WIRELESS MESH NETWORKS: PROTOCOL DESIGN AND A …kx780hc2331/PhD... · 2011-09-22 · wireless mesh networks: protocol design and performance evaluation a dissertation submitted to

WIRELESS MESH NETWORKS: PROTOCOL DESIGN AND

PERFORMANCE EVALUATION

A DISSERTATION

SUBMITTED TO THE DEPARTMENT OF ELECTRICAL

ENGINEERING

AND THE COMMITTEE ON GRADUATE STUDIES

OF STANFORD UNIVERSITY

IN PARTIAL FULFILLMENT OF THE REQUIREMENTS

FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY

Hyunok Lee

March 2010

http://creativecommons.org/licenses/by-nc/3.0/us/

This dissertation is online at: http://purl.stanford.edu/kx780hc2331

© 2010 by Hyunok Lee. All Rights Reserved.

Re-distributed by Stanford University under license with the author.

This work is licensed under a Creative Commons Attribution-Noncommercial 3.0 United States License.

ii



http://purl.stanford.edu/kx780hc2331

I certify that I have read this dissertation and that, in my opinion, it is fully adequatein scope and quality as a dissertation for the degree of Doctor of Philosophy.

Donald Cox, Primary Adviser


John Cioffi


Fouad Tobagi

Approved for the Stanford University Committee on Graduate Studies.

Patricia J. Gumport, Vice Provost Graduate Education

This signature page was generated electronically upon submission of this dissertation in electronic format. An original signed hard copy of the signature page is on file inUniversity Archives.

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Abstract

With the increasing interest in multi-hop wireless communications networks, wire-

less mesh networks (WMNs) have emerged as an affordable and scalable solution to

provide broadband packet data communications across wide geographic areas. How-

ever, due to the prohibitive complexity of analysis and simulations, studies on WMNs

for large-scale applications have often oversimplified the physical and/or networking

models.

In this thesis, based on more realistic physical and networking models, we study

the performance of large-scale WMNs that serve as access networks over large geo-

graphic areas. First, we create a new set of medium access control (MAC) protocols

that incorporate such models. The protocols are designed within a time division mul-

tiple access (TDMA) and time division duplex (TDD) framework. Utilizing separate

resources for control and data packets, the protocols provide mechanisms for network

entities to explicitly cooperate among themselves for resource allocation in a fully

distributed and adaptive manner.

We also develop a large WMN simulator that implements the protocols and sup-

ports measurement-based models for radio propagation and interference calculation

for a large built-in urban area. The simulator also captures the stochastic network

behavior resulting from random traffic arrivals, admission control, and queueing. The

enormous size and computational complexity of the simulator is addressed using a

parallel-processing simulation technique that utilizes multiple processors intercon-

nected with high-speed links and associated with large high-speed memory.

Through extensive simulations incorporating such details, the performance of the

WMNs is assessed under various simulation scenarios. First, it is demonstrated that

iv

an effective admission and congestion control (ACC) policy is critical to support stable

user throughput under heavy traffic loads, and one of the ACC policies created in

the thesis is shown to stabilize the network even under heavy traffic loads. Then, the

scalability of the WMNs is investigated under different scenarios of network topology

and routing metrics. The scalability behavior of several fundamental performance

metrics is examined including the network throughput, per-session throughput, and

blocking and dropping rates. Major factors are identified across the physical, MAC

and routing layers that affect the scalability behavior, and the factors are shown

to interact with one another in a complicated manner to determine the network

performance. With more backbone support to the network, the network throughput

and per-session throughput are shown to improve significantly, and the improvement

is explained based on the aforementioned interactions across the layers of the network.

The overall network performance is shown for two different routing metrics.

v

Acknowledgment

First of all, I would like to express my deep gratitude to my advisor Professor Donald

C. Cox for his continuous and consistent support, guidance, and encouragement.

I sincerely thank him for his endeavor to secure financial support for most of my

Ph.D. years. I also thank him for his innumerable hours for discussions with me.

Those discussions repeatedly inspired me to tackle problems from fresh, different, and

greater perspectives. I am also grateful for his continuous encouragement that kept

me to develop the large-scale wireless mesh network simulator presented in this thesis.

