CH)

We have optimized the placements of multiple ONUs so that users’ average dis-

tance from their premium ONU gets minimized. This is essentially focused on the wireless

front-end optimization. The efficient fiber layout in the back end (from OLT to ONUs) has

not been considered. Also, it assumes an ideal situation with no interference among wireless

BSs (or wireless routers). Therefore, this section involves proposing and investigating the

characteristics of a joint optimization algorithm, which considers the design interplay be-

tween both optical and wireless domains together (and the results from Greedy will be used

for our joint optimization algorithm to place ONUs). A proper pre-deployment optimization

strategy can actually save expensive optical and wireless resources needed for a “greenfield”

deployment2 of this type of network. Thus, we propose a Combined Heuristic (CH) model

that focuses on the placements of BSs (on the basis of interference), placements of ONUs

(as returned by Greedy Algorithm), and the minimum-cost fiber layout from OLT/CO to

ONUs in the back end simultaneously (see Algorithm 3).

Note that an additional challenge in deploying WOBAN lies in co-channel in-

terference among neighboring BSs, which are in close proximity. Co-channel interference

arises when two neighboring BSs use the same wireless channel to communicate at the same

time. This will reduce the maximum number of users a BS and its ONU can support. The

co-channel interference will deteriorate the signal quality. If the signal quality is below a

certain threshold (called Carrier-to-Interference threshold or CI threshold), then no trans-2The “greenfield” deployment of a network considers that no prior infrastructure exists.


mission is possible. In order to deal with the co-channel interference, CH carefully plans

the placements of nearby BSs.

The Combined Heuristic (CH) models the BS’s “footprint” (or transmission area)

as a hexagonal cell (similar to the concept of modeling a cellular architecture) [67]. Given

the number of available wireless channels and the CI threshold, CH determines the number

of BSs needed for proper communications without interference.

In CH, based on the channel reuse criteria, a cell and its neighboring cells can not

use the same channel due to interference. The set of neighboring cells that do not use the

same channel is known as a cluster. For example, if a cluster size is 7, each cell and its six

neighboring cells should use different channels, and the same channel can only be reused

beyond these cells. The channel reuse distance is√

3NR, where N is the cluster size and

R is the cell radius (or transmission radius of the BS). Assuming that only surrounding

BSs can interfere with each other, Table 3.9 estimates the number of BSs needed to serve

a number of users (for example 800 wireless users) with 50 available wireless channels that

can be assigned to these users.

Table 3.9: Estimation of channel interference and number of BSs by CH.

N 1 3 4 7 9 12 13 16

Reuse Distance√

3R 3R 2√

3R√

21R 3√

3R 6R√

39R 4√

3R

CI 1.5 13.5 24 73.5 121.5 216 253.5 384CI (dB) 1.8 11.3 13.8 18.7 20.8 23.3 24 25.8Channels/BS 50 50/3 50/4 50/7 50/9 50/12 50/13 50/16BSs 16 48 64 112 144 192 208 256

In Table 3.9, cluster size (N) is computed by N = u2 + uv + v2, where u and v

are non-negative integers. Assuming an BS can interfere only with its six surrounding BSs,

and the wireless signal attenuation factor is 4, then CI can be computed by CI= (√

3N)4

6 .

When all the BSs in the same cluster share the available channels, each BS can get at

most 50/N number of channels. Therefore, we need at least(

800N50

)number of BSs to serve

all 800 users. This is the minimum number of BSs computed on the basis of co-channel

interference threshold. CH deploys these BSs uniformly, determines their transmission

radius, and assigns channels to them so that there is no CI violation. After deploying BSs,

CH determines the number of ONUs needed to support BSs and deploys ONUs according


to our Greedy Algorithm (see Algorithm 1). Finally, CH finds a minimum-cost spanning

tree (MST) to lay fiber from OLT to all the ONUs, and calculates the total WOBAN setup

cost based on the design.

Algorithm 3 Combined Heuristic (CH) (for joint optimization in WOBAN in a “greenfield”deployment)

1. Begin

2. Construct a channel interference table (see Table 3.9).

3. Derive minimum # of BSs, based on CI threshold (see Table 3.9).

4. Deploy these BSs uniformly in the area.

5. Assign channels to BSs to serve users.

6. Determine transmission radius (R) of each deployed BS.

7. If (All users are within BSs’ “footprint”)

8. Go to Step 13.

9. Else

10. Deploy additional BSs.

11. Assign channels to them without violating existing CI constraints.

12. End If

13. Deploy ONUs according to Greedy Algorithm (see Algorithm 1).

14. Construct a Minimum-cost Spanning Tree (MST) from OLT to ONUs to lay fiber.

15. Calculate design cost, based on # of BSs, # of ONUs, and fiber layout.

16. End

3.6.1 Illustrative Numerical Examples: CH

We placed 800 users randomly in an area of 5 × 5 square-miles. We assumed

an OLT to be located at (0, 0). We chose WiMAX as the front end wireless solution for

WOBAN. There are 50 available channels, and each channel operates at 20 MHz. In non-

line-of-sight (NLOS) WiMAX communication, when the channel operates at 20 MHz, the

maximum transmission radius can reach up to 5 miles [68, 69]. The maximum capacity for

each BS and ONU was set as 1 Gbps and 10 Gbps, respectively. Device and fiber layout


costs are chosen according to Table 3.6.

Figure 3.10 shows the overall expenditure of a “greenfield” deployment of the

WOBAN by our joint optimization algorithm. The user coverage ratio captures what frac-

tion of users among the population needs to be served. With low user coverage ratio, it

is intuitive that fewer BSs and ONUs would be needed. In Fig. 3.10, we observe that the

WOBAN deployment cost is less for a lower user coverage ratio. The threshold of CI is set

at 12 dB, and the number of available channels is chosen as 50.

Note that WOBAN deployment cost is normalized to one ONU cost, which is taken

to be USD 100 at the time of this study.

Figure 3.10: WOBAN setup cost (normalized to one ONU unit cost) by Combined Heuristic(CH).

We will report more experimental results on CH in our next chapter and compare

the solution of CH with a constraint programming model.

3.7 Summary

This chapter investigated the problem of optimal placements of multiple ONUs in

a WOBAN. We first studied a simple algorithm (Greedy) for placing the multiple ONUs.

We formulated and analyzed the solution. We conducted a survey of existing wireless users


in the Wildhorse neighborhood of North Davis, and then compared the performance of

various schemes (Greedy vs. random vs. deterministic) and network configurations. We

demonstrated a suitable placement of 3 ONUs in a real neighborhood of wireless users, viz.,

Wildhorse, Davis, with our Greedy algorithm.

We also investigated the problem of multiple-ONU placement using a combinato-

rial optimization algorithm, viz., simulated annealing (SA). We measured the accuracy of

Greedy vs. global optimizer. We found that Greedy performs very well in minimizing the

network cost, but at much lower processing requirements.

After getting the proper locations for ONUs, we compared the expenditures of a

WOBAN vs. a full wired access solution, namely PON. We demonstrated that WOBAN

is a cost-effective broadband access network alternative. To capture the challenges behind

a complete WOBAN setup, we proposed and investigated the characteristics of a joint

optimization algorithm [Combined Heuristic (CH)]. CH expounds on the design aspects of

both the wireless front end, such as avoiding interference among neighboring BSs, and the

optical back end, such as minimizing expensive fiber layout.

57

Chapter 4

Constraint Programming Model

for WOBAN Deployment

4.1 Introduction

Our prior work in Chapter 3 reported on simple, yet efficient approaches, viz.

greedy algorithm, simulated annealing, and combined heuristic for WOBAN deployment.

In these approaches, minimizing average distance (from ONU to users) is the optimization

metric, and other aspects of WOBAN deployment (such as ONU and BS capacities, user as-

signment, channel assignment, etc.) were not considered. (These terms will become clearer

later in this chapter.) Therefore, a sophisticated deployment approach should capture the

design interplay between various aspects of a WOBAN. Additionally, a “good” estimation

model of deployment cost of a WOBAN should be very important to a network designer.

To tackle this challenging problem, in this chapter, we propose and investigate the

characteristics of a constraint programming model (called Primal Model or PM) with the

deployment cost as an optimization metric. We identify six sets of constraints, viz., user

assignment constraints, BS installation constraints, ONU installation constraints, capacity

constraints, channel assignment constraints, and signal-quality and interference constraints.

For analytical tractability of this PM, we relax a few constraints (“Lagrangean Relaxation”)

to transform the problem to the corresponding “Lagrangean Dual” problem. Then, we

Chapter 4: Constraint Programming Model for WOBAN Deployment 58

solve the dual problem to obtain a lower bound on the primal problem (i.e., PM without

relaxation). We also develop an algorithm (called Primal Algorithm) to solve the PM and

obtain its upper bound. By measuring the “duality gap”, which is the difference between

the solution to the primal problem and the solution to its Lagrangean dual problem, we

verify the accuracy of our formulation. Then, we explore how the “Combined Heuristic

(CH)”, a joint deployment algorithm developed in Chapter 3, performs compared to this

optimal analytical model.

The rest of the study is organized as follows. In Section 4.2, we briefly discuss

WOBAN’s design aspects that needs to be considered. In Section 4.3, we present the ana-

lytical formulation (Primal Model) and its solution approach by “Lagrangean Relaxation”

and Primal Algorithm. Section 4.4 contains performance studies of the PM and Section 4.5

summarizes this work.

4.2 Design Criteria for WOBAN

With the advances in wireless technologies, IEEE 802.11a/b/g (WiFi) deploy-

ments, which are very common, can support up to 54 Mbps today; and the emerging IEEE

802.16 (WiMAX) can support much higher data rates (∼ 100 Mbps) over a long distance.

In WOBAN, gateway BSs are associated with ONUs with fiber; so each user can connect to

the back end PON infrastructure via wireless channels. Then, WOBAN can save a major

part of the deployment cost, which is the high cost of laying fiber in the “last mile” from

CO to user. In addition, due to high ONU capacity (e.g., 1 Gbps to 10 Gbps), one ONU can

support multiple BSs; and a BS, in turn, can support multiple users via wireless channels.

WOBAN can also leverage a wireless network’s flexibility; i.e., the “anytime-anywhere”

approach.

In the WOBAN architecture, the deployment cost of the optical part of this net-

work is much higher than its wireless counterpart. Besides the deployment cost, the deploy-

ment time for the optical part of WOBAN is longer than its wireless part. Therefore, to

keep minimum fiber penetration, the wireless part of the network should provide coverage

as far as possible. In other words, one ONU needs to support more wireless BSs. However,


this kind of design strategy must be careful to meet the following criteria.

- How to cope with the increasing user traffic demands:

As the user’s traffic demand grows, we need to deploy additional BSs to serve this

demand. However, due to the capacity constraint of an ONU, the number of BSs that

an ONU can support is limited. Therefore, unless carefully planned, the increasing

traffic demand may not be properly served.

- How to avoid co-channel interference among BSs:

The frequency spectrum in a wireless network is a limited and valuable resource. If

two adjacent BSs (or BSs in close proximity) use the same channel to serve their users,

they will incur co-channel interference. Since the co-channel interference deteriorates

the signal quality, it reduces the maximum number of users a BS and an ONU can sup-

port. If the signal quality is below a certain threshold (called Carrier-to-Interference

threshold or CI threshold), then users’ information (such as data packets) will be

dropped. In order to deal with the co-channel interference, channel assignment for

users communicating with BSs should be carefully planned in a WOBAN design.

Note that there is a tradeoff between WOBAN’s deployment cost and its perfor-

mance. Hence, how to minimize the deployment cost without degrading the performance

is a challenging task. Given the user traffic demands and the signal quality requirements,

several decisions should be made (details of which are elaborated in Section 4.3).

- BS location and its transmission radius:

That is, where to deploy the BSs and how to determine their coverage area (or assign

transmission radius) to satisfy the user traffic demand and avoid interference at the

same time.

- User homing decision:

That is, which BS should serve a user to satisfy user’s bandwidth requirement.

- Carrier-to-Interference ratio for channel assignment:

A BS’s channel assignment should avoid co-channel interference, which is captured by


Carrier-to-Interference ratio (CI ratio). Unlike the conventional approach to assign

different non-overlapping channels to nearby BSs, which may result in poor channel

utilization and throughput, we tackle this issue with a more sophisticated approach

utilizing Carrier-to-Interference (CI) ratio. CI ratio is also linked with signal quality

and bit-error rate (BER). The higher the CI ratio, the lower is the BER and vice

versa (see Section 4.4 for more information).

- ONU location and fiber link deployment:

The deployment of the optical part of the network should minimize the deployment

cost and meet the BS’s traffic demands.

4.3 Mathematical Formulation for Optimal Placement of BSs

and ONUs

This study focuses on the optimal placement of BSs and ONUs in the front end,

and the fiber layout from BSs to ONUs and from ONUs to OLT/CO in the back end of a

WOBAN. In our mathematical formulation of this optimization problem, we consider the

cost of ONUs and BSs, though our performance studies (in Section 4.4) indicate that the

cost of laying fiber in a WOBAN is more significant than the costs of various devices. Also

note that more ONUs can lead to additional OLT installation cost, because an OLT can

generally drive a fixed number of ONUs. Therefore, additional OLTs will increase the cost

of a WOBAN solution considerably as OLTs are expensive.

We propose and investigate a “Primal Model (PM)” formulation, which is a pre-

deployment network-optimization scheme, where the cost of WOBAN design is minimized

(by placing reduced number of BSs and ONUs, and planning an efficient fiber layout).

