Design and Analysis of Wireless-Optical Broadband Access Networks

Design and Analysis of Wireless-Optical
Broadband Access Networks (WOBAN)
By

SUMAN SARKAR
B.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

–i–

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.

–iv–

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

–vii–

Contents

List of Figures xi

List of Tables xiii

1 Introduction 1
1.1 Recent Trends in Optical Access Networks . . . . . . . . . . . . . . . . . . . 1
1.2 Recent Trends in Wireless Access Networks . . . . . . . . . . . . . . . . . . 4
1.3 Radio-on-Fiber – A Precursor of WOBAN . . . . . . . . . . . . . . . . . . . 6
1.4 Wireless-Optical Broadband Access Networks (WOBAN) . . . . . . . . . . 7
1.5 Research Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.5.1 WOBAN Architecture and Research Challenges . . . . . . . . . . . . 8
1.5.2 Network Planning and Setup for WOBAN . . . . . . . . . . . . . . . 8
1.5.3 Constraint Programming Model for WOBAN Deployment . . . . . . 9
1.5.4 WOBAN Connectivity and Routing . . . . . . . . . . . . . . . . . . 9
1.5.5 WOBAN Fault Tolerance and Restoration . . . . . . . . . . . . . . . 10
1.6 Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2 WOBAN Architecture and Research Challenges 12
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.1.1 Hybrid Wireless-Optical Broadband Access Network Architecture . . 13
2.1.2 Why is WOBAN a Compelling Solution? . . . . . . . . . . . . . . . 15
2.2 WOBAN’s Early Incarnations . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.3 Network Setup: A Review of Placement Algorithms in WOBAN . . . . . . 19
2.3.1 Random and Deterministic Approaches . . . . . . . . . . . . . . . . 19
2.3.2 Greedy Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.3.3 Combinatorial Optimization: Simulated Annealing Approach . . . . 20
2.3.4 Joint Optimization: Constraint Programming Approach . . . . . . . 20
2.4 Network Connectivity: A Review of Routing Algorithms in WOBAN . . . . 23
2.4.1 Minimum-Hop and Shortest-Path Routing Algorithms (MHRA and
SPRA) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.4.2 Predictive Throughput Routing Algorithm (PTRA) . . . . . . . . . 24
2.4.3 Delay-Aware Routing Algorithm (DARA) . . . . . . . . . . . . . . . 24
2.5 Fault Tolerance: Risk Awareness in WOBAN . . . . . . . . . . . . . . . . . 27
2.5.1 Risk-and-Delay Aware Routing Algorithm (RADAR) . . . . . . . . . 27
2.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

–viii–

3 Network Planning and Setup for WOBAN 29
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.1.1 Related Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.2 Placement of Multiple ONUs in WOBAN . . . . . . . . . . . . . . . . . . . 32
3.2.1 Cost Metric for ONU Deployment . . . . . . . . . . . . . . . . . . . 33
3.2.2 Greedy Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.2.3 Notations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.2.4 Running Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.2.5 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.3 Illustrative Numerical Examples: Greedy Algorithm . . . . . . . . . . . . . 38
3.3.1 Survey on Wireless Users in Wildhorse, Davis, California . . . . . . 39
3.4 Global Optimization of Placements of Multiple ONUs in WOBAN . . . . . 43
3.4.1 Simulated Annealing (SA) . . . . . . . . . . . . . . . . . . . . . . . . 44
3.4.2 Applying SA to Multiple-ONU Placement Problem of WOBAN . . . 44
3.4.3 Illustrative Numerical Examples: Greedy vs. SA . . . . . . . . . . . 46
3.5 Cost Comparison of WOBAN and PON Setup in Wildhorse . . . . . . . . . 49
3.6 Joint Optimization of WOBAN: Combined Heuristic (CH) . . . . . . . . . . 52
3.6.1 Illustrative Numerical Examples: CH . . . . . . . . . . . . . . . . . . 54
3.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

4 Constraint Programming Model for WOBAN Deployment 57
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
4.2 Design Criteria for WOBAN . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
4.3 Mathematical Formulation for Optimal Placement of BSs and ONUs . . . . 60
4.3.1 Lagrangean Relaxation and Lower Bound of Primal Model (PM) . . 65
4.3.2 Primal Algorithm and Upper Bound of Primal Model . . . . . . . . 70
4.3.3 Computing Upper Bound (UB) and Lower Bound (LB) of Primal Model 71
4.4 Performance Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
4.4.1 PM vs. CH: Impact of Carrier-to-Interference (CI) Threshold, I . . 76
4.4.2 PM vs. CH: Impact of Wireless Channel Pool, F . . . . . . . . . . . 78
4.4.3 PM vs. CH: Impact of User Coverage Ratio, ρ . . . . . . . . . . . . 79
4.4.4 PM vs. CH: Impact of Non-Homogeneous Demography . . . . . . . 80
4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