Without his encouragement, I would not have been able to study the wireless mesh

networks as thoroughly. I also thank him for his careful and invaluable suggestions

for this dissertation.

I also would like to thank Professor John Cioffi for his serving as my oral and

reading committee member as well as serving as my associate advisor for my Ph.D. I

also thank Professor Fouad Tobagi for his serving as my oral and reading committee

member and for his helpful suggestions for this dissertation. I also thank Professor

John Pauly for serving as my oral chairperson.

I thank Dr. Nim Cheung. He supported me to work on the project on parallel-

processing simulation techniques that eventually became an indispensable tool for my

large-scale simulations of the wireless mesh networks presented in this thesis. He also

helped me with writing an extensive research proposal that later became a basis for

my thesis research.

I wish to thank the government of Republic of Korea for the Information and

Telecommunication National Scholarship that partially supported my M.S. and the

first two years of my Ph.D. at Stanford University. My research was also supported

vi

in part by the National Science Foundation (NSF), Ericsson Inc., and the Center

for Integrated Systems (CIS) at Stanford University. The US Army Research Office

supported the acquisition of the supercomputing platform on which my large-scale

parallel simulations have been run.

During my years with the Wireless Communications Research Group, I have

greatly benefited from many interactions and discussions with my former and cur-

rent group members: Ali Faghfuri, Dana Porrat, Hichan Moon, Karen Tian, Kerstin

Johnsson, Mark Smith, Mehdi Soltan, Persefoni Kyritsi, Qinfan Sun, Ravi Narasimhan,

Raymond Wang, Tom McGiffen, Wonchae Kim, and Yasimin Mostofi. I also thank

Professor Homayoun Hashemi for his encouragement and feedback on my research

presentations. I also thank Bernadette Aguiao, Pat Oshiro, and late Joice Debolt for

their administrative help. I also benefited from interactions and discussions with my

colleagues and co-authors Vahideh Manshadi, Alex Shaw, Gordon Wong, and Profes-

sor Divanilson Campelo. I specially thank Vahideh Manshadi for her contribution to

the development of the parallel-processing simulation technique.

I thank Pat Burke for his countless help with the system administration of the

group computers as well as the supercomputing system “multipath”. I also thank

Timothy Chevalier and Mike Chevalier as they helped me to set up “multipath” for

running parallel programs and provided resources for me to learn how to write parallel

programs. I thank David Nguyen and Tung Nguyen for their technical support for

maintaining “multipath” over the years. I also thank other users of “multipath” for

their help with maintaining the system: Amit Vyas, Mukesh Hira and Sangwook Ha.

My life at Stanford would not have been as enjoyable and memorable without these

people. I wish to thank my friends Moon-Jung Kim and Su-Jeong Ok for their selfless

love and unchanging support for me throughout my years at Stanford. I also thank all

of the Korean Christian Fellowship (KCF) members. I specially thank Pastor Don,

Maria SMN, Jeon JSN, Kwon GSN, Misung Han, Jungwoo Lee, Meeyoung Park,

Hyun Jin Kim, and Min-Sung Kim. I also thank Postechians and special thanks go

to Daeho Lee, Hyejean Suh, Wonjae Lee, Keonwook Kang, and Jieun Rim.

Finally, I thank my friends and family members back in Korea. I thank my friends

Sun-Ae Kim and Seong-Ok Lee for their unchanging friendship. I also thank my

vii

sister Hyun-Jung Lee and my brother Chang-Ho Lee for their unconditional support

and love. I am indescribably indebted to my parents for their love and sacrifices

throughout my entire life. I dedicate this dissertation to them.

viii

Contents

Abstract iv

Acknowledgment vi

1 Introduction 1

1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.2 Thesis Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2 Simulated Network 7

2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.2 Network Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.3 MAC Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.4 Radio Propagation Environment and Simulation Models . . . . . . . 11