We also examine the interference among multiple BSs, and explore several installation and

assignment constraints that have to be satisfied for a better-quality access solution and

increased coverage. Our proposed model (PM) for WOBAN placement and its solution

approach by “Lagrangean Relaxation” are shown below.


Given Parameters:

L: set of possible locations for BSs1,

O: set of possible locations for ONUs,

Θi (Bi): installation cost of a BS at location i,

Λk (Uk): installation cost of an ONU at location k,

Φik (Zik): fiber installation cost from BS i to ONU k,

T : set of users,

Kr: traffic demand of user r,

F : set of available wireless channels,

A: upper bound on number of channels assigned to a BS,

Ei: maximum capacity of BS at location i,

ρ: fraction of users served by BSs (user coverage ratio),

Dri: distance between user r and BS at location i,

Ψ (Dri): supported bandwidth (in Mbps) to user r from BS i with distance Dri,

Γ: discrete set of possible transmission radius of BS,

J ′: upper bound of decision variable Jk,

R′: upper bound of decision variable Ri,

I: threshold of Carrier-to-Interference (CI) ratio, and

G: an arbitrarily large number.

1Here ”locations” mean cross-points on a square grid.


Decision Variables:

Bi: 1, if a BS is installed at location i, and 0 otherwise,

Uk: 1, if an ONU is installed at location k, and 0 otherwise,

Zik: 1, if an ONU at location k is connected to a BS at location i, and 0 otherwise,

Xji: 1, if channel j is assigned to BS at location i, and 0 otherwise,

Yri: 1, if user r is assigned to BS at location i, and 0 otherwise,

Jk: capacity of ONU at location k,

Ri: transmission radius of BS at location i, and

Iii′: interference factor of BS at locations i′ on BS at location i (Iii′ =(

Ri′Dii′

)4where Dii′

is the distance between BSs at i and i′).

Objective Function of Primal Model:

CPM = Min

(∑k∈O

Λk (Uk) +∑i∈L

Θi (Bi) +∑i∈L

∑k∈O

Φik (Zik)

)(4.1)

The objective (CPM ) is to minimize the sum of the following items: installation

cost for all ONUs required, plus installation cost for all BSs required, plus cost of connecting

BSs to an ONU by fiber. These three items are the major costs involved in configuring a

WOBAN. As discussed before, for analytical tractability, we relax a few challenging con-

straints (constraints are described later) in Section 4.3.1 to transform the primal model

(CPM ) to the corresponding Lagrangean dual problem (CLR).

Constraints:

Below are the constraints that need to be satisfied in the Primal Model. The goal of the

first set of constraints is to enforce user assignment constraints. Constraint 1 captures the

binary decision variable Yri. Each user is at most associated with only one BS (Constraint


2), and at least ρ (ρ ≤ 1) fraction of total number of users needs to be served by BSs

(Constraint 3).

1. Yri = 0 or 1 ∀r ∈ T, i ∈ L,

2.∑

i∈L Yri = 1 ∀r ∈ T , and

3.∑

r∈T

∑i∈L Yri ≥ ρ|T | ∀r ∈ T, i ∈ L.

The goal of the second set of constraints is to enforce BS installation constraints. Constraint

4 specifies that the decision variable Bi must be binary. A BS must be installed first before

a user can be assigned to it (Constraint 5). In addition, the distance between user r and

BS i must be within the transmission radius of BS i (Constraint 6). The non-negativity

constraints of decision variable Ri are captured by Constraints 7 and 8. Constraint 9 is

used to enforce that the transmission radius of a BS should be a discrete set.

4. Bi = 0 or 1 ∀i ∈ L,

5. Yri ≤ Bi ∀r ∈ T, i ∈ L,

6. DriYri ≤ Ri ∀r ∈ T, i ∈ L,

7. Ri ≤ R′Bi ∀i ∈ L,

8. 0 ≤ Ri ∀i ∈ L, and

9. Ri ∈ Γ ∀i ∈ L.

The goal of the third set of constraints is to enforce channel assignment constraints of BS.

Constraint 10 specifies that the decision variable Xji must be binary. In WiFi or WiMAX

technology, the channel can be CDMA codes or TDMA time slots in wireless frequency

bands. Therefore, Constraint 11 indicates that the number of channels assigned to each BS

is large enough to serve its users. Constraint 12 indicates that BS must be installed first

before channel assignment. Constraint 13 indicates that the number of channels assigned

to a BS should not exceed the upper bound of channels assigned to any BS.

10. Xji = 0 or 1 ∀j ∈ F, i ∈ L,


11.∑

r∈T Yri ≤∑

j∈F Xji ∀i ∈ L,

12. Xji ≤ Bi ∀j ∈ F, i ∈ L, and

13.∑

j∈F Xji ≤ A ∀i ∈ L.

The fourth set of constraints captures the ONU installation constraints. Constraints 14 and

15 specify that the decision variables Zik and Uk, respectively, must be binary. Constraint

16 indicates that an ONU must be installed first before any BS can be connected to it.

Each BS should be connected to only one ONU, which can be captured by an equality∑k∈O Zik = Bi, where ∀i ∈ L. This equality can be broken into two inequalities such as

Constraints 17 and 18.

14. Zik = 0 or 1 ∀i ∈ L, k ∈ O,

15. Uk = 0 or 1 ∀k ∈ O,

16. Zik ≤ Uk ∀i ∈ L, k ∈ O,

17.∑

k∈O Zik ≤ Bi ∀i ∈ L, and

18. Bi ≤∑

k∈O Zik ∀i ∈ L.

The fifth set of constraints enforces capacity constraints of BSs and ONUs. Constraint

19 enforces that the maximum bandwidth of user r from BS i satisfies the user’s traffic

demand. Constraint 20 indicates that a BS should be able to manage its users’ aggregate

traffic demands. Constraint 21 enforces that the capacity of each ONU is large enough to

serve all traffic introduced by its associated BSs. The non-negativity constraints of decision

variable Jk are captured by Constraints 22 and 23.

19. Kr ≤ Ψ(Dri) Yri ∀r ∈ T, i ∈ L,

20.∑

r∈T Ψ(Dri) Yri ≤ Ei ∀i ∈ L,

21.∑

i∈L EiZik ≤ Jk ∀k ∈ O,

22. Jk ≤ J ′Uk ∀k ∈ O, and


23. 0 ≤ Jk ∀k ∈ O.

The goal of the sixth set of constraints is to enforce signal quality constraints for each user.

Since co-channel interference will significantly impact the signal quality, we need to take this

into account when we decide on the channel assignment for each BS. In Constraint 24, the

left-hand side is the total co-channel interference introduced by other BSs using the same

channel j to BS at i. In Constraint 24, when Xji = 0, the right-hand side will be equal to

G (a very large number). This makes Constraint 24 to be always satisfied. However, when

Xji = 1, the right-hand side will be equal to 1I . Hence, we can guarantee the signal quality

to be at least the threshold of acceptable CI ratio. Constraints 25 is the non-negativity

constraint of decision variable Iii′ .

24.∑

i′∈L,i′ 6=i Iii′Xji′ ≤ G +(

1I −G

)Xji ∀i ∈ L, j ∈ F , and

25. 0 ≤ Iii′ ∀(i, i′) ∈ L, i 6= i′.

4.3.1 Lagrangean Relaxation and Lower Bound of Primal Model (PM)

This Primal Model (PM) formulation is very challenging since we need to carefully

plan the locations of BSs and ONUs, the transmission radius of BSs, the channel assignment

of BSs, the assignment of users to BS, and the assignment of BSs to ONU in order to satisfy

the traffic requirement and signal-quality requirement of users at minimum cost.

We apply the Lagrangean Relaxation (LR) method to relax some of the constraints

of our formulation. For analytical tractability, we relax those constraints that make the PM

hard. After the relaxation, we get the Lagrangean dual problem. The solution to this

dual problem is the lower bound to the primal problem. On the other hand, by developing

the Primal Algorithm, we can obtain feasible solutions which give the upper bound to the

primal problem. The Primal Algorithm (see Section 4.3.2) is designed such that it can

obtain the upper bound of the primal problem quickly. By measuring the duality gap, i.e.,

the gap between the upper bound and the lower bound of our model, we can determine how

optimal the solutions are.

Next, we relax the ten Constraints 5, 6, 11, 12, 17, 18, 20, 21, 22, and 24. The

intuition behind relaxing these ten constraints is that they make the Primal Model “hard”


to solve, and after relaxation, the Lagrangean dual model will be analytically tractable.

The more we relax these constraints to make the Primal Model simpler, the larger will be

the duality gap. Then, the obtained solution to the primal problem will be too far from

its optimal solution. On the other hand, if we relax too few constraints, we may not be

able to solve the Lagrangean dual problem optimally. Then, the solution obtained to the

dual problem will not produce the true lower bound of the primal problem. Hence, how to

relax the minimum number of constraints to get the correct and tight duality gap between

the lower bound and the upper bound solutions is very important in Lagrangean relaxation

scheme. Next, we will show how relaxing the above-mentioned ten constraints will provide

an optimal solution to the our Lagrangean dual problem and a tighter duality gap.

So, the Lagrangean dual problem (CLR) becomes:

CLR (µ) = Min∑k∈O

Λk (Uk) +∑i∈L

Θi (Bi) +∑i∈L

∑k∈O

Φik (Zik) ... (4.2)

+∑i∈L

∑r∈T

µ1ir (Yri −Bi) +

∑i∈L

∑r∈T

µ2ir (DriYri −Ri) ...

+∑i∈L

µ3i

∑r∈T

Yri −∑j∈F

Xji

+∑i∈L

∑j∈F

µ4ij (Xji −Bi) ...

+∑i∈L

µ5i

(∑k∈O

Zik −Bi

)+∑i∈L

µ6i

(Bi −

∑k∈O

Zik

)...

+∑i∈L

µ7i

(∑r∈T

Ψ(Dri) Yri − Ei

)+∑k∈O

µ8k

(∑i∈L

EiZik − Jk

)...

+∑k∈O

µ9k

(Jk − J ′Uk

)+∑i∈L

∑j∈F

µ10ij

∑i′∈L,i′ 6=i

Iii′Xji′ −G−(

1I−G

)Xji

,

subject to Constraints 1, 2, 3, 4, 7, 8, 9, 10, 13, 14, 15, 16, 19, 23, and 25. Note that the

µn’s (µn ≥ 0,∀n ∈ [1, 10]) are known as “Lagrangean multipliers”.

We can decompose CLR into five independent subproblems (viz., CS1, CS2, CS3,

CS4, and CS5) and the terms with given parameters Ei and G. Subproblems are the smaller

blocks of problems for a large problem, such as CLR, and each subproblem contains one or


multiple decision variable(s). So, Eqn. (4.2) becomes:

CLR (µ) = CS1 + CS2 + CS3 + CS4 + CS5 −∑i∈L

µ7i Ei −

∑i∈L

∑j∈F

µ10ij G. (4.3)

The five subproblems are as follows.

- Subproblem 1: for Bi and Ri

CS1 = Min∑i∈L

Θi (Bi)−∑i∈L

∑r∈T

µ1irBi −

∑i∈L

∑j∈F

µ4ijBi... (4.4)

−∑i∈L

(µ5

i − µ6i

)Bi −

∑i∈L

∑r∈T

µ2irRi,

subject to Constraints 4, 7, 8, and 9.

We can further decompose Subproblem 1 into |L| independent subsubproblems.

So, for BS at location i ∈ L, Subproblem 1 becomes:

Min Θi (Bi)−

∑r∈T

µ1ir +

∑j∈F

µ4ij + µ5

i − µ6i

Bi −∑r∈T

µ2irRi, (4.5)

subject to Constraints Bi = 0 or 1, Ri ≤ R′Bi, 0 ≤ Ri, and Ri ∈ Γ.

We can observe that, if we assign Bi = 0, then Ri = 0 and the objective value

of the subproblem is zero as well. On the other hand, if we assign Bi = 1, we can as-

sign Ri = R′ and this will lead to the smallest objective value (since the coefficient of

Ri is negative for a non-negative µ2ir). Then, the objective value is equal to Θi (Bi) −(∑

r∈T µ1ir +

∑j∈F µ4

ij + µ5i − µ6

i

)−∑

r∈T µ2irR

′. If this objective value is smaller than

zero, then the optimal solution is to assign Bi = 1 and Ri = R′; otherwise, the optimal

solution is to first assign Bi = 0 and then Ri = 0. The time complexity of solving this

subproblem is on the order of (|T |+ |F |) for each BS i.

- Subproblem 2: for Uk and Zik

CS2 = Min∑k∈O

Λk

(Uk − µ9

kJ′Uk

)+∑i∈L

∑k∈O

(Φik (Zik) +

(µ5

i − µ6i + µ8

kEi

)Zik

), (4.6)


subject to Constraints 14, 15, and 16.

Similarly, we can further decompose Subproblem 2 into |O| independent subsub-

problems. So, for ONU at location k ∈ O, Subproblem 2 becomes:

Min Λk (Uk)− µ9kJ

′Uk +∑i∈L

(Φik (Zik) +

(µ5

i − µ6i + µ8

kEi

)Zik

), (4.7)

subject to Constraints Zik = 0 or 1 ∀i ∈ L, Uk = 0 or 1, and Zik ≤ Uk ∀i ∈ L.

When Uk = 0, all the corresponding decision variables Zik will be zero. Then, the

objective value of the subproblem will be zero as well. If Uk = 1, then some of the Zik’s

will be one. In this case, we will only select those Zik’s that have negative coefficient, i.e.,(µ5

i − µ6i + µ8

kEi

)< 0. Consider, for i ∈ L′, L′ ⊆ L, ω =

∑i∈L′

(µ5

i − µ6i + µ8

kEi

)Zik < 0.