5 WOBAN Connectivity and Routing 82
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
5.1.1 San Francisco WOBAN: A Community Wireless Mesh . . . . . . . . 84
5.2 Current Routing Approaches and Opportunities . . . . . . . . . . . . . . . . 86
5.2.1 Current Routing Approaches . . . . . . . . . . . . . . . . . . . . . . 86
5.2.2 Other Research Eﬀorts . . . . . . . . . . . . . . . . . . . . . . . . . . 86
5.3 Delay-Aware Routing Algorithm (DARA) . . . . . . . . . . . . . . . . . . . 88
5.3.1 Achieving Load Balancing . . . . . . . . . . . . . . . . . . . . . . . . 90
5.3.2 Analysis of Link-State Predictions . . . . . . . . . . . . . . . . . . . 92
5.3.3 Analysis of Throughput . . . . . . . . . . . . . . . . . . . . . . . . . 94
5.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101

–ix–

6 WOBAN Fault Tolerance and Restoration 102
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
6.2 Risk-and-Delay Aware Routing Algorithm (RADAR) . . . . . . . . . . . . . 103
6.3 Analysis of RADAR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
6.3.1 Risk Awareness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
6.3.2 Self Healing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
6.3.3 Delay Awareness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
6.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110

7 Conclusion 111
7.1 WOBAN Architecture and Research Challenges . . . . . . . . . . . . . . . . 111
7.2 Network Planning and Setup for WOBAN . . . . . . . . . . . . . . . . . . . 112
7.3 Constraint Programming Model for WOBAN Deployment . . . . . . . . . . 112
7.4 WOBAN Connectivity and Routing . . . . . . . . . . . . . . . . . . . . . . 113
7.5 WOBAN Fault Tolerance and Restoration . . . . . . . . . . . . . . . . . . . 113

Bibliography 115

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List of Figures

1.1 Typical tree-based passive optical network (PON). . . . . . . . . . . . . . . 3
1.2 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. . . . . . 38
3.2 Average distance (in meters) of ONUs from their primary users. . . . . . . 39
3.3 Map of wireless routers in Wildhorse. . . . . . . . . . . . . . . . . . . . . . . 40
3.4 Map of wireless routers by their signal strengths. . . . . . . . . . . . . . . . 41
3.5 Average distance (in meters) of ONUs from their primary users in Wildhorse. 42
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 wireless users). . . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.7 Cost improvement (in meters) in WOBAN for individual ONU deployment
with SA (in the test network). . . . . . . . . . . . . . . . . . . . . . . . . . . 47
3.8 Cost improvement (in meters) in Wildhorse WOBAN for individual ONU
deployment 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). . . . . . . 72
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. . . 77
4.3 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. . . 78
4.4 Impact of user coverage ratio on normalized deployment cost (with I = 12
dB and |F | = 50 channels). . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

5.1 A WOBAN’s upstream and downstream protocols. . . . . . . . . . . . . . . 83
5.2 San Francisco WOBAN and its front-end wireless mesh (SFNet). . . . . . . 85
5.3 Diﬀerential and asymmetric capacity assignment. . . . . . . . . . . . . . . . 90
5.4 Link-state predictions (LSPs) used at time intervals. . . . . . . . . . . . . . 93

–xi–

5.5 Average delay vs. load in SFNet. . . . . . . . . . . . . . . . . . . . . . . . . 95
5.6 Delay vs. load [for the furthest router/gateway pair (1, 25)] in SFNet. . . . 96
5.7 Comparing K-DARA (K > 1) path delays with PTRA delay [for the furthest
router/gateway pair (1, 25)] in SFNet. . . . . . . . . . . . . . . . . . . . . . 97
5.8 Average number of K-DARA (K > 1) paths under PTRA delays in SFNet. 97
5.9 Average hops vs. load in SFNet. . . . . . . . . . . . . . . . . . . . . . . . . 98
5.10 Hop distributions vs. load in SFNet. . . . . . . . . . . . . . . . . . . . . . . 98
5.11 Load balancing (or link congestion) vs. load in SFNet. . . . . . . . . . . . . 99
5.12 Actual vs. predicted packet intensities at high loads. . . . . . . . . . . . . . 100
5.13 Actual vs. predicted packet intensities at low loads. . . . . . . . . . . . . . 100

6.1 An illustration of RADAR. . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
6.2 Packet loss for gateway failure. . . . . . . . . . . . . . . . . . . . . . . . . . 108
6.3 Packet loss for ONU failure. . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
6.4 Packet loss for OLT failure. . . . . . . . . . . . . . . . . . . . . . . . . . . . 110