2.4.1 Radio Propagation Environment . . . . . . . . . . . . . . . . . 11

2.4.2 Radio Propagation Models for Large-Scale Radio Propagation 13

2.4.3 Toroidal Universe and SINR Calculation . . . . . . . . . . . . 21

2.5 PHY Configurations . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

2.6 Traffic Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

2.7 WMN Simulator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

2.7.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

2.7.2 Simulation Routine . . . . . . . . . . . . . . . . . . . . . . . . 27

2.8 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

ix

3 Control Time Slot Assignment Protocol 29

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

3.2 Protocol Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

3.2.1 Basic Mechanisms . . . . . . . . . . . . . . . . . . . . . . . . . 32

3.2.2 Notation and Terminology . . . . . . . . . . . . . . . . . . . . 33

3.3 Operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

3.3.1 tBUSY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

3.3.2 rBUSY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

3.3.3 tBUSY/RTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

3.3.4 NACK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

3.4 Time Slot Selection Strategy . . . . . . . . . . . . . . . . . . . . . . . 38

3.5 Parameter Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

3.5.1 PTH NBR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

3.5.2 SINRTH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

3.5.3 PTH T . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

3.5.4 PTH R . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

3.6 Hidden Node Problem Revisited . . . . . . . . . . . . . . . . . . . . . 42

3.7 Power Control of Receive Busy Tones . . . . . . . . . . . . . . . . . . 44

3.8 Protocol Initialization . . . . . . . . . . . . . . . . . . . . . . . . . . 46

3.9 Deadlock Resolution . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

3.10 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

3.10.1 Baseline Performance . . . . . . . . . . . . . . . . . . . . . . . 49

3.10.2 Effect of Shadowing . . . . . . . . . . . . . . . . . . . . . . . . 49

3.10.3 Performance of Power Control of Receive Busy Tones . . . . . 53

3.10.4 Effect of GR Topology . . . . . . . . . . . . . . . . . . . . . . 53

3.10.5 Effect of NACK Transmission Conditions . . . . . . . . . . . . 56

3.11 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

4 Data Time Slot Access Control Protocol 58

4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

4.2 Resource Negotiation . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

x

4.2.1 Between EU and MR . . . . . . . . . . . . . . . . . . . . . . . 60

4.2.2 Among One-Hop Neighbor MRs . . . . . . . . . . . . . . . . . 61

4.3 Data Time Slot Selection . . . . . . . . . . . . . . . . . . . . . . . . . 63

4.4 Queue/Session Prioritization . . . . . . . . . . . . . . . . . . . . . . . 64

4.5 Data Transmission/Retransmission . . . . . . . . . . . . . . . . . . . 64

4.6 Resource Release . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

4.7 Routing Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

4.7.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

4.7.2 Processing and Forwarding Announcements . . . . . . . . . . 66

4.7.3 Generating and Forwarding Replies to Announcements . . . . 66

4.7.4 Routing Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . 67

4.8 Admission and Congestion Control . . . . . . . . . . . . . . . . . . . 70

4.8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

4.8.2 Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

4.8.3 AC RO (Reception Only) . . . . . . . . . . . . . . . . . . . . 72

4.8.4 AC RF (Reception Forwarding) . . . . . . . . . . . . . . . . . 72

4.8.5 Stability Properties of AC RF . . . . . . . . . . . . . . . . . . 73

4.9 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

4.9.1 Simulation Setup . . . . . . . . . . . . . . . . . . . . . . . . . 75

4.9.2 Performance Metric . . . . . . . . . . . . . . . . . . . . . . . . 75

4.9.3 Results under AC RO . . . . . . . . . . . . . . . . . . . . . . 76

4.9.4 Results under AC RF . . . . . . . . . . . . . . . . . . . . . . . 82

4.10 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

5 Scalability 89

5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

5.2 Scalability with Different GR Topology . . . . . . . . . . . . . . . . . 90

5.2.1 Network Throughput . . . . . . . . . . . . . . . . . . . . . . . 92

5.2.2 Per-Session Throughput . . . . . . . . . . . . . . . . . . . . . 96

5.2.3 Blocking Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . 99

5.2.4 Dropping Rate . . . . . . . . . . . . . . . . . . . . . . . . . . 103

xi

5.3 Scalability with Different Routing Metrics . . . . . . . . . . . . . . . 106

5.3.1 Network Throughput . . . . . . . . . . . . . . . . . . . . . . . 107

5.3.2 Per-Session Throughput . . . . . . . . . . . . . . . . . . . . . 107

5.3.3 Blocking and Dropping Rates . . . . . . . . . . . . . . . . . . 110

5.4 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112

6 Conclusions 113

6.1 Thesis Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113

6.2 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115

6.3 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117

A Parallel Time-Driven Simulation 119

A.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119

A.2 Principles of the Technique . . . . . . . . . . . . . . . . . . . . . . . . 121