Now, if Λk (Uk) − µ9kJ

′Uk +∑

i∈L′ Φik (Zik) + ω < 0, then we will assign Uk = 1, Zik = 1

∀i ∈ L′, and Zik = 0 ∀i /∈ L′, where L′ ⊆ L. Otherwise, we will get optimal solution at

Uk = 0 and Zik = 0 ∀i ∈ L. The time complexity of solving Subproblem 2 is on the order

of (|L|) for each ONU k.

- Subproblem 3: for Jk

CS3 = Min∑k∈O

(µ9

k − µ8k

)Jk, (4.8)

subject to Constraint 23.

Similarly, we can further decompose Subproblem 3 into |O| independent subsub-

problems. So, for ONU at location k ∈ O, Subproblem 3 becomes:

Min(µ9

k − µ8k

)Jk, (4.9)

subject to Constraint 0 ≤ Jk.

For each ONU k, if the coefficient of Jk is negative, i.e.,(µ9

k − µ8k

)< 0, then we

will assign Jk to its maximum value, i.e., Jk = J ′ to minimize the objective value of the

subproblem, else assign Jk = 0. The time complexity of solving Subproblem 3 is on the

order of (1) for each ONU k.


- Subproblem 4: for Yri

CS4 = Min∑i∈L

∑r∈T

(µ1

ir + µ2irDri + µ3

i + µ7i Ψ(Dri)

)Yri, (4.10)

subject to Constraints 1, 2, 3, and 19.

For each user r ∈ T , since the possible number of BS locations is finite and fixed,

we can exhaustively examine each BS. Then, we identify the set of BSs that can satisfy the

maximum bandwidth requirement.

Among those BSs, we choose the one with the smallest(µ1

ir + µ2irDri + µ3

i + µ7i Ψ(Dri)

)value. Then, we will select ρ|T | number of users to be served by those BSs. The time com-

plexity of solving this subproblem is on the order of (|T ||L|).

- Subproblem 5: for Iii′ and Xji

CS5 = Min∑i∈L

∑j∈F

(µ4

ij − µ3i −

(1I−G

)µ10

ij

)Xji +

∑i∈L

∑j∈F

∑i′∈L,i′ 6=i

µ10ij Iii′Xji′ , (4.11)

subject to Constraints 10, 13, and 25.

After performing variable changing (Xji′ as Xji), we can decompose Subproblem 5

into |L| independent subsubproblems. So, for BS at location i ∈ L, Subproblem 5 becomes:

Min∑j∈F

µ4ij − µ3

i −(

1I−G

)µ10

ij +∑

i∈L,i6=i′

µ10i′jIi′i

Xji, (4.12)

subject to Constraints Xji = 0 or 1 ∀j ∈ F ,∑

j∈F Xji ≤ A, and 0 ≤ Ii′i.

The transmission radius of each BS is a discrete and finite set. So, for each

transmission radius assignment, the interference factor Ii′i can be precomputed. Therefore,

Xji is the only decision variable in Subproblem 5. Since the number of channels assigned

to the BS at location i can not exceed A, we can choose at most A channels. Being a finite

set, we can exhaustively try all possible transmission radius assignments to determine the

minimum cost one. The scheme for solving Subproblem 5 optimally is as follows, with the

time complexity on the order of (|L||Γ|).


I. Initialize set S = Null .

II. Until all transmission radius assignments have been taken into account, for a transmis-

sion radius Ri, calculate Ii′i, and do the following.

III. For channel j, calculate coefficient(µ4

ij − µ3i −

(1I −G

)µ10

ij +∑

i∈L,i6=i′ µ10i′jIi′i

).

IV. For all possible channel assignments in j ∈ F ,

calculate∑

j∈F

(µ4

ij − µ3i −

(1I −G

)µ10

ij +∑


). Find which of these chan-

nel assignments produces the smallest coefficient.

Assume for a particular channel assignment j ∈ ξ, the coefficient will be the minimum,

smin =∑

j∈ξ

(µ4

ij − µ3i −

(1I −G

)µ10

ij +∑


).

V. S = S ∪ {smin}.

VI. Change the transmission radius Ri ← (Ri + ∆(Ri)) ∈ Γ. Go back to Step II.

VII. After getting all the smallest coefficient values for possible Ri’s, find Min (S) and let

the corresponding channels Xji = 1 ∀j ∈ ξ.

Note that the Lagrangean Relaxation technique helps us to successfully solve a

non-convex formulation of Subproblem 5.

According to the weak Lagrangean duality theorem [70, 71] (which says “for any

given set of nonnegative multipliers, the optimal objective function value of the Lagrangean

dual problem is a lower bound on the optimal objective function value of the corresponding

primal problem”), solving CLR (µ) will give the lower bound (LB) of CPM . Based on above

observations for each subproblem, we can solve the Lagrangean dual problem (CLR (µ))

optimally by using the subgradient method to get the tightest lower bound (LB) [72,73] (see

Section 4.3.3 for details).

4.3.2 Primal Algorithm and Upper Bound of Primal Model

Though the solution to the Lagrangean dual problem (CLR (µ)) is not an exact

solution (since some of the constraints of the PM have been relaxed), it can serve as a


good starting point to get a feasible solution. The basic idea of the Primal Algorithm is to

install the BSs that can serve more users under the capacity constraints and interference

constraints until at least a fraction of the total number of users is covered (Constraint 3).

Then, we will deploy the minimum number of ONUs to satisfy the traffic demands from

BSs. Figure 4.1 shows the schematic of the Primal Algorithm, which will give the upper

bound (UB) of CPM .

We identify the sequence of channels to be assigned to users in order not to violate

the co-channel interference constraints in Step 2 of the algorithm. This is because users

at close proximity should be assigned non-interfering channels simultaneously to reduce

cross-talk; the same channel can only be reassigned to users far apart from each other

(channel reuse). The non-negative Lagrangean multiplier µ10ij has a physical significance of

co-channel interference violation cost. Therefore, we can determine the sequence of channel

assignment for BS i by sorting µ7ij in ascending order.

In Step 4, we examine the capacity constraint of BS (i.e., Constraint 20). In Step 5,

we examine the co-channel interference constraint (i.e., Constraint 24). If these constrains

are satisfied, then we assign one channel to the user; otherwise, we continue to examine

the other unvisited (unassociated) users. In Step 8, if Constraint 3 is not satisfied, we add

a new BS to cover unvisited users. After Constraint 3 is satisfied, we can assign ONUs

to cover all the traffic demands from installed BSs (Constraint 21). After the ONUs are

identified, we construct a minimum-cost spanning tree (MST) to layout fibers from OLT to

reach all ONUs and BSs.

4.3.3 Computing Upper Bound (UB) and Lower Bound (LB) of Primal

Model

Next, we show how to compute the UB (solution produced by Primal Algo-

rithm, see Section 4.3.2) and the LB (solution produced by Lagrangean relaxation, see

Section 4.3.1) of our PM formulation. We use the subgradient method as given below, where

quiescence age is incremented if CLR (µ) does not improve. When quiescence age becomes

quiescence threshold, step size coefficient (or δ) becomes halved for the next iteration. The


Figure 4.1: Primal Algorithm schematic (“T” means True, “F” means False).


complexity of this method for each iteration is on the order of (|L|(|L||Γ|+ |F |log|F |+ |T |)).

4.4 Performance Study

We conducted several computational experiments to test the solution quality and

effectiveness of our approach. In this study, we set max iteration and quiescence threshold

as 1000 and 30, respectively (using our experience after experimenting with various values

for these parameters). We initialized step size coefficient (or δ) as 2.

We placed 800 users randomly in an area of 5× 5 square-miles. We assumed OLT

to be located at (0, 0). We chose WiMAX as the front-end wireless solution for WOBAN.

There are 50 available channels, and each channel operates at 20 MHz. In non-line-of-

sight (NLOS) WiMAX communication, when the channel operates at 20 MHz, we can get

a maximum data rate of 75 Mbps with a maximum transmission radius of 5 miles [68].

WiMAX supports adaptive modulation schemes to adjust its data rates as needed inside

a BS’s coverage area. More sophisticated modulation scheme (e.g., 64 QAM) are used in

inner-most zone of the coverage area to provide better signal quality, which, in turn, leads to

higher throughput and lower BER. On the other hand, moderate modulation schemes (e.g.,

QPSK, BPSK, etc.) are adopted in the outer zones of a BS’s coverage area [69]. Table 4.1

shows typical values of Carrier-to-Interference ratios (CI) in order to ensure a BER of 10−6

for different WiMAX modulation schemes [74].

Table 4.1: WiMAX modulations vs. CI.

Modulations QPSK 16QAM 32QAM 64QAMCI (dB) 16 20 23 27

Hence, WiMAX is expected to support much higher data rates in the inner-most

zone of a BS’s coverage area, compared to its outer zones. In other words, when the

distance between a BS and a user is larger, the WiMAX data rate will be reduced. From

above observations, we set 2, 5, 10, 20, 30, 40, 75 Mbps for transmission radius of 5, 4, 3,

2, 1.5, 1, 0.5 miles, respectively [68, 69]. Traffic demand for each user is chosen between

1 Mbps and 75 Mbps. The maximum capacity for each BS i and ONU k was set as 1

Gbps and 10 Gbps, respectively (these are the futuristic inputs to our numerical study and


Algorithm 4 Subgradient Method to compute UB and LB

1. Begin

2. Initialize Lagrangean multipliers, µn(0) = 0,∀n ∈ [1, 10].

3. UB =∞ and LB = −∞.

4. quiescence age = 0, δ0 = 2, and ε ∼ 0.

5. Initialize quiescence threshold.

6. For (m = 0; m ≤ max iteration; m = m + 1)

7. Solve Subproblem 1, Subproblem 2, Subproblem 3, Subproblem 4, and Subprob-lem 5.

8. Compute CmLR (µ) as in Eqn. (3).

9. If CmLR (µ) > LB

10. LB = CmLR (µ) and quiescence age = 0.

11. δm+1 = δm.

12. Else quiescence age = quiescence age + 1.

13. End If

14. If quiescence age = quiescence threshold

15. δm+1 = δm2 and quiescence age = 0.

16. End If

17. Run Primal Algorithm. Compute upper bound ub.

18. If ub < UB, UB = ub

19. End If.

20. γm = g(CmLR (µ)). /*piecewise gradient computation*/

21. tm = δm∗(UB−LB)||γm||2 . /*step size update*/

22. µn(m + 1) = µn(m) + tmγm. /*Lagrange multiplier update*/

23. If ||µn(m + 1)− µn(m)||1 ≤ ε /*stopping criteria*/

24. Stop.

25. End If

26. End For

27. /* Comments: ||.||1: Norm of 1 and ||.||2: Norm of 2. */

28. End


research on achieving higher BS and ONU capacities is very active) [66].

Table 4.2 shows the expenses of various devices and fiber layout, normalized to

the cost of one ONU unit, which is taken to be USD 100 at the time of this article.2

Table 4.2: Device and fiber layout expenses (in normalized units).

Device Cost (1 ONU unit)ONU 1 [62]WiMAX BS 100 [64]Fiber 1000/mile [61,75]OLT 50 [62]CPE 1

Note: The expenditure reported here is normalized to the cost of one ONU unit.Note: At the time of writing this dissertation, one ONU unit cost is taken as 100 USD.

From Table 4.2, we infer that Θi (Bi) for each BS i and Λk (Uk) for each ONU k are

set to 10000 USD and 100 USD (US Dollar), respectively [62, 64]. Φik (Zik) for connecting

BS i and ONU k is determined by the cost of laying fiber, which is chosen to be 100000

USD/mile [61,75]. [For the sake of completeness of this study, we also show the normalized

cost of an OLT and a WiMAX Customer Premise Equipment (CPE). Note that the CPE

cost will be borne by customers, not the WOBAN designers.]

We assume that all ONUs connect to one OLT, located at (0, 0). We connect the

OLT and ONUs/BSs through a minimum-cost spanning tree with OLT as the root.

In Sections 4.4.1 and 4.4.2, we set the user coverage ratio, ρ = 1, and observe

how a WOBAN’s deployment cost varies due to CI threshold (I) and available wireless

channels (F ), respectively. In Section 4.4.3, we study the impact of user coverage ratio ρ on

deployment cost of WOBAN. In Section 4.4.4, we examine how the WOBAN’s deployment

cost varies in a non-homogeneous demography, where a majority of users is clustered in

a small area, and the remaining users are far away. By observing the duality gap (see

Figs. 4.2, 4.3, 4.4, and 4.5) of Primal Model (PM), we can infer a WOBAN’s optimum

deployment cost; this is because the optimum cost is upper and lower bounded by UB and

LB, respectively. Also, we compare the cost returned by the PM to the joint optimization

heuristic captured by CH (discussed in Chapter 3). The complexity of CH is on the order2The normalized cost is less sensitive to ups and downs of the absolute cost.


of (|L|(|L||Γ|+ |F |+ |T |)).

4.4.1 PM vs. CH: Impact of Carrier-to-Interference (CI) Threshold, I

Intuitively, the higher the distance between a BS and a user, the lower will be

the signal quality and higher will be the noise/interference. So, to serve all users with

satisfactory channel quality, we need sufficient numbers of BSs and ONUs. In other words,

we need to deploy enough BSs (and ONUs) to accommodate all the users if CI threshold

is higher. In Table 4.3, we show the number of BSs and ONUs needed for different CI

thresholds. Note that, when CI threshold is set to be 18 dB or higher, Combined Heuristic

(CH) could not find feasible solutions.

Table 4.3: Number of BSs and ONUs.

CI theshold (dB) 0 3 6 9 12 15 18 20Primal Model (PM).