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List of Tables

2.1 A sample of municipal access networks. . . . . . . . . . . . . . . . . . . . . 18
2.2 Pros and cons of various placement schemes in WOBAN. . . . . . . . . . . 22
2.3 Pros and cons of various routing algorithms in the wireless part of a WOBAN. 26

3.1 Research activities on network placement. . . . . . . . . . . . . . . . . . . . 31
3.2 A small part of scanning results from Wildhorse. . . . . . . . . . . . . . . . 41
3.3 Wildhorse WOBAN user distributions (in hop count). . . . . . . . . . . . . 42
3.4 Simulation parameters for SA. . . . . . . . . . . . . . . . . . . . . . . . . . 47
3.5 Various components of WOBAN and PON expenditure. . . . . . . . . . . . 50
3.6 Device and ﬁber layout expenses (in normalized units). . . . . . . . . . . . . 50
3.7 ONU, WiFi, and WiMAX capacities. . . . . . . . . . . . . . . . . . . . . . . 51
3.8 WOBAN and PON setup expenditures (in normalized units). . . . . . . . . 51
3.9 Estimation of channel interference and number of BSs by CH. . . . . . . . . 53

4.1 WiMAX modulations vs. CI. . . . . . . . . . . . . . . . . . . . . . . . . . . 73
4.2 Device and ﬁber layout expenses (in normalized units). . . . . . . . . . . . . 75
4.3 Number of BSs and ONUs. . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

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

6.1 Risk List (RL) in a router. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
6.2 Updated Risk List for Gateway failure. . . . . . . . . . . . . . . . . . . . . . 106
6.3 Updated Risk List for ONU failure. . . . . . . . . . . . . . . . . . . . . . . . 106
6.4 Updated Risk List for OLT failure. . . . . . . . . . . . . . . . . . . . . . . . 106

–xiii–

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 the
1
With 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 Downlink
2
Throughout 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.

Table 2.1: A sample of municipal access networks.
Area/Location Architecture Compatibility Configuration Operating Player
Present Future Range
Akron, OH Flat AP infrastructure WiFi WiFi Multiple radio 2.4 GHz MobilePro
Athens, GA Multi-layered MP2P WiFi WiFi, WiMAX Multi-radio 2.4, 5 GHz Belair
Bristol, UK Multi-layered deployment WiFi WiFi, WiMAX Multi-radio multi-antenna 2.4, 5 GHz Belair
Chaska, MN Flat deployment WiFi WiFi, WiMAX Single radio (omni-directional) 2.4 GHz Tropos, Pronto
Corpus Christi, TX Flat deployment (GPS-compatible) WiFi WiFi Single radio (omni-directional) 2.4 GHz Tropos, Pronto
Culver City, CA Flat (intermesh capable) WiFi WiFi Multi-radio omni-directional 2.4, 5 GHz Firetide
Farmers Branch, TX Gateways with OC-3 ingress WiFi WiFi, WiMAX Multiple radio 2.4, GHz NeoReach, Pronto
Galt, CA Multi-layered deployment WiFi WiFi, WiMAX Multi-radio multi-antenna 2.4, 5 GHz Belair
Gilbert, AZ Flat AP infrastructure WiFi WiFi Multiple radio 2.4 GHz MobilePro
Gordes, France Flat with intermesh WiFi WiFi Multi-radio omni-directional 2.4, 5 GHz Firetide
Isla Vista, CA Flat deployment WiFi WiFi Multi-radio 2.4, 5 GHz Firetide
Islington, UK 3-tier hierarchical deployment WiFi WiFi Multi-radio multi-antenna 2.4, 5 GHz Belair
Moorehead, MN P2MP with fiber optic backbone WiFi WiFi Single radio (omni-directional) 2.4 GHz Tropos
New Orleans, LA WiFi routers with digital IP cameras attached to IP-backbone for video surveillance system Tropos
Chapter 2: WOBAN Architecture and Research Challenges

Philadelphia, PA Currently being deployed Earthlink
San Francisco, CA Currently being deployed Earthlink, Google
Springfiled, MO Hierarchical (L2 VLAN capable) WiFi WiFi Multi-radio 2.4, 5 GHz Belair
St. Maarten, Carribean Hierarchical deployment WiFi WiFi Multi-radio 2.4, 5 GHz Belair, Lucent
Tempe, AZ Gateways with OC-3 ingress WiFi WiFi, WiMAX Multi-radio multi-antenna 2.4, 5 GHz Strix, NeoReach
Wavion, Inc. is a new player with their “spatially adaptive” MIMO-based routers having an antenna array and six radio transreceivers.
18


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.