A.2.1 Simulation Platform . . . . . . . . . . . . . . . . . . . . . . . 121

A.2.2 Workload Partitioning . . . . . . . . . . . . . . . . . . . . . . 121

A.2.3 Synchronization . . . . . . . . . . . . . . . . . . . . . . . . . . 125

A.2.4 Database Design . . . . . . . . . . . . . . . . . . . . . . . . . 127

A.2.5 Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130

A.2.6 System Routine . . . . . . . . . . . . . . . . . . . . . . . . . . 131

A.3 Numerical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131

A.3.1 Mobile Cellular Network Simulator . . . . . . . . . . . . . . . 132

A.3.2 Large Wireless Mesh Network Simulator . . . . . . . . . . . . 137

A.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137

Bibliography 139

xii

List of Tables

2.1 Simulation models and parameters for radio propagation . . . . . . . 21

2.2 PHY configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

2.3 PHY transmission data rate vs. average received SINR . . . . . . . . 24

3.1 Probability for a MR to have at least 2 neighbor MRs within 400

meters under the simulated network topology and radio propagation

environment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

5.1 Parameter values for simulated GR topology . . . . . . . . . . . . . . 92

xiii

List of Figures

2.1 The wireless mesh network (WMN) scenario considered in this thesis:

it has 2-layered network structure consisting of mesh routers (MRs)

and end users (EUs) and serves as a large access network. A subset of

MRs are co-located with gateway routers (GRs) that are wired to the

backbone network. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.2 The network frame structure: time is slotted into control and data

time slots and each time slot comprises multiple subslots. See the text

for detailed explanations of each of the subslots. . . . . . . . . . . . . 10

2.3 The network topology of the simulated network. . . . . . . . . . . . . 12

2.4 Radio signal propagation scenario when the transmitter is at an in-

tersection: the signal propagates along radial or line-of-sight (LOS)

streets and along cross streets. . . . . . . . . . . . . . . . . . . . . . . 13

2.5 Simulated received signal power (dBm) when the transmitter is at an

intersection, path-loss only, transmitter = MR: (a) along a line-of-

sight (LOS) street. Breakpoints are determined by Eqn. (2.1); (b)

along cross streets, receiver = MR. Each curve corresponds to a cross

street. Slope and corner attenuation are determined by Eqn. (2.2) and

Eqn. (2.3). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

2.6 Radio signal propagation scenario when the transmitter is off an inter-

section: the signal propagates along radial, cross, and parallel streets. 16

xiv

2.7 Simulated received signal power (dBm) along parallel streets when

transmitter is off an intersection, path-loss only, transmitter = MR

(in the middle of a block), receiver = MR. Each curve corresponds to a

parallel street and peaks correspond to intersections with cross streets.

Slopes and corner attenuation are determined by Eqn. (2.2) and (2.3)