BSs 21 26 32 39 48 58 70 86ONUs 3 3 4 4 5 6 7 9

Combined Heuristic (CH).BSs 23 30 36 49 71 99 NA NAONUs 3 3 4 5 8 10 NA NA

NA stands for Not Applicable.

In Fig. 4.2, we examine the solution quality of our formulation with respect to

different CI thresholds (i.e., I in Section 4.3). UB is the upper bound solution through

the Primal Algorithm and LB is the lower bound solution with CLR (µ). There are three

important observations. First, the cost increase at higher CI threshold is not very significant

(though we need significantly more number of BSs and ONUs at higher CI threshold as

in Table 4.3). This is because the fiber layout cost (100000 USD/mile) dominates the

equipment costs of BSs and ONUs. Also, ONUs and BSs will be diversely placed as users

are randomly distributed. Therefore, for smaller number of BSs and ONUs, the total fiber

length in the minimum-cost spanning tree is comparable to the larger number of deployed

BSs and ONUs. This is the reason why the deployment costs do not shoot up while deploying

large numbers of ONUs and BSs.

Second, the UB increases with higher CI threshold, but the LB does not increase


Figure 4.2: Impact of channel interference on normalized deployment cost (with ρ = 1 and|F | = 50 channels). If I ≥ 18 dB, no feasible solution exists for CH.

significantly. This is because the co-channel interference constraint (Constraint 24) was

relaxed in formulating the dual problem. Hence, the LB is not very sensitive to CI threshold.

Third, observe that the cost estimation by PM always outperforms the Combined

Heuristic (CH), especially at higher CI threshold. This is because, in the first phase, CH

estimates the minimum number of BSs required, distributes them homogeneously in the

area, and tries to cover as many users as possible by tuning the transmission radius. But

apart from these BSs, we may need additional BSs to take care of other constraints such

as traffic demand. At higher co-channel interference constraint, the transmission radius of

the additional BSs need to be small in order to satisfy existing CI constraints. This results

in the deployment of a larger number of additional BSs. Note that CH does not produce a

feasible solution for CI threshold of 18 dB or higher. This is due to the fact that a stringent

CI threshold makes the deployment of additional BSs (which are needed to cover all the

users and their traffic demands) difficult without violating the CI constraints of existing

BSs, leading to an infeasible solution.

Note that WOBAN deployment cost is normalized to one ONU cost, which is taken

to be USD 100 at the time of this study.


4.4.2 PM vs. CH: Impact of Wireless Channel Pool, F

In some of the WiMAX standards (such as IEEE 802.16-2004), the frequency

spectrum is not free. Therefore, we need to utilize channels efficiently to satisfy the traffic

demands. In Fig. 4.3, we study the impact of total number of available channels on the

solution quality where the CI threshold is set at 12 dB.

Figure 4.3: Impact of available channel pool on normalized deployment cost (with ρ = 1and I = 12 dB). If |F | < 35 channels, no feasible solution exists for CH.

We observe that, when the channel resource is scarce, we need to deploy more

BSs and ONUs to cover all the users. This leads to higher deployment cost compared to

when the channel resource is rich. For example, when there are 20 channels, WOBAN’s

deployment cost is upper bounded by 75000 ONU unit cost; but when there are 50 channels,

deployment cost is reduced to around 45000 ONU unit cost for UB. Therefore, the solution

quality is better when channel resource is rich than when it is scarce.

Also note that the LB is less sensitive to channel resources compared to the UB.

This is because we relax channel assignment constraints (Constraints 11 and 12), which

indicates that the number of channels assigned to each BS is large enough to serve its users.

The solution quality of the Primal Model is always superior to that of CH for all

types of channel resources (rich or scarce). Furthermore, when channel resource is scarce


(i.e., less than 35 channels), CH can not find a feasible solution. This is due to the fact

that the number of limited channels will incur severe co-channel interference, leading to an

infeasible solution.

4.4.3 PM vs. CH: Impact of User Coverage Ratio, ρ

Another interesting property is the impact of user coverage ratio. With low user

coverage ratio, it is intuitive that fewer BSs and ONUs would be needed to satisfy the

traffic demand. In Fig. 4.4, we observe that the deployment cost is decreasing with respect

to smaller user coverage ratio ρ. The threshold of CI is set at 12 dB, and the number of

available channels is chosen as 50.

Figure 4.4: Impact of user coverage ratio on normalized deployment cost (with I = 12 dBand |F | = 50 channels).

Also note that the solution quality is better for smaller ρ. For example, when

ρ = 0.5, the duality gap is 26.17% compared to 35.35% at ρ = 1.0. Again, the solution

quality of the PM outperforms CH for all coverage ratios.


4.4.4 PM vs. CH: Impact of Non-Homogeneous Demography

In a practical situation, the user population density may not be evenly distributed.

More often than not, it is expected to be significantly non-homogeneous and clustered, where

a majority of users resides in a small area. Hence, we study what fraction of the WOBAN

deployment cost is needed to serve the extreme users (who are far away and/or isolated).

For this study, we partition the test network (area of 5× 5 square-miles) into 100

equal grids, where each grid is of 0.5× 0.5 square-miles area. We select 20 grids randomly

(called hot-spots) and distribute 80% of the users in these hot-spots. The remaining 20% of

the users are scattered over other parts of the network. Therefore, 80% of the users reside

in only 20% of the area. Figure 4.5 shows the WOBAN deployment cost with this uneven

user coverage ratio (ρ).

Figure 4.5: Impact of non-homogeneous user coverage ratio on normalized deployment cost(with I = 12 dB and |F | = 50 channels). If ρ > 0.8, no feasible solution exists for CH.

There are three important observations. First, the deployment cost is almost linear

to the user coverage ratio till we serve the nearest 80% of the users (ρ ≤ 0.8). After that,

the cost grows superlinearly. This is expected because the farthest 20% of the users are

scattered over a larger area, leading to higher deployment cost (due to more expense for

longer fiber layout).


Second, as expected, the PM outperforms CH, especially for higher user coverage

ratio. CH can not produce a feasible solution after 80% user-coverage ratio, because users’

population in hot-spots is too dense to be served by the minimum number of BSs (as

calculated from Table 3.9 in Chapter 3). Therefore, we need additional BSs, but it is hard

to deploy them in a small area without violating the CI constraints of existing BSs. Thus,

some users in hot-spots do not get served, leading to an infeasible solution.

Third, the vast majority (80%) of users can be served at a cost of approximately

35000 ONU units (cost at mid-point of the duality gap at ρ = 0.8). The cost for serving

all 100% of users, however, is approximately 75000 ONU units (mid-point of duality gap at

ρ = 1). Thus, we observe that more than 50% of the deployment cost is used to serve the

20% of users who are far away (or outliers). The additional cost is mainly due to expensive

fiber layout.

4.5 Summary

In this chapter, we proposed and investigated the characteristics of an analytical

model (called Primal Model) for optimum placements of Base Stations (BS) and Optical

Network Units (ONU) so that the WOBAN deployment cost is minimized. We devel-

oped several constraints that need to be satisfied for optimality: BS and ONU installation

constraints, their capacity constraints, user assignment constraints, channel assignment

constraints, and channel interference constraints. For analytical tractability of the primal

problem, we used the “Lagrangean Relaxation” technique to relax some of the harder con-

straints, and obtained the corresponding Lagrangean dual problem. We solved this dual

problem to obtain the lower bound of the PM. We also developed a Primal Algorithm and

found an upper bound of the PM. We verified the solution quality with respect to a set of

chosen metrics such as user coverage ratio, number of channels, and channel interference

threshold. Specifically, we measured the “duality gap” between the upper and lower bounds

(UB and LB, respectively) of the PM, and compared the primal solutions to a Combined

Heuristic (CH), discussed in Chapter 3. We found that the PM outperformed CH in all

these metrics, and CH could not find a feasible solution in several challenging scenarios.

82

Chapter 5

WOBAN Connectivity and

Routing

5.1 Introduction

Once a WOBAN is deployed, how to create a mesh topology in the front end and

how to route information (data packets) through it are important problems. In a typical

WOBAN, an end user, e.g., a subscriber with wireless devices at individual homes (scattered

over a geographic area) sends a data packet to one of its neighborhood wireless routers. This

router then injects the packet into the wireless mesh of the WOBAN. The packet travels

through the mesh, possibly over multiple hops, to one of the gateways (and to the ONU)

and is finally sent through the optical part of the WOBAN to the OLT/CO. In the upstream

direction of the wireless front end (from a wireless user to a gateway/ONU), the WOBAN

is an anycast network, i.e., an end user can try to deliver its packet(s) to any one of the

gateways (from which the packet will find its way to the rest of the Internet). In the optical

back end, the upstream part of a WOBAN (from an ONU to a OLT/CO) is a multi-point

media-access network, where ONUs are deployed in a tree network with respect to their OLT

and they contend for a shared upstream resource (or bandwidth). But in the downstream

direction of the wireless front end (from a gateway/ONU to a wireless user), this network

is a unicast network, i.e., a gateway will send a packet to only its specific destination (or

Chapter 5: WOBAN Connectivity and Routing 83

Figure 5.1: A WOBAN’s upstream and downstream protocols.

user). In the optical back end, the downstream (from a OLT/CO to an ONU) of a WOBAN

is a broadcast network, where a packet, destined for a particular ONU, is broadcast to all

ONUs in the tree and processed selectively only by the destination ONU (all other ONUs

discard the packet), as in a standard PON. Figure 5.1 captures a WOBAN’s upstream and

downstream transmit modes.

Note that the wireless links in the front end mesh of a WOBAN may have asym-

metric and differential capacities. This is because of how a router connects to other routers

in its neighborhood; e.g., if a router is associated with two other routers, and the wire-

less channel is time-division multiplexed, then on average, each link (associated with that

router) will get half of the capacity to other routers. Also the effective link capacity from

router A to router B may be different than that from router B to router A, because routers

A and B may have different numbers of neighbors.

This chapter explores the routing properties of WOBAN. Although it compares

several performance metrics among routing algorithms, the study primarily focuses on

packet delay (latency) in the front end (wireless mesh) of the WOBAN, i.e., the packet

delay from the router to the gateway (attached to ONU) and vice versa. The packet delay

could be significant as the packet may travel through several routers in the mesh before

finally reaching the gateway (in the upstream direction) or to the user (in the downstream


direction). The larger the mesh of the WOBAN, the higher is the expected delay. Con-

sequently, we propose “Delay-Aware Routing Algorithm (DARA)” as a proactive routing

scheme where we model each wireless router as a queue and predict the wireless link states

(using link-state prediction or LSP) periodically [49]. Based on the LSP information, we

assign weights to the wireless links. Links with higher predicted delays are given higher

weights. Then, we compute the path with the minimum predicted delay from a router to

any gateway and vice versa. While traveling upstream/downstream, a router/gateway will

send its packet along the computed path only if the predicted delay is below a predeter-

mined threshold, referred to as the delay requirement for the mesh; otherwise, we will not

admit the packet into the mesh. We also study how choosing a path from a set of paths

(whose delays are below the delay requirement) can alleviate congestion and achieve better

load balancing.

A common vision of a next-generation converged (fixed and wireless) network is

that of the IP-based end-to-end (between the end nodes) network for connectivity and rout-

ing, which enables devices to access common services over one or more networks seamlessly.

In a WOBAN-like network, end terminal mobility can also be supported at the IP layer

by one of the three dominant approaches, namely Mobile IP, Migrate, and Host Identity

Protocol (HIP) [76], which is beyond the scope of this discussion.

5.1.1 San Francisco WOBAN: A Community Wireless Mesh

We consider a part of the city of San Francisco, California, from approximately

(N 37◦46′43.39′′, W 122◦26′19.22′′ (Golden Gate Avenue and Divisadero Street intersec-

tion)) to (N 37◦46′51.78′′, W 122◦25′13.27′′ (Golden Gate Avenue and Van Ness Avenue

intersection)) and from (N 37◦47′32.57′′, W 122◦26′28.90′′ (Divisadero Street and Pacific

Avenue intersection)) to (N 37◦47′41.39′′, W 122◦25′23.71′′ (Van Ness Avenue and Pacific

Avenue intersection)) (see Fig. 5.2) for our performance study. This is approximately a

one square-mile area in downtown San Francisco with an estimated population of around

15, 000 residents, where San Francisco has an area of nearly 47 square-miles with a popula-

tion of around 745, 000; so the population of SFNet in Fig. 5.2 is quite representative of San

Francisco’s population density. The wireless part of our San Francisco WOBAN (henceforth


Figure 5.2: San Francisco WOBAN and its front-end wireless mesh (SFNet).

called “SFNet”) is a mesh that consists of a number of point-to-point or point-to-multipoint

WiFi routers1. SFNet is envisioned as a part of an on-going effort to deploy the San Fran-

cisco community mesh. In SFNet, we distribute 25 wireless routers in one square-mile area.

Five of these 25 routers are designated as gateways to the optical back end of WOBAN and

placed at the edges of SFNet. We carefully choose the number (as well as distribution) of

routers and gateways in SFNet to match the solution provider’s current deployment status,

where typically 25− 30 routers are needed to serve one square-mile of area.

The rest of this chapter is organized as follows. In Section 5.2, we briefly review

the current routing schemes in these networks and similar research efforts. In Section 5.3,

we propose our delay-aware routing algorithm (DARA). Section 5.4 contains performance

studies of DARA compared to other routing schemes used in the front end wireless mesh of

WOBAN. Section 5.5 concludes this chapter.1In greyscale image (of Fig. 5.2), black squares (five of them) are atached to the optical part of WOBAN

as gateways; others (twenty of them) are routers.