Table 2.2: Pros and cons of various placement schemes in WOBAN.
Placement Scheme Objective Solution Quality Processing time Comments (in brief)
Random Placements Worse Constant Simple.
of Optical Trial-and-error methods may be used.
Deterministic Network Better Constant Works well for symmetric topology.
Units Pre-determined placement.
(ONU) in No prior optimization.
Greedy Algorithm WOBAN. Good Linear (in practical cases) Low complexity.
Divide-and-conquer heuristic.
Good solution for uniform distribution of users.
Simulated Annealing Improved over Greedy Depends on convergence criteria Combinatorial optimizer.
Improved solution over Greedy.
May not converge for discontinuous cost model.

Primal Model Optimum Optimal Very high Complex analytical solution.
setup of Considers several constraints.
ONUs/BSs. Model predicts setup costs in dollars.
22


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].

Table 2.3: Pros and cons of various routing algorithms in the wireless part of a WOBAN.
Routing Objective Link Alternative Performance
algorithm prediction path Delay Throughput Hop count Load balancing Risk
used used H L H L H L H L awareness
√ √ √ √
MHRA Hop minimization; No No × × × × No
unity link weight.
√ √ √ √
SPRA Shortest path; No No × × × × No
inverse-capacity
link weight.
√ √ √ √
PTRA Throughput No Yes × × × × No
optimization.
√ √ √ √ √ √ √
DARA Delay Yes Yes × No
minimization.

√ √ √ √ √ √ √
RADAR Minimize delay Yes Yes × Yes
and packet loss.
H: High load (0.5-0.95), L: Low load (0.0-0.49)
√
: Algorithm performs well, ×: Algorithm performs poorly.
26


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].

Table 3.1: Research activities on network placement.
Research Work Objective Setting Cost Model Optimization Contributions (in brief)
Sherali et al. [50] Optimum Outdoor Signal strength Hooke-Jeeves, Quasi-Newton, Hill-climbing Minisum, Minimax, combination model;
placements Captures single and multiple Tx problems.
Wright [51] of Base Indoor Signal propagation Nelder-Mead Direct Search Generic model with attenuation;
Stations Finds local optimum.
Molina et al. [52] for Outdoor Distance Greedy, Genetic, Greedy+Genetic Optimized cellular coverage;
minimizing Combinatorial approach.
Hurley [53] the total Outdoor Signal, Distance, Traﬃc Simulated Annealing Multiple costs approach;
cost of Cell handover considered.
Nagy et al. [54] network Indoor Motley-Keenan path loss Genetic Algorithm Empirical model;
setup. High complexity.
Hills [55] Indoor Signal strength Trial-and-error Cylindrical design approach;
Chapter 3: Network Planning and Setup for WOBAN

Deployed in Carnegie Mellon.
Chen et al. [56] Indoor Bahl’s path loss Linear Regression, Least Square Both signal-strength and location awareness;
Generic model with attenuation.
Kamenetskym et al. [57] Outdoor Signal strength Uniform, Pruning, Simulated Annealing Empirical model;
Minisum and Minimax approaches.
Battiti et al. [58] Outdoor Signal strength Tabu, Hill-climbing, Simulated Annealing Localization + signal-coverage model;
Shows trade-oﬀ between two metrics.
31


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, ..., ki ). We model the cost to deploy the i-th ONU as the

average “Euclidean” distance from that ONU to its users as follows:
 
ki
1 
CON Ui = ∗ (xj − Xi )2 + (yj − Yi )2  . (3.1)
ki
j=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.),

• ON Uij rimary : Primary ONU i (minimum-distance ONU) for user j,
P

• 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

N
• ki : Premium users for ONU i (where i=1 ki = 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(N logN ). 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(ki ). 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(N logN ) + k ∗ O(1) + N ∗ O(ki ) + N ∗ O(1) = O(kN + kN logN + N ki ) =


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 ONU
placements.

2. Find the distances between a user j and all the ONUs, dij =
(xj − Xi ) 2 + (y − Y )2 , ∀i ∈ (1, 2, 3, ..., N ). Repeat the same for all other
j i
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 = i }. Repeat the same for all other users,
i.e., ∀j ∈ (1, 2, 3, ..., k).

4. Identify a primary ONU for each user, where ON Uij rimary = mini {SDj }.
P

Phase 2: Find Placement of Primary ONUs

1. Obtain the set of users (call them “premium users”) for each primary ONU for which
the distances between the ONU and its users are minimum (compared to all other
ONUs), SUi = {(xj , yj ) : (Xi − xj )2 + (Yi − yj )2 is min}, ∀j ∈ (1, 2, 3, ..., ki ), ki ≤
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, ..., ki ), place ON Ui at the mean of
ki ki
j=1 xj j=1 yj
the users’ X/Y-coordinates. Therefore, (Xi , Yi ) = ki
, ki
. Repeat the

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

Design and Analysis of Wireless-Optical Broadband Access Networks

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