with respect to virtual transmitters. . . . . . . . . . . . . . . . . . . . 18

2.8 Illustration of four shadow maps maintained for each mesh router: (a)

shows two shadow maps, map V and map H, and (b) shows two shadow

maps, map VH and map HV. . . . . . . . . . . . . . . . . . . . . . . 19

2.9 Illustration of the simulated toroidal universe with a transmitter at

the center. The red rectangle in a solid line denotes the original finite

universe, whose replicas tessellate the infinite universe. The blue rect-

angle in a dashed line denotes the universe considered in this thesis for

calculating the received interference level at the receiver. . . . . . . . 22

2.10 Overall simulation routine . . . . . . . . . . . . . . . . . . . . . . . . 26

3.1 The network frame structure: time is slotted into control and data time

slots and each time slot comprises multiple subslots. See Section 3.3

for detailed explanation of each of the control subslots. . . . . . . . . 32

3.2 Example of conditions for transmitting NACK packets for two transmit

MRs A and B that are received at a MR with average received power

PA and PB respectively. . . . . . . . . . . . . . . . . . . . . . . . . . 37

3.3 Scatter plot of average received signal power vs. separation for a pair

of neighbor MRs, PTH NBR = -50 dBm and σS = 4 dB . . . . . . . . 41

3.4 Illustration of the condition in Eqn. (3.18) for an active link to become

degraded due to a new contending MR. . . . . . . . . . . . . . . . . . 43

3.5 (a) The number of mesh routers that acquired a specific control time

slot vs. control time slot index and (b) the distribution of SINR over

links between two neighbor mesh routers, both for different values of

αR. In all cases, we set αT = αR. . . . . . . . . . . . . . . . . . . . . 50

xv



links between two neighbor mesh routers, both for different values of

the standard deviation of the log-normal shadowing. In all cases, we

set αT = αR = 1 dB. . . . . . . . . . . . . . . . . . . . . . . . . . . . 51



links between two neighbor mesh routers, both for with and without

the power control of receive busy tones. In all cases, we set αT = αR

= 1 dB. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54



links between two neighbor mesh routers, both for different GR topolo-

gies. In all cases, we set αT = αR = 1 dB, and the power control of

receive busy tones is employed. . . . . . . . . . . . . . . . . . . . . . 55

4.1 The network frame structure: time is slotted into control and data

time slots and each time slot comprises multiple subslots. . . . . . . . 60

4.2 Example for illustrating the two different routing metrics. PA→B 6=PA→C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

4.3 Distribution of the number of hops under two different link metrics

considered in this thesis. (#MRs):(#GRs) = 40:1 . . . . . . . . . . . 70

4.4 (a) Mean network throughput vs. session arrival rate; (b) mean per-

session throughput for successfully completed sessions vs. session ar-

rival rate. Both under AC RO and (#MRs):(#GRs) = 40:1 . . . . . 77

4.5 (a) Mean PHY transmission rate of successfully received data packets

among MRs vs. session arrival rate; (b) dropping rate vs. session

arrival rate. Both under AC RO and (#MRs):(#GRs) = 40:1 . . . . 79

4.6 Mean per-session throughput for successfully completed sessions vs.

path lengths (hops) under AC RO and (#MRs):(#GRs) = 40:1 . . . 82

xvi

4.7 (a) Mean network throughput for successfully completed sessions vs.

session arrival rate; (b) mean per-session throughput for successfully

completed sessions vs. session arrival rate. Both are under (#MRs):(#GRs)

= 40:1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

4.8 (a) Blocking rate vs. session arrival rate; (b) dropping rate vs. session

arrival rate. Both are under (#MRs):(#GRs) = 40:1. . . . . . . . . . 86

4.9 Mean PHY transmission rate of successfully received packets among

MRs. (#MRs):(#GRs) = 40:1. . . . . . . . . . . . . . . . . . . . . . 87

5.1 The network topology of the simulated network. . . . . . . . . . . . . 90

5.2 Distribution of the number of hops along paths from MRs to their best

GRs under different scenarios of GR topology and routing metric. . . 91

5.3 Mean network throughput vs. session arrival rate under different gate-

way router topologies. In all cases, the routing metric ‘min air-time’ is

used. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

5.4 Mean PHY transmission rate of successfully received data packets

among MRs under different gateway router topologies. In all cases,

the routing metric ‘min air-time’ is used. . . . . . . . . . . . . . . . . 94

5.5 Mean aggregate number of data time slots used for reception at MRs

across the network under different gateway router topologies. In all

cases, the routing metric ‘min air-time’ is used. . . . . . . . . . . . . 95


session arrival rate under different gateway router topologies. In all

cases, the routing metric ‘min air-time’ is used. . . . . . . . . . . . . 97

5.7 (a) Mean per-session throughput for successfully completed sessions

vs. path lengths (hops) under (#MRs):(#GRs) = 40:1; (b) mean per-

session throughput for successfully completed sessions vs. path lengths

(hops) under (#MRs):(#GRs) = 10:1. In both cases, AC RF and the

routing metric ‘min air-time’ are considered. . . . . . . . . . . . . . . 98

5.8 Blocking rate vs. session arrival rate under different gateway router

topologies. In all cases, the routing metric ‘min air-time’ is used. . . . 100

xvii

5.9 (a) Blocking rate at MRs as a function of path lengths from MRs to

best GRs under (#MRs):(#GRs) = 40:1; (b) blocking rate at MRs as a

function of path lengths from MRs to best GRs under (#MRs):(#GRs)