5.2 Current Routing Approaches and Opportunities

Below we review various routing algorithms used in community and federated mesh

networks as well as other research efforts.

5.2.1 Current Routing Approaches

The minimum-hop routing algorithm (MHRA) and the shortest-path routing al-

gorithm (SPRA) are widely used in the wireless mesh of a WOBAN because they are easy

to implement. The link metric in MHRA is assigned as unity, and in SPRA, it is gener-

ally inversely proportional to the current link capacity. MHRA and SPRA work on the

shortest-path principle without generally considering other traffic demands on the network.

Recent approaches also consider solution providers’ patented routing algorithms.

Predictive-throughput routing algorithm (PTRA) is one such protocol, where PTRA is

similar to “Predictive Wireless Routing Protocol (PWRP)” [39]. PTRA measures wireless

links periodically and works on maximizing the throughput of the path on the basis of

link measurement. The major drawback in PTRA is that the packet may end up traveling

inside the mesh longer than expected (as PTRA does not take into account packet delay).

So, PTRA is not suitable for delay-sensitive applications as the corresponding packets can

take longer routes (as long as the route satisfies the throughput criteria). Note that, in

PTRA, the “weakest wireless link” will be the bottleneck for transporting packets (and for

throughput) along the path with multiple hops. Therefore, it chooses the path with the

best of the “weakest wireless links”.

Please see Section 2.4 for more information on MHRA, SPRA, and PTRA.

5.2.2 Other Research Efforts

There exist several research efforts that address routing which can fit in the front

end wireless mesh of a WOBAN. In [77], the authors propose a link-activation framework

(even-odd link assignments) for scheduling packets in a wireless network. They show that the

packet delay for wireline schedulers, viz., Weighted Fair Queuing (WFQ) and Coordinated

Earliest Deadline First (CEDF), when implemented over the wireless multi-hop network,


achieve approximately twice the delay of the corresponding wireline topology. This is be-

cause, in their even-odd framework, two consecutive links (one is even and other is odd)

are active in alternate time slots. The even-odd framework acts as a half-duplex system.

Therefore, a link in the wireless system is only activated half of the time (compared to the

corresponding wireline topology). In [78], the authors provide admission control schemes

for a multi-hop wireless network for packets with QoS requirements (such as bandwidth

and delay). In [79], the authors develop a routing protocol that makes use of Bottleneck

Link Capacity (BLC) as the link metric for wireless networks. In [80], the authors propose

a joint optimal channel assignment scheme for a multi-channel wireless mesh network. The

scheme can provide higher aggregate throughput and better load balancing.

Among other research efforts, in [81], authors proposed a primal-dual model to

determine the constraints for node-channel assignment and interference in a multi-channel,

multi-radio, and multi-hop wireless mesh networks. They also presented link channel as-

signment algorithms with scheduling. In [82], authors formulated a joint channel assignment

and routing problem in infrastructure wireless mesh networks. They also proposed an ap-

proximation algorithm to maximize the allocated bandwidth to traffic aggregation points,

subject to fairness and interference constraints. In [83], authors implemented a routing

protocols in a multi-radio, multi-hop wireless networks to accommodate channel diversity

and path length. In [84], authors proposed a fairness model to enhance the end-to-end

performance in multi-hop wireless backhaul networks. In [85], authors presented central-

ized channel assignment, bandwidth allocation, and routing algorithms for multi-channel,

multi-hop wireless mesh networks that serve as the backbone for relaying end user traffic

to the wired network.

Among other research efforts on integrated wireless-optical network, the authors

in [86] investigate a bandwidth-allocation algorithm for an interactive video-on-demand

(VoD) system over such a hybrid network.

Next, we propose our routing algorithm, DARA.


5.3 Delay-Aware Routing Algorithm (DARA)

DARA is a proactive routing algorithm. DARA considers the end-to-end delay for

packets (here end-to-end means from the packet’s source router to a gateway or vice versa

in the front end of a WOBAN). So, routing in the mesh deals with packets from a router

to a gateway (and vice versa). A user will send its packet to a nearest router. From the

router, the packet will be injected into the mesh. The packet delay in the mesh (namely

the front end of a WOBAN) consists of four components:

- propagation delay,

- transmission delay,

- slot synchronization delay, and

- queuing delay.

Propagation delay will not be significant assuming routers are close to one an-

other. Transmission delay depends on the effective link capacity. Higher the link capacity,

lower is the transmission delay. Slot synchronization delay comes from the time-division-

multiplexing (TDM)-based operation of a wireless channel, where each router will send

packets to its neighboring routers at the pre-assigned time slots. Queuing delay depends

on the rate of packet arrivals and rate of service at a router. Higher the packet arrival rate

and slower the service rate, higher will be the queuing delay. A packet’s delay includes the

queueing delay experienced at the routers traversed to reach the gateway.

Problem Statement

• Given parameters:

- G(V,E) : directed graph denoting front end mesh of WOBAN,

where V : set of vertices and E : set of wireless links,

- R : set of routers in mesh,

- O : set of gateways, where |V | = |R|+ |O|,


- λ′′n : rate of packet arrivals at router n, n ∈ [1, |R|],

- λ′j : rate of packet arrivals at gateway j, j ∈ [1, |O|],

- λi : rate of packet arrivals at link i, i ∈ [1, |E|], and

- Ci : capacity of link i, i ∈ [1, |E|].

• Objective:

- Average-packet-delay-optimized routing in front-end mesh of WOBAN.

For purposes of estimating the queuing delays, we approximately model each router

inside the mesh as an M/M/1 queue. This approximate queuing model is used to estimate

the packet delays on the various links, which in turn drives the routing decisions of DARA.

Packet arriving at the routers (directed to gateways upstream) and at the gateways (des-

tined to routers downstream) are modeled as independent arrivals (with rates λ′′n and λ′j ,

respectively). We note that λi, the average packet intensity on link i, follows same prop-

erties, where λi can be found knowing λ′′n and λ′j . The packet lengths are independent

and exponentially distributed with average length 1µ . The effective link capacity is Ci.

(The independence assumption is being employed as an approximation for mathematical

tractability for route computation as indicated above.)

The capacity is assigned to each link i in a differential and asymmetric manner.

Consider router A has router B and two other routers in its vicinity. Similarly, router B

attaches to router A and three other routers (see Fig. 5.3). Router A’s transmitter will

send packets to three neighboring routers in slots u1, u2, and u3. So, differential capacity

for link i from router A to router B will be on average one-third of the full capacity C (i.e.,

CA→Bi = C

3 ). Similarly, since router B’s transmitter will send packets to four neighboring

routers, including router A, in slots v1, v2, v3, and v4, differential capacity for link i from

router B to router A will be on average one-fourth of the full capacity C (i.e., CB→Ai = C

4 ).

Note that link capacities can be asymmetric (i.e., CA→Bi 6= CB→A

i ), because routers A and

B may have different numbers of neighbors.

Therefore, the transfer delay for any link i is Qi =(

1µCi

+ 12µCi

+ ρi

µCi−λi

), where

1µCi

is the transmission delay (also known as “service time”), 12µCi

is the slot synchronization


Figure 5.3: Differential and asymmetric capacity assignment.

delay, and ρi

µCi−λiis the queuing delay, where ρi is the link utilization, and ρi = λi

µCi.

Algorithm 5 shows our proposed delay-aware routing algorithm (DARA).

In Link-State Advertisement (LSA) (see Algorithm 5), each router/gateway will

periodically advertise its link conditions. Smaller the LSA period, less is the possibility of

“stale” advertisement (where an advertisement becomes “stale” when the link state changes

significantly after the last advertised information). However, LSA in smaller intervals leads

to the problem of sacrificing a significant portion of the network’s bandwith in advertise-

ment, which could have otherwise been used for data packets. Therefore, we can increase

the LSA intervals suitable for WOBAN to preserve the bandwidth for packets and predict

the link conditions (called Link-State Prediction) (see Section 5.3.2) between the intervals

to avoid “stale” information. These Link-State Predictions (LSP) can also capture the

burstiness of data packets in the access network.

In Link-Weight Assignment (LWA) (see Algorithm 5), we assign link weights in

such a manner that the links with more delay get higher weights.

5.3.1 Achieving Load Balancing

DARA works on the principle of delay optimization along paths from a router

to a gateway in the front end of WOBAN. If every packet in the mesh wants the path

with minimum weight (alternatively, the path with minimum delay), then some links in the


Algorithm 5 Delay-Aware Routing Algorithm (DARA)

- Link-State Advertisement (LSA): For each link i, advertise periodically currentpacket intensity (λi), effective link capacity (Ci), and time stamp (tn).

- Link-State Prediction (LSP): For each link i, estimate packet intensity (λesti ) to

be used until next LSA gets advertised (see Section 5.3.2).

- Link-Weight Assignment (LWA): Assign weight of each link i as: Wi = Qi =(1

µCi+ 1

2µCi+ ρi

µCi−λesti

).

- Path Computation:

1. Compute K minimum-weight paths (K > 1),(∑

i∈PkWi

), from the source

router to the gateway or vice versa, where Pk is the k-th path for k ∈ [1,K]. Wecall these paths as K-DARA paths.

2. Derive a set of paths, F (called “feasible paths”), that satisfy the delay require-ment of the packet.

3. Among F , choose one path.

- Admission Control:

1. Admit a new packet in the mesh only if its delay requirement (Treq) satisfies the

minimum delay among the feasible paths F , MinPk∈F

(∑i∈Pk

Wi

)≤ Treq.

2. Else reject the packet.


mesh may attract more packets (overload situation) compared to the other links. This may

adversely affect the operation of the network as many packets might get rejected due to the

link congestions in some parts of the network (had they chosen some other path, they would

have still satisfied their delay requirement). This is why we compute K minimum-weight

paths, instead of only computing the minimum-weight path to leverage greater flexibility

in choosing the paths.

Delays for K-DARA paths are bounded between the minimum delay and the max-

imum delay that satisfies the delay requirement of the packet. Let Tpkt denotes the delay of

a packet whose delay requirement is Treq in the mesh, then Tpkt is shown to be as follows:

Tpkt =

MinPk∈F

∑i∈Pk

Wi

,MaxPk∈F

∑i∈Pk

Wi

≤ Treq (5.1)

where Pk is the k-th path with k ∈ [1,K].

We may achieve better load balancing by choosing paths described above. DARA

will also help us relieve network congestion. Let γ denote the average system arrivals and

Tsys denote the average system delay in the mesh, then Tsys can be defined as below (where

E denotes the connectivity in the mesh):

Tsys =1γ

∑i∈Pk

λiWi +∑

i6∈Pk,i∈E

(λi

µCi+

λi

2µCi+

λiρi

µCi − λi

) (5.2)

=1γ

∑i∈Pk

λiWi +∑

i6∈Pk,i∈Em

(ρi +

ρi

2+

ρ2i

1− ρi

) (5.3)

where Pk is the k-th path with k ∈ [1,K].

5.3.2 Analysis of Link-State Predictions

Link-State Predictions (LSP) need to be quite accurate to capture the current

network conditions and the packet burstiness. We use “weighted moving average (WMA)”

to estimate the packet intensity. Let λi(tn) denote the measured packet intensity that gets

advertised (through LSA) in the mesh at time tn for link i, and let λesti (tn−1) denote the


Figure 5.4: Link-state predictions (LSPs) used at time intervals.

estimated (or predicted) packet intensity for the same link at the previous time instant tn−1

(and used for time interval [tn−1, tn))(see Fig. 5.4). So, LSP will compute the estimated

packet intensity for the next time instant, tn (and to be used for time interval [tn, tn+1)) as

follows:

λesti (tn) =

(3α

3α + 1

)∗(λi(tn) + λest

i (tn−1))∗ S

1−α1+α , (5.4)

where α is the “decaying index” of WMA and S is the number of samples used for pre-

dictions. Decaying index has a physical significance. It captures if a link is highly loaded

or not. If a link is highly loaded, α = ∞. Then Eqn. (5.4) estimates the current intensity

based on all previous samples. On the other hand, if a link is lightly (or moderately) loaded,

we set α = 1, and and the estimations are as follows:

λesti (0) = 0.75λi(0) (5.5)

λesti (1) = 0.75λi(1) + 0.752λi(0) (5.6)

λesti (2) = 0.75λi(2) + 0.752λi(1) + 0.753λi(0) (5.7)

...

Following Eqns. (5.5), (5.6), and (5.7), we observe that only 10% of λi(0) remains present in

packet intensity computations (or 90% of the past samples are “forgotten” after only eight

time periods).


5.3.3 Analysis of Throughput

Till now, we approximately modeled each router to be a M/M/1 queue with

infinite capacity. This assumption is reasonably accurate for light to moderate loads as

storage devices are inexpensive and compact. But for a more realistic throughput analysis,

we now consider each router/gateway to be an M/M/1/B queue with finite queue size B.

Throughput of the WOBAN can be computed by measuring the number of dropped

packets over a certain time period. Packets will start dropping if a router’s queue is filled

up and a new packet arrives. The packet-loss probability for a router R′ is ΦR′B and has a

closed-form equation:

ΦR′B =

1− λ′′nµC

1−(

λ′′nµC

)B+1

(λ′′nµC

)B

(5.8)

and a similar expression for ΦO′B exists for gateway O′ with λ′j as the packet arrival metric.

Now, a packet could traverse g hops from a router to a gateway in the mesh.