= 10:1. In both cases, AC RF and the routing metric ‘min air-time’

are considered. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101

5.10 Dropping rate vs. session arrival rate under different gateway router

topologies. In all cases, the routing metric ‘min air-time’ are considered.104

5.11 (a) Dropping rate at MRs as a function of path lengths from MRs to

best GRs under (#MRs):(#GRs) = 40:1; (b) dropping rate at MRs as a

function of path lengths from MRs to best GRs under (#MRs):(#GRs)

= 10:1. In both cases, AC RF and the routing metric ‘min air-time’

are considered. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

5.12 Mean network throughput vs. session arrival rate under the two dif-

ferent routing metrics considered and AC RF. . . . . . . . . . . . . . 108

5.13 Mean PHY transmission rate of successfully received data packets

among MRs under the two different routing metrics considered and

AC RF. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109


session arrival rate under the two different routing metrics considered

and AC RF. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110

5.15 (a) Blocking rate vs. session arrival rate; (b) dropping rate vs. session

arrival rate under the two different routing metrics considered and

AC RF. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111

A.1 Our supercomputing platform . . . . . . . . . . . . . . . . . . . . . . 122

A.2 Primary communication pattern among processors in one simulation

time step: (a) geography-based; (b) channel-based workload partition-

ing. Pi denotes a processor. . . . . . . . . . . . . . . . . . . . . . . . 123

A.3 Database structure and operations executed in one simulation time step

for an example of two processors. A computation phase is followed by

a communication phase. . . . . . . . . . . . . . . . . . . . . . . . . . 128

xviii

A.4 Performance of the parallel simulation technique for a mobile cellular

network simulator and a WMN simulator: (a) runtime vs. number of

processors; (b) speedup gain vs. number of processors. . . . . . . . . 133

A.5 Grade of service (GOS) vs. number of processors of a mobile cellular

network. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135

A.6 Grade of service (GOS) vs. offered traffic (Erlang/cell) for the simu-

lated mobile cellular network with and without extrapolation for 128

channels, along with Erlang-B system. . . . . . . . . . . . . . . . . . 136

xix

Chapter 1

Introduction

1.1 Motivation

Multi-hop wireless communications networks have been of increasing interest over the

past decade. Two main types of such networks have emerged: mobile ad-hoc networks

(MANETs) and wireless mesh networks (WMNs). MANETs are infrastructure-less,

non-hierarchical wireless networks where all network entities can move and function

as routers and discover and maintain routes to other entities in the network. On

the other hand, WMNs typically have a hierarchical structure and are supported by

an infrastructure. Furthermore, the functionalities of the network entities may vary

significantly across the layers of the hierarchical network structure. While MANETs

have been historically envisioned to serve very specialized applications, such as bat-

tlefields and emergency situations, WMNs have inspired numerous general applica-

tions ranging from broadband home networking to community networks to high-speed

metropolitan area networks (MANs) [1–3]. Particularly, large-scale WMNs with in-

frastructure support have been considered as an affordable and scalable solution to

provide broadband packet data communications across wide geographic areas, thanks

to their inherent advantages such as robustness to node failures, ease of deployment

and maintenance, and low initial deployment cost [1–3].

The architecture of WMNs, i.e., the structure of the hierarchy and the assumptions

on the functionalities of the network entities, is mainly determined by the target

1

CHAPTER 1. INTRODUCTION 2

application scenario [1]. In this thesis, we consider large-scale WMNs that serve as

wireless access networks over large geographic areas. Such an application scenario

of WMNs often has been proposed to have a layered network structure. Examples

include the public Wi-Fi mesh network deployed by Google, Inc. [4] in the downtown

area of Mountain View, CA and the proposed city-wide public wireless access network

in San Francisco, CA submitted jointly by Google, Inc. and Tropos Networks, Inc [5].

Such a layered network structure typically contains end-users at the lowest layer,

wireless mesh routers in the middle layer, and other aggregation nodes at the top

layer. The aggregation nodes of the highest layer may include another set of wireless

entities that communicate wirelessly with the wireless mesh routers of the middle

layer, or may be entirely wired through e.g., optical or DSL networks. In this thesis,

we focus on the two lowest layers that consist of end-users and wireless mesh routers.