Assuming each router as an independent M/M/1/B queue (independence assumption is

valid if we consider that a large number of packets is passing through each router), the

packet-loss probability Lpkt could be computed as follows:

Lpkt = 1−g−1∏n=1

(1− ΦR′

nB

)(1− Φ

O′j

B

)j ∈ [1, |O|]. (5.9)

We have |V | routers and gateways (where |V | = |R|+ |O|) with average packet arrivals as

λ′′n and λ′j , then the total packet arrival in the system per unit time is(∑B

s=1 ΦR′s

)λ′′n|R|+(∑B

s=1 ΦO′s

)λ′j |O| = λ′′n|R|+ λ′j |O|.

The number of dropped packets will be ΦR′B λ′′n|R|+ ΦO′

B λ′j |V | (assuming indepen-

dence of packet loss in each router). So, system throughput ∆(T ) over a time-period T is

as follows:

∆(T ) =(1− ΦR′

B

)λ′′n|R|T +

(1− ΦO′

B

)λ′j |O|T. (5.10)


Figure 5.5: Average delay vs. load in SFNet.


We compare how delay-aware routing algorithm (DARA) performs vis-a-vis MHRA,

SPRA, and PTRA. We took SFNet as our test setting. Packet arrivals are independent.

Packet lengths are independent and exponentially distributed. We modeled a wireless

router’s capacity to be 11 Mbps (i.e., WiFi IEEE 802.11b), and chose the delay thresh-

old as 25 ms. Each of our simulation experiments was run for 100,000 packet arrivals, and

results averaged over all these runs are reported below.

Figure 5.5 shows that DARA outperforms MHRA, SPRA, and PTRA with respect

to average transfer delay. We observe that, at low loads (till the normalized load of 0.40)2,

MHRA and SPRA perform comparably with DARA. This is expected because both MHRA

and SPRA work on the shortest-path principle. So, at low loads, these two algorithms have

higher probability to find the shortest paths with less delay. But as load increases, DARA’s

performance improves significantly compared to MHRA and SPRA. DARA performs much

better than PTRA at all loads. At a very high load of 0.95, the average transfer delay for

DARA improves nearly 30% from its nearest competitor, namely PTRA.2A link load is computed by dividing the link’s packet arrival rate by the service rate. The networkwide

normalized load (referred to as “load” in our discussions here) is computed by averaging over all the linkloads.


Figure 5.6: Delay vs. load [for the furthest router/gateway pair (1, 25)] in SFNet.

Figure 5.6 compares individual path delays among the four schemes. We choose

the furthest origin/gateway pair (1, 25) in the mesh [see Fig. 5.2 for (1, 25) pair in SFNet],

because a packet will travel multiple hops and the delay will be cumulative in each hop.

We find that DARA performs much better than all the other schemes. The performance

improves at high loads. We also observe that, after a load of 0.50, PTRA delay shoots up

and overtakes SPRA delay.

Figure 5.7 shows how many paths one can find between minimum DARA delay

and PTRA delay for the same origin/gateway pair (1, 25). As described in Section 5.3.1,

we find K-DARA paths (K > 1) (a set of paths whose delay is less than the PTRA delay).

At low loads, we get two such paths as PTRA performs quite well. But at high loads (0.50

and beyond), we can find several such paths. In Fig. 5.8, we plot how many such K-DARA

paths on average exist whose delays are less than the PTRA delay.

Figure 5.9 shows the average hop counts of all four schemes. Expectedly, MHRA

and SPRA produce the minimum average number of hops, but DARA performs comparably

with them (particularly at a low load, till 0.40, DARA performs very well). DARA performs

much better than PTRA for all loads. In Fig. 5.10, we plot the percentage distribution of

path lengths for each of these schemes. We observe that DARA performs well, because


Figure 5.7: Comparing K-DARA (K > 1) path delays with PTRA delay [for the furthestrouter/gateway pair (1, 25)] in SFNet.

Figure 5.8: Average number of K-DARA (K > 1) paths under PTRA delays in SFNet.


Figure 5.9: Average hops vs. load in SFNet.

Figure 5.10: Hop distributions vs. load in SFNet.


Figure 5.11: Load balancing (or link congestion) vs. load in SFNet.

unlike PTRA, DARA tries to pack many packets in fewer hops (1− 3 hops). DARA has a

maximum of ten hops in this example, but only 0.025% of packets will get routed along the

10-hop paths.

Figure 5.11 captures how these schemes perform in terms of load balancing and

link congestion. We plot the traffic difference, which is the difference between the maximum

and the minimum packet intensities for links in the mesh for MHRA, SPRA, DARA, and

PTRA paths. Smaller the difference, better will be the load balancing (or less will be the

link congestion) and vice versa. In this performance metric also, DARA performs much

better than MHRA and SPRA. MHRA and SPRA find the shortest paths and thereby

poorly balance the load. Consequently, they congest a part of the mesh. Both DARA

and PTRA perform well and are comparable to each other. Till the load of 0.75, DARA

performs better than PTRA. After that, PTRA performs better.

Figures 5.12 and 5.13 capture the accuracy of our LSP. We plot the LSA values

for packet intensities in wireless links against the predicted values by LSP. We observe that,

both at high and low loads, the predicted values by LSPs are quite accurate. At high

loads, predicted values are on average 0.1% off the range of LSA values. Even at higher

LSA intervals, LSPs perform well (except during the transient phase where the LSP values


Figure 5.12: Actual vs. predicted packet intensities at high loads.

Figure 5.13: Actual vs. predicted packet intensities at low loads.


oscillate before settling down after a certain time period, see subplots 2 and 3 of both

figures). The maximum difference between LSA and LSP values is 15.58%. Similarly, at

low loads, LSP values are on average below 1% off the range of LSA values.

We also observe the increasing bandwidth consumption for LSAs if we decrease

the LSA period in Table 5.1. For LSA periods of 60, 30, 15, and 7.5 seconds, the bandwidth

consumptions due to LSAs in San Francisco WOBAN (SFNet) are 1.54%, 3.07%, 6.15%,

and 12.31%, respectively of total bandwidth. For the mesh in WOBAN, it is very important

to keep the LSA period high enough so that the total bandwidth consumption due to LSA

is low. So, in between the higher LSA periods, LSP will predict link states. Thus, LSP

saves WOBAN bandwidth with accurate predictions of link states.

Table 5.1: LSA’s bandwidth consumption.

Computed for San Francisco WOBAN.LSA period Bandwidth consumption1 min. 1.54%30 secs. 3.07%15 secs. 6.15%7.5 secs. 12.31%

5.5 Summary

This chapter focused on the WOBAN’s front-end wireless mesh connectivity (rout-

ing properties). We reviewed several routing algorithms, which are currently being used to

carry packets in the front end. Then, we proposed and investigated the characteristics of

“Delay-Aware Routing Algorithm (DARA)” that minimizes the average packet delay in the

wireless front end of a WOBAN. Our numerical examples showed that DARA achieves better

load balancing and less congestion compared to tradional approaches such as minimum-hop

routing algorithm (MHRA) and shortest-path routing algorithm (SPRA). In addition to

minimizing delay, DARA also improves on the average hop count compared to the predic-

tive throughput routing algorithm (PTRA), a popular protocol used in several deployments

for the wireless front end of a WOBAN.

102

Chapter 6

WOBAN Fault Tolerance and

Restoration

6.1 Introduction

WOBAN architecture exhibits fault-tolerant behavior and can restore the net-

work against possible failure scenarios. Due to its multi-domain hierarchical architecture,

WOBAN can experience multiple failures. The failures can be of several types:

- Gateway failure: If a wireless gateway fails, the wireless routers need to reassociate

themselves with other “live” gateways.

- ONU failure: If an ONU fails, the connection from its gateways (and their associated

routers down the hierarchy) should be reprovisioned to other neighboring ONUs.

- OLT failure: This failure is more damaging (since an OLT at the top the hierarchy

drives several ONUs/gateways), but less frequent (due to its protection from natural

calamities and human errors because of its location inside the CO). Consequently,

multiple ONUs fail. In this failure, traffic from a large portion of the area needs to

be rerouted.

- Fiber cut: Failure due to fiber cut between upstream (ONU/gateways) and down-

stream (OLT) components. Hence, paths between CO and PON groups will be non-

Chapter 6: WOBAN Fault Tolerance and Restoration 103

functional.

Packet loss may also occur due to any combination of the failure scenarios in WOBAN

architecture, viz., gateway failure, ONU failure, and/or OLT failure.

WOBAN has a self-healing property to combat these failures. We propose “Risk-

and-Delay Aware Routing Algorithm”, called RADAR, to exploit this property. The rest

of the chapter is organized as follows. Section 6.2 describes RADAR. Section 6.3 exam-

ines how efficiently RADAR can exploit WOBAN’s risk awareness and self-healing prop-

erty. In Section 6.4, we report how RADAR minimizes packet loss vs. traditional routing

approaches, viz. Minimum-Hop Routing Algorithm (MHRA), Shortest-Path Routing Al-

gorithm (SPRA), and Predictive Throughput Routing Algorithm (PTRA) as discussed in

Chapter 5. Section 6.5 summarizes this chapter.

6.2 Risk-and-Delay Aware Routing Algorithm (RADAR)

RADAR is a proactive routing scheme and an extension to Delay-Aware Routing

Algorithm (DARA), proposed in Chapter 5. Like DARA, in RADAR, we model each router

inside the mesh as an M/M/1 queue. Each router will advertise the wireless link states (via

link-state advertisement or LSA) periodically. Based on the LSA information, we assign

link weights to the wireless links. Links with higher delay are assigned higher weights.

Then, we compute the path with the minimum average transfer delay from a router to any

gateway and vice versa, and maintain a Risk List (RL) table in each router. If a failure

occurs, RL will be updated accordingly and the subsequent packets will be rerouted.

A packet’s average transfer delay, Qi, along a wireless link i depends on its trans-

mission delay, slot-synchronization delay (TDM-based operation of a wireless channel), and

queuing delay. We ignore propagation delay because routers are close to one another.

Let λ denote the average packet intensity on link i, which is approximated by a Poisson

distribution. Consider that packet lengths are independent and exponentially distributed

with average lengths as 1µ , and the effective link capacity is Ci. Then, Qi is shown to be

Qi =(

1µCi

+ 12µCi

+ ρi

µCi−λi

), where 1

µCiis the transmission delay (also known as “service

time”), 12µCi

is the slot-synchronization delay, and ρi

µCi−λiis the queuing delay, where ρi is


the link utilization, and ρi = λiµCi

. (Please refer to Chapter 5 for details.)

Algorithm 6 shows our proposed risk-and-delay aware routing algorithm.

Algorithm 6 Risk-and-Delay Aware Routing Algorithm (RADAR)

- Link-State Advertisement (LSA): For each link i, advertise periodically currentpacket intensity (λi), effective link capacity (Ci), and time stamp (tn).

- Link-State Prediction (LSP): For each link i, estimate packet intensity (λesti ) to

be used until next LSA gets advertised (see Section 5.3.2 in Chapter 5 for details).

- Link-Weight Assignment (LWA): Assign weight of each link i as: Wi = Qi =(1

µCi+ 1

2µCi+ ρi

µCi−λesti

).

- Path Computation:

1. Compute K minimum-weight paths (K > 1),(∑

i∈PkWi

), from the source

router to the gateway or vice versa, where Pk is the k-th path for k ∈ [1,K].

2. Derive a set of paths, F (called “feasible paths”), that satisfy the delay require-ment of the packet.

3. Among F , choose one path.

- Risk List Update: Maintain a Risk List (RL) table in each router based on F.Update RL at next LSA.

- Path Selection: Among paths in RL, choose a “live” path. (See Section 6.3 fordetails.)

6.3 Analysis of RADAR

We explain how WOBAN exhibits risk awareness and self-healing properties. To

handle failures, RADAR exploits these properties through its periodic “Risk List Update”

and a suitable “Path Selection” mechanism.

6.3.1 Risk Awareness

To reduce packet loss, each router maintains a “Risk List (RL)” to keep track of

failures. An RL in each router contains six fields, viz. path number (PN), Primary Gateway

Group (PGG), Secondary Gateway Group (SGG), Tertiary Gateway Group (TGG), path

status (PS) (“live” or “stale”), and corresponding path delay (PD). The primary gateway


Figure 6.1: An illustration of RADAR.

for a router is the gateway with the minimum delay path. PGG contains paths with

the primary gateway and the gateways connected to the same ONU as with the primary

gateway. SGG contains paths with gateways that are connected to different ONUs but the

same OLT as with the PGG. TGG contains paths with gateways that are connected to a

different OLT (and consequently a different ONU). Figure 6.1 illustrates the risk awareness

and self-healing mechanisms of RADAR.

There are a number of gateways at the edges of the front end of a WOBAN.

Furthermore, at the optical back end, there are different OLTs, and each OLT supports

multiple ONUs. Each gateway will be assigned a gateway id, viz. CBvAwu , where CBvAw

u

stands for the u-th gateway (denoted by C), associated with the v-th ONU (denoted by B),

which, in turn, is connected to the w-th OLT (denoted by A) at the back end. For example,

gateway id CB16A21 will be assigned to the 1st gateway associated with the 16th PON group

(or ONU) of the 2nd OLT (see Fig. 6.1).

A router may find multiple paths for a packet satisfying its delay requirement in

the mesh. Now, if a router finds five minimum-weight paths, then F = 5. Consider that the


Table 6.1: Risk List (RL) in a router.

PN PGG SGG TGG PS PD1 CB1A1

1 Live A2 CB3A1

3 Live B3 CB16A1

1 Live C4 CB1A1

3 Live D5 CB5A2

2 Live E

Table 6.2: Updated Risk List for Gatewayfailure.


1 Stale A2 CB3A1

3 Live B3 CB16A1

1 Live C4 CB1A1

3 Live D5 CB5A2

2 Live E

Table 6.3: Updated Risk List for ONU failure.


1 Stale A2 CB3A1

3 Live B3 CB16A1

1 Live C4 CB1A1

3 Stale D5 CB5A2

2 Live E

Table 6.4: Updated Risk List for OLT failure.


1 Stale A2 CB3A1

3 Stale B3 CB16A1

1 Stale C4 CB1A1

3 Stale D5 CB5A2

2 Live E

minimum-weight path (or alternatively the minimum-delay path) chooses gateway CB1A11 .

Therefore, PGG in the RL in that router will contain the path with gateway CB1A11 (primary

gateway) and any other paths with gateways CB1A1u (means gateways that are connected

to ONU B1 of OLT A1). SGG will contain paths with gateways CBvA1u , v 6= 1 (means

gateways that are connected to ONUs of OLT A1, except ONU B1). TGG will contain

paths with gateways CBvAwu , w 6= 1 (means gateways that are connected to ONUs of any

OLT, except OLT A1). Now, if CB1A11 , CB3A1

3 , CB16A11 , CB1A1

3 , and CB5A22 are the gateways

of five minimum-weight paths (in ascending order of delay) for that router, then its RL will

be as shown in Table 6.1 (with A < B < C < D < E).

Table 6.2 shows how the RL will be updated if a gateway failure occurs. If primary

gateway CB1A11 fails, then all the paths with that gateway will be “stale”, and packets,

destined for CB1A11 , will be rerouted through “live” PGG, SGG, or TGG paths. Table 6.3

considers an ONU failure. If ONU B1 fails, all PGG paths will be “stale”; packets will be

rerouted through SGG and TGG paths. Table 6.4 considers an OLT failure. If OLT A1 fails,

all PGG and SGG paths will be “stale”; but the packets could still be rerouted through

“live” TGG path with CB5A22 . Therefore, RADAR can provide protection for multiple

front-end and back-end hierarchical failure scenarios.


6.3.2 Self Healing

If all links adjacent to gateway ids CB1A11 and CB1A1

3 go down, and that of CB3A13

and CB16A11 are “live”, then routers can infer that either both gateways CB1A1

1 and CB1A13

have failed simultaneously or ONU B1 has failed. Then, packets will be rerouted through

SGG and TGG paths. If all links adjacent to gateway ids CBvA1u go down, then routers can

infer that either all these gateways CBvA1u have failed simultaneously or their corresponding

ONUs Bv have failed simultaneously, or OLT A1 has failed. Then, TGG paths should take

care of the packets. Therefore, we observe that a router does not always need to recompute

a new set of K minimum-weight paths even if a failure occurs.

A router will recompute paths only if all its previously-computed paths fall under

PGG and the ONUs/OLT fail, or all paths fall under PGG and SGG and the corresponding

OLT fails. After path recomputation, packets will be admitted in the WOBAN with reduced

level of service (alternatively, increased delay). This mechanism is called “self healing”.

6.3.3 Delay Awareness

In LSA (see Algorithm 6), each router/gateway will periodically advertise its link

conditions, and link weights are assigned in such a manner that links with more delay get

higher weights and vice versa. Then, we compute the K minimum-weight paths, which are

the K minimum-delay paths as well. We choose a path among the set of paths. So, RADAR

works on finding a packet’s delay-optimized path in the front-end mesh of a WOBAN. (See

Chapter 5 for details about delay optimization.)


We compare how RADAR performs vs. Minimum-Hop Routing Algorithm (MHRA,

where links’ weights are unity), Shortest-Path Routing Algorithm (SPRA, where weights

are inversely proportional to link capacities), and Predictive-Throughput Routing Algo-

rithm (PTRA, a popular algorithm used in the front end of a WOBAN where a packet

chooses a path with higher estimated throughput). As discussed in Chapter 5, PTRA is

similar to Tropos’s Predictive Wireless Routing Protocol or PWRP. We consider a part of


Figure 6.2: Packet loss for gateway failure.

San Francisco city for our performance study (see Fig. 6.1). As reported with DARA, it is

approximately a one square-mile area in downtown San Francisco with an estimated pop-

ulation of around 15,000 residents. We distributed 25 wireless routers. We designated five

of these 25 routers as gateways to the optical back end of WOBAN and placed them at the

edges. We generated packets in a Poisson distribution. The packet lengths are independent

and exponentially distributed. We assumed a wireless router’s capacity to be 11 Mbps.

We have simulated different failure situations: a single gateway failure (gateway

id CB1A11 in Fig. 6.1), a single ONU failure (ONU B1 in Fig. 6.1, which also drives two

gateways, namely CB1A11 and CB1A1

3 ), and a single OLT failure (OLT A1 in Fig. 6.1, which

also drives three ONUs, namely ONU B1, ONU B3, and ONU B16; and four gateways,

namely CB1A11 , CB1A1

3 , CB3A13 , and CB16A1

1 ). The experiment was conducted with various

network loads (normalized load from 0.05 to 0.75 with a step increase of 0.05) and the

average percentage packet loss over all loads was computed. The total simulation time on

each failure was 5 minutes. Each failure is modeled at the beginning (after 18 seconds) of

the experimental run-time.

Figure 6.2 shows that, for a wireless gateway failure, RADAR (packet loss less

than 1%) performs much better than MHRA, SPRA (packet loss close to 20%), and PTRA


Figure 6.3: Packet loss for ONU failure.

(packet loss close to 5%). For an ONU failure (see Fig. 6.3), packet loss in RADAR is 1-2%,

whereas for MHRA and SPRA, it is close to 35%, and for PTRA, it is around 10%.

For OLT failure (see Fig. 6.4), though packet loss for RADAR is increased, it still

performs better than the other schemes MHRA, SPRA, and PTRA (loss close to 45%).

Also note that PTRA performs worse in OLT failure; this is because, PTRA senses the

wireless channel and finds if any link fails due to either gateway failure or ONU failure

in close geographical proximity. So, PTRA can reroute packets to other gateways in close

proximity. OLTs are often far apart and gateways attached to different OLTs are also spaced

out. So, PTRA often does not have any information regarding these gateways. RADAR,

on the other hand, keeps track of all the gateways in its RL table and performs well.

Besides its risk awareness and self healing, RADAR possesses the delay awareness

and load balancing capabilities of DARA. This is why, on these aspects, RADAR performs

similar to DARA. Chapter 5 reports details of the performance. Therefore, RADAR achieves

its goal of delay optimization as well as packet-loss minimization due to failure.


Figure 6.4: Packet loss for OLT failure.

6.5 Summary

Failure in WOBAN may occur due to the breakdown of router/gateway, ONU,

OLT, or any combination of these. This chapter described how efficiently WOBAN can

combat network failure and restore connectivity.

To exploit WOBAN’s risk awareness and self-healing properties, we developed

”Risk-and-Delay Aware Routing Algorithm (RADAR)”. By maintaining a “Risk List”

table in each router, RADAR can handle failures. Our performance studies show that,

besides inheriting DARA’s delay awareness, RADAR reduces the packet loss for multiple

failure scenarios, viz., gateway failure, ONU failure, and OLT failure.

111

Chapter 7

Conclusion

This dissertation has made five important contributions to the body of knowledge

on the design and modeling of wireless-optical broadband access network (WOBAN). These

are as follows.

7.1 WOBAN Architecture and Research Challenges

In Chapter 2, we introduced an architecture and a vision for WOBAN, and ar-

ticulated why the combination of wireless and optical presents a compelling solution that

optimizes the best of both worlds. While it briefly touched upon the business drivers, the

main arguments focussed on design and deployment considerations.

We discussed network setup, network connectivity, and fault-tolerant character-

istics of the WOBAN. In network setup, we proposed and investigated the design of a

WOBAN where the back end is a wired optical network, the front end is configured by

wireless connectivity, and, in between, the tail ends of the optical part [known as Opti-

cal Network Units (ONUs)] communicate directly with the wireless base stations (known

as “gateway routers”). We reviewed algorithms to optimize the placement of ONUs in a

WOBAN deployment scenario. We also evaluated the pros and cons of the various routing

algorithms (network connectivity) in a WOBAN, including its fault-tolerant behavior and

presented some novel concepts that are better suited for such hybrid networks.

Chapter 7: Conclusion 112

7.2 Network Planning and Setup for WOBAN

Chapter 3 investigated the problem of optimal placement of multiple ONUs in a

WOBAN. We studied a simple algorithm (Greedy) for placing multiple ONUs. We formu-

lated and analyzed the solution. We conducted a survey of existing wireless users in the

Wildhorse neighborhood of North Davis, and then compared the performance of various

schemes (Greedy vs. random vs. deterministic) and network configurations. We demon-

strated a suitable placement of three ONUs in a real neighborhood of wireless users, viz.,

Wildhorse, Davis, with our Greedy algorithm.

We also investigated the problem of multiple-ONU placement using a combinato-

rial optimization algorithm, viz., simulated annealing (SA). We measured the accuracy of

Greedy vs. a global optimizer. We found that Greedy performs very well in minimizing the

network cost, but at much lower processing requirements.

After getting the proper locations for ONUs, we compared the expenditures of

a WOBAN with a fully wired access solution, namely PON. We argued that WOBAN is

a cost-effective broadband access network alternative. To capture the challenges behind

a complete WOBAN setup, we proposed and investigated the characteristics of a joint

optimization algorithm [Combined Heuristic (CH)]. CH expounds on the design aspects of

both the wireless front end, such as avoiding interference among neighboring APs, and the

optical back end, such as minimizing expensive fiber layout.

7.3 Constraint Programming Model for WOBAN Deploy-

ment

In Chapter 4, we proposed and investigated the characteristics of an analytical

model (called Primal Model) for optimum placements of Base Stations (BS) and Optical

Network Units (ONU) so that the WOBAN deployment cost is minimized. We devel-

oped several constraints that need to be satisfied for optimality: BS and ONU installation

constraints, their capacity constraints, user assignment constraints, channel assignment

constraints, and channel interference constraints. For analytical tractability of the primal

Chapter 7: Conclusion 113

problem, we used the “Lagrangean Relaxation” technique to relax some of the harder con-

straints, and obtained the corresponding Lagrangean dual problem. We solved this dual

problem to obtain the lower bound of the PM. We also developed a Primal Algorithm and

found an upper bound of the PM. We verified the solution quality with respect to a set of

chosen metrics such as user coverage ratio, number of channels, and channel interference

threshold. Specifically, we measured the “duality gap” between the upper and lower bounds

(UB and LB, respectively) of the PM, and compared the primal solutions to the Combined

Heuristic (CH), discussed in Chapter 3. We found that the PM outperformed CH in all

these metrics, and CH could not find a feasible solution in several challenging scenarios.

7.4 WOBAN Connectivity and Routing

Chapter 5 focused on the WOBAN’s front-end wireless mesh connectivity (routing

properties). We reviewed several routing algorithms, which are currently being used to carry

packets in the front end. Then, we proposed and investigated the characteristics of “Delay-

Aware Routing Algorithm (DARA)” that minimizes the average packet delay in the wireless

front end of a WOBAN. Our numerical examples showed that DARA achieves better load

balancing and less congestion compared to tradional approaches such as minimum-hop

routing algorithm (MHRA) and shortest-path routing algorithm (SPRA). In addition to

minimizing delay, DARA also improves on the average hop count compared to the predictive

throughput routing algorithm (PTRA), a popular protocol used in several deployments for

the wireless front end of a WOBAN.

7.5 WOBAN Fault Tolerance and Restoration

Failure in WOBAN may occur due to the breakdown of router/gateway, ONU,

OLT, or any combination of these. Chapter 6 described how efficiently WOBAN can combat

network failures and restore connectivity. To exploit the WOBAN’s risk awareness and self-

healing properties, we developed “Risk-and-Delay Aware Routing Algorithm (RADAR)”.

By maintaining a “Risk List” table in each router, RADAR can cope with failures. Our

114

performance studies showed that, besides inheriting DARA’s delay awareness, RADAR can

also reduce the packet loss for multiple failure scenarios, viz., gateway failure, ONU failure,

and OLT failure.

115

Bibliography

[1] G. Kramer, B. Mukherjee, and G. Pesavento, “Ethernet PON (ePON): Design andAnalysis of an Optical Access Network,” Photonic Network Communications, vol. 3,no. 3, pp. 307-319, July 2001.

[2] G. Kramer, Ethernet Passive Optical Networks, McGraw-Hill, 2005.

[3] G. Kramer and G. Pesavento, “Ethernet Passive Optical Network (EPON): Building aNext-Generation Optical Access Network,” IEEE Communications Magazine, vol. 40,no. 2, pp. 66-73, February 2002.

[4] M. Wegleitner, “Maximizing the Impact of Optical Technology,” Plenary Talk,IEEE/OSA Optical Fiber Communication Conference (OFC), Anaheim, California,March 2007.

[5] A. Banerjee et al., “Wavelength-Division Multiplexed Passive Optical Network (WDM-PON) Technologies for Broadband Access – A Review [Invited],” OSA Journal ofOptical Networking, Special Issue on Optical Access Networks, vol. 4, no. 11, pp. 737-758, November 2005.

[6] B. Mukherjee, Optical WDM Networks, Springer, 2006.

[7] G. Maier, M. Martinelli, A. Pattavina, and E. Salvadori, “Design and Cost Performanceof the Multistage WDM-PON Access Networks,” IEEE/OSA Journal of LightwaveTechnology, vol. 18, no. 2, pp. 125-143, February 2000.

[8] Verizon, http://www22.verizon.com.

[9] Novera Optics, http://www.noveraoptics.com/.

[10] J.-J. Yoo et al., “ A WDM-Ethernet hybrid passive optical network architecture,”Proc., International Conference on Advanced Communication Technology (ICACT),Korea, February 2006.