A set of wireless mesh routers are assumed to be co-located with wired network

entities called gateway routers.

Substantial research on WMNs has been conducted and several protocols have

been proposed by various standardization bodies [3]. Particularly, routing for multi-

hop wireless communications networks has been extensively studied in the context

of MANETs [6–9] and more recently for WMNs [10–12]. These studies commonly

assume given protocol stacks for medium access control (MAC) and physical (PHY)

layer of network functions such as those of the IEEE 802.11 standards [13].

Compared to routing, research on MAC for WMNs has been relatively sparse.

The majority of the studies on MAC for WMNs consider contention-based schemes

such as the IEEE 802.11 MAC protocol that is based on the carrier sense multiple

access (CSMA) with collision avoidance (CA) principle. CSMA/CA-based schemes

initiate the assessment of the availability of the medium or resources for transferring

data packets mainly by sensing or measuring the power level of the medium directly.

On the other hand, another type of MAC schemes [14–16] has been proposed. These

schemes allocate separate resources for control and data packets, and the availability

of resources for transferring data packets is assessed through exchanging control pack-

ets using control resources. Data resources are then reserved through these control

packets.


The former type of MAC schemes based on the CSMA/CA principle inherently

has less control overhead compared to the latter type of MAC schemes and thus may

lead to higher network throughput when the traffic load is light. However, as the

traffic load increases and increasingly more nodes need to acquire the medium simul-

taneously, it may become possible to coordinate data packet transmissions among

nodes more efficiently when resources are divided for control and data packets and

the availability or usage of data resources can be known and controlled by exchanging

control packets. In this thesis, we focus on the latter type of MAC schemes. Com-

paring the two different types of MAC schemes for the type of WMNs considered in

this thesis (i.e., access networks deployed over large geographic areas) is beyond the

scope of this thesis and is listed as a possible future research direction in Chapter 6.

Due to their prohibitive complexity of analysis and simulations, most of the studies

on WMNs for large-scale applications have often oversimplified the underlying physi-

cal and/or networking models and do not represent many of the issues and vagaries of

the radio propagation, interference, random traffic arrivals, queueing and admission

control encountered in real networks. As a result, protocols and algorithms tailored

to these idealized environments do not perform as well as predicted in real wireless

networks.

In this thesis, based on more realistic physical and networking models, we eval-

uate the performance of large-scale WMNs that serve as access networks over large

geographic areas. We create a new set of MAC protocols for the WMNs incorpo-

rating such models. We also develop a large WMN simulator that implements the

protocols and includes measurements-based models for radio propagation and inter-

ference calculation for a large built-in urban area. The simulator also captures the

stochastic network behavior resulting from random traffic arrivals, admission control,

and queueing. The enormous size and computational complexity of the simulator

is addressed using a parallel-processing simulation technique that utilizes multiple

processors interconnected with high-speed links and associated with large high-speed

memory. Through extensive simulations incorporating such details, we determine the

performance of the WMNs. Primary factors are identified across the PHY, MAC


and routing layers of network functions that affect the performance and their intri-

cate interactions are examined to explain the behavior of fundamental performance

metrics including the network throughput, per-session throughput, and blocking and

dropping rates.

1.2 Thesis Outline

This thesis is organized as follows. Chapter 2 describes the WMN studied in this

thesis. The network architecture and the envisioned application scenario are first

described. Then the MAC framework is described and the overall network operations

are explained within the framework. The MAC framework is based on time division

multiple access (TDMA) and time division duplex (TDD) and utilizes two types

of time slots: control and data. Then the WMN simulator created for this thesis

research is described: the radio propagation environment and its simulation models

and methodology are first described; then PHY considerations and the user traffic

model and parameters are presented; and finally the overall simulation routine is

illustrated.

In Chapter 3, we design a protocol through which every wireless mesh router in

the WMN acquires a broadcast time slot that supports a minimum average received

signal-to-interference-plus-noise ratio (SINR) from the mesh router to all of its neigh-

bor mesh routers. The acquired broadcast time slot is used for exchanging control

packets among one-hop neighbor mesh routers and between mesh routers and their

associated end-users. The chapter first explains the basic mechanisms of the protocol

and illustrates the full operations. Selection criteria of the protocol parameters are

then given. Furthermore, a power control scheme is introduced that allows better uti-

lization of resources for maintaining control time slots. Other design considerations

of the protocol including protocol initialization and deadlock resolution are discussed.