[11] IEEE 802.11 Working Group for WLAN Standards,http://grouper.ieee.org/groups/802/11/.

[12] IEEE 802.16 Working Group on WMAN Standards, http://www.ieee802.org/16/.

[13] Sprint, http://www2.sprint.com/mr/news dtl.do?id=12960.

[14] Towerstream, http://www.towerstream.com/content.asp?home.

[15] Intel, http://www.intel.com/netcomms/technologies/wimax/index.htm/.

Bibliography 116

[16] Federal Communications Commission, 3G Wireless Systems, http://www.fcc.gov/3G/.

[17] Alcatel-Lucent, 4G Telecom Review, http://www.alcatel-lucent.com/.

[18] H. Luo, et al., “UCAN: A Unified Cellular and Ad hoc Network Architecture,” Proc.,ACM MOBICOM, San Diego, CA, September 2001.

[19] H. Wu, C. Qiao, S. De, and O. Tonguz, “Integrated Cellular and Ad Hoc RelayingSystems: iCAR,” IEEE Journal on Selected Areas in Communications, vol. 19, no. 10,pp. 2105-2115, 2001.

[20] S. De, O. Tonguz, H. Wu, and C. Qiao, “Integrated Cellular and Ad Hoc Relay (iCAR)Systems: Pushing the Performance Limits of Conventional Wireless Networks,” Proc.,Hawaii International Conference on System Sciences, Hawaii, 2002.

[21] C. Qiao, H. Wu, and O. Tonguz, “Load balancing via relay in next generation wirelesssystems,” Proc., ACM International Symposium on Mobile Ad hoc Networking andComputing, Boston, MA, March 2000.

[22] G. K. Chang, J. Yu, Z. Jia, and J. Yu, “Novel optical-wireless access network archi-tecture for simultaneously providing broadband wireless and wired services,” Proc.,IEEE/OSA Optical Fiber Communications (OFC), Anaheim, California, March 2006.

[23] T. Nakasyotani, H. Toda, T. Kuri, and K. Kitayama, “Wavelength-Division-Multiplexed Millimeter-Waveband Radio-on-Fiber System Using a SupercontinuumLight Source,” IEEE/OSA Journal of Lightwave Technology, vol. 24, no. 1, pp. 404-410,January 2006.

[24] J. Yu, G. K. Chang, Z. Jia, L. Yi, Y. Su, and T. Wang, “A ROF downstream linkwith optical mm-wave generation using optical phase modulator for providing broad-band optical-wireless access service,” Proc., IEEE/OSA Optical Fiber Communications(OFC), Anaheim, California, March 2006.

[25] W.-P. Lin, “A Robust Fiber-Radio Architecture for Wavelength-Division-MultiplexingRing-Access Networks,” IEEE/OSA Journal of Lightwave Technology, vol. 23, no. 9,pp. 2610-2620, September 2005.

[26] H. Pfrommer et al., “Full-Duplex DOCSIS/WirelessDOCSIS Fiber-Radio Network Em-ploying Packaged AFPM-Based Base-Stations,” IEEE Photonics Technology Letters,vol. 18, no. 2, pp. 406-408, January 2006.

[27] S. Ray, M. Medard, and L. Zheng, “A SIMO Fiber Aided Wireless Network Architec-ture,” Proc., International Symposium on Information Theory, Seattle, Washington,July 2006.

[28] C. P. Liu, T. Ismail, and A. J. Seeds, “ Broadband access using wireless-over-fibretechnologies,” Springer BT Technology Journal, vol. 24, no. 3, pp. 130-143, July 2006.

[29] IEEE/OSA Journal of Lightwave Technology, Special Issue on Convergence of OpticalWireless Access Networks, (Nov. 2007)

Bibliography 117

[30] S. Sarkar, S. Dixit, and B. Mukherjee, “Hybrid Wireless-Optical Broadband AccessNetwork (WOBAN): A Review of Relevant Challenges,” IEEE/OSA Journal of Light-wave Technology, Special Issue on Convergence of Optical Wireless Access Networks(Invited Paper), vol. 25, no. 11, pp. 3329-3340, Nov. 2007.

[31] S. Sarkar, H. Yen, S. Dixit, and B. Mukherjee, “Hybrid Wireless-Optical BroadbandAccess Network (WOBAN): Network Planning and Setup,” IEEE Journal on SelectedAreas in Communications, to appear.

[32] S. Sarkar, B. Mukherjee, and S. Dixit, “Optimum Placement of Multiple Optical Net-work Units (ONUs) in Optical-Wireless Hybrid Access Networks,” Proc., IEEE/OSAOptical Fiber Communications (OFC), Anaheim, California, March 2006.

[33] S. Sarkar, B. Mukherjee, and S. Dixit, “Towards Global Optization of Multiple ONUsPlacment in Hybrid Optical-Wireless Broadband Access Networks,” Proc., IEEE Con-ference on Optical Internet (COIN), Jeju, South Korea, July 2006.

[34] S. Sarkar, H. Yen, S. Dixit, and B. Mukherjee, “Hybrid Wireless-Optical Broad-band Access Network (WOBAN): Network Planning Using Lagrangean Relaxation,”IEEE/ACM Transactions on Networking, to appear.

[35] S. Sarkar, H. Yen, S. Dixit, and B. Mukherjee, “A Mixed Integer Programming Modelfor Optimum Placement of Base Stations and Optical Network Units in a HybridWireless-Optical Broadband Access Network (WOBAN),” Proc., IEEE Wireless Com-munications and Networking Conference (WCNC), Hong Kong, March 2007.

[36] S. Sarkar, H. Yen, S. Dixit, and B. Mukherjee, “A Novel Delay-Aware Routing Algo-rithm (DARA) for a Hybrid Wireless-Optical Broadband Access Network (WOBAN),”IEEE Network, vol. 22, no. 3, May 2008.

[37] S. Sarkar, H. Yen, S. Dixit, and B. Mukherjee, “DARA: Delay-Aware Routing Algo-rithm in a Hybrid Wireless-Optical Broadband Access Network (WOBAN),” Proc.,IEEE International Conference on Communications (ICC), Glasgow, Scotland, June2007.

[38] S. Sarkar, H. Yen, S. Dixit, and B. Mukherjee, “RADAR: Risk-and-Delay Aware Rout-ing Algorithm in a Hybrid Wireless-Optical Broadband Access Network (WOBAN),”Proc., IEEE/OSA Optical Fiber Communications (OFC), Anaheim, California, March2007.

[39] Tropos Netwoks, http://tropos.com.

[40] Firetide Networks, http://www.firetide.com.

[41] BelAir Networks, http://www.belairnetworks.com.

[42] NeoReach Networks, http://www.neoreach.com.

[43] Pronto Networks, http://www.prontonetworks.com.

[44] Strix Networks, http://www.strixsystems.com.

[45] Wavion Networks, http://www.wavionnetworks.com.

Bibliography 118

[46] Earthlink Networks, http://www.earthlink.net.

[47] Sandia National Laboratory, http://www.cs.sandia.gov/opt/survey/.

[48] S. Kirkpatrick, C. Gelatt Jr., and M. Vecchi, “Optimization by Simulated Annealing,”Science, vol. 220, no. 4598, pp. 671-680, May 1983.

[49] L. Kleinrock, Queueing Systems, Vol. I: Theory, John Wiley, 1975.

[50] H. Sherali, C. Pendyala, and T. Rappaport, “Optimal Location of Transmitters forMicro-Cellular Radio Communication System Design,” IEEE Journal on Selected Areasin Communications, vol. 14, no. 4, pp. 662-673, May 1996.

[51] M. Wright, “Optimization Methods for Base Station Placement in Wireless Applica-tions,” Proc., IEEE Vehicular Technology Conference (VTC), Ottawa, Canada, May1998.

[52] A. Molina, G. Athanasiadou, and A. Nix, “The Automatic Location of Base-stations forOptimised Cellular Coverage: A New Combinatorial Approach,” Proc., IEEE VehicularTechnology Conference (VTC), Amsterdam, Netherlands, September 1999.

[53] S. Hurley, “Automatic Base Station Selection and Configuration in Mobile Networks,”Proc., IEEE Vehicular Technology Conference (VTC), Boston, MA, September 2000.

[54] L. Nagy and L. Farkas, “Indoor Base Station Location Optimization using GeneticAlgorithms,” Proc., Personal, Indoor and Mobile Radio Communications, London,UK, September 2000.

[55] A. Hills, “Large Scale Wireless LAN Design,” IEEE Communications Magazine, vol.39, no. 11, pp. 98-107, November 2001.

[56] Y. Chen and H. Kobayashi, “Signal Strength Based Indoor Geolocation,” Proc., IEEEInternational Conference on Communications (ICC), New York, NY, April-May 2002.

[57] M. Kamenetskym and M. Unbehaun, “Coverage planning for outdoor wireless LANsystems,” Proc., International Zurich Seminar on Broadband Communications, Access,Transmission, Networking, Zurich, Switzerland, February 2002.

[58] R. Battiti, M. Brunato, and A. Delai, “Optimal Wireless Access Point Placement forLocation-dependent Services,” Technical Report, University of Trento, Italy, October2003.

[59] S. Yang, personal communication, based on traffic and services projection by ETRI,South Korea, 2006.

[60] R. Otten and L. Ginneken, The Annealing Algorithm, Kluwer, 1989.

[61] http://www.freespaceoptic.com/fiber optics without fiber.htm.

[62] http://lw.pennnet.com/articles/article display.cfm?article id=238749.

[63] http://www.networkworld.com/buyersguides/.

[64] http://www.wirelessnetdesignline.com/.

Bibliography 119

[65] http://www.infoworld.com/article/06/05/31/78801 HNwi-fiblast 1.html.

[66] http://www.dailywireless.org/2007/02/20/wimax-80216m-100-mbps/.

[67] T. S. Rapapport, Wireless Communications - Principles and Practice, Prentice-Hall,2002.

[68] WiMax Forum, http://www.wimaxforum.org.

[69] http://www.wimaxforum.org/technology/downloads/WiMAXNLOSgeneral-versionaug04.pdf

[70] A. M. Geoffrion, “Lagrangean relaxation and its uses in integer programming,” Math-ematical Programming Study, vol. 2, pp. 82-114, 1974.

[71] M. L. Fisher, “The Lagrangean relaxation method for solving integer programmingproblems,” Management Science, vol. 27, no. 1, pp. 1-18, January 1981.

[72] M. L. Fisher, W. D. Northup, and J. F. Shapiro, “Using duality to solve discreteoptimization problems: Theory and computational experience,” Mathematical Pro-gramming Study, vol. 3, pp. 56-94, 1975.

[73] R. Ahuja, T. Magnanti, and J. Orlin, Network Flows: Theory, Algorithms, and Appli-cations, Prentice-Hall, 1993.

[74] Aprisa XE Report, http://www.4rf.com.

[75] http://www.unstrung.com/document.asp?doc id=55856&site=unstrung

[76] S. Dixit, “On Fixed-Mobile Convergence,” Journal on Wireless Personal Communica-tions (Springer, Netherlands), vol. 38, no. 1, pp. 55-65, June 2006.

[77] G. Narlikar, G. Wilfong, and L. Zhang, “Designing Multihop Wireless Backhaul Net-works with Delay Guarantees,” Proc., IEEE INFOCOM 2006, Barcelona, Spain, April2006.

[78] S. Lee, G. Narlikar, M. Pal, G. Wilfong, and L. Zhang, “Admission Control for MultihopWireless Backhaul Networks with QoS Support,” Proc., IEEE Wireless Communica-tions and Networking Conference (WCNC), Las Vegas, Nevada, April 2006.

[79] T. Liu and W. Liao, “Capacity-Aware Routing with Multi-Channel Multi-Rate Wire-less Mesh Networks,” Proc., IEEE International Conference on Communications(ICC), Istanbul, Turkey, June 2006.

[80] A. H. M. Rad and V. W. S. Wong, “Joint Optimal Channel Assignment and Conges-tion Control for Multi-channel Wireless Mesh Networks,” Proc., IEEE InternationalConference on Communications (ICC), Istanbul, Turkey, June 2006.

[81] M. Kodialam and T. Nandgopal, “Characterizing the Capacity Region in Multi-radioMulti-channel Wireless Mesh Networks,” Proc., ACM MOBICOM, Cologne, Germany,September 2005.

[82] M. Alicherry, R. Bhatia, and L. Li, “Joint Channel Assignment and Routing forThroughput Optimization in Multi-radio Wireless Mesh Networks,” Proc., ACM MO-BICOM, Cologne, Germany, September 2005.

Bibliography 120

[83] R. Draves, J. Padhye, and B. Zill, “Routing in Multi-radio, Multi-hop Wireless MeshNetworks,” Proc., ACM MOBICOM, Philadelphia, Pennsylvania, September 2004.

[84] V. Gambiroza, B. Sadeghi, and E. Knightly, “End-to-end Performance and Fairnessin Multihop Wireless Backhaul Networks,” Proc., ACM MOBICOM, Philadelphia,Pennsylvania, September 2004.

[85] A. Raniwala, K. Gopalan, and T. Chiueh, “Centralized Channel Assignment and Rout-ing Algorithms for Multi-Channel Wireless Mesh networks,” ACM Mobile Computingand Communications Review, vol. 8, pp. 50-56, April 2004.

[86] P. Lin, C. Qiao, T. Wang, and J. Hu, “Optimal Utility-Based Bandwidth AllocationOver Integrated Optical And WiMAX Networks,” Proc., IEEE/OSA Optical FiberCommunications (OFC), Anaheim, California, March 2006.

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