Extensive simulation results are presented and discussed. First of all, the protocol is

shown to support the target minimum average received SINR over all neighbor pairs

of mesh routers in all simulated scenarios. In addition, the protocol sensitivities to

the amount of shadowing of the radio propagation and to the topology of gateway


routers are determined. The benefit of the power control scheme is also demonstrated.

In Chapter 4, we develop a protocol that controls the medium access over data

time slots. The protocol utilizes control time slots that are assigned among mesh

routers through the control time slot assignment protocol developed in Chapter 3.

The protocol provides mechanisms for networks entities (mesh routers and end-users)

to negotiate and allocate resources in a fully cooperative and distributed manner.

Furthermore, the protocol supports adaptive resource allocation through dynamic

allocation of data time slots and PHY transmission modes over the slots as well as

through user prioritization. As part of the data time slot access control protocol, we

introduce a new admission and congestion control (ACC) policy that incorporates

the resource availability at the intermediate routers along the path to the destination

router, and yet utilizes only local information available at the admitting router and

has a minimal increase in control overhead.

The chapter first illustrates the resource negotiation mechanisms provided by the

protocol. Key elements of the protocol for dynamic and effective resource allocation

are then discussed including data time slot selection, queue/session prioritization,

data transmission/retransmission, and resource release. The routing protocol em-

ployed by the WMN is then described including its operations and routing metrics.

Two ACC policies (denoted as AC RO and AC RF) are then presented and their

stability properties are analyzed. Finally, extensive simulation results are presented

and discussed. Several fundamental performance metrics are examined including the

network throughput, per-session throughput and blocking and dropping rates. The

impact of the two ACC schemes on the network performance are compared and dis-

cussed. The ACC scheme AC RF is shown to stabilize the network under heavy traffic

loads unlike the policy AC RO.

In Chapter 5, we investigate the performance of the WMN with focus on the

scalability under different scenarios of network topology and routing metric. Specifi-

cally, while keeping the total number of mesh routers constant, we vary the number

and locations of gateway routers deployed in the network and also consider two dif-

ferent routing metrics. We examine the scalability behavior of several fundamental

performance metrics including the network throughput, per-session throughput, and


blocking and dropping rates, and identify major factors that affect the scalability

behavior under the simulated scenarios. We show that the PHY, MAC and routing

layers of network functions interact intricately with one another to determine the net-

work performance. Specifically, we demonstrate that different mesh sizes (i.e., number

of mesh routers served by one gateway router) and different routing paths affect the

tolerable interference level across the network and that they consequently determine

the usage of radio resources, i.e, supportable PHY transmission rates and data time

slots, across the network. Particularly, we show that with more deployed gateway

routers, i.e., more backbone support to the network, the network throughput and

per-session throughput improve significantly, and we explain the improvement based

on the aforementioned interactions across the layers of network functions. The overall

network performance is shown for two different routing metrics.

Chapter 6 summarizes the thesis along with a list of major contributions and

concludes with possible future research directions.

Finally, Appendix A presents a parallel processing technique for time-driven sim-

ulation of large and complex wireless networks with substantial PHY details such as

radio propagation and interference. We identify and demonstrate the issues of the

technique related to the time-driven nature of the simulation and propose schemes for

effective and efficient parallelization over a supercomputing platform which comprises

multiple processors with large high-speed memory and interconnected with high-speed

links. We apply the technique to two different wireless network simulators, a mobile

cellular network simulator and a large WMN simulator, and demonstrate significant

runtime speedup gains for both simulators.

Chapter 2

Simulated Network

2.1 Introduction

In this chapter, we describe the WMN studied in this thesis. We first describe the

network architecture and its envisioned application scenario. We then detail the MAC

framework that is based on TDMA and TDD, and illustrate the overall network

operations within the framework. We then describe the WMN simulator created

for this thesis research: we first explain the radio propagation environment and its

simulation models and methodology; then present PHY considerations and user traffic

statistics; and finally illustrate the overall simulation routine.

2.2 Network Architecture

As explained in Chapter 1, WMNs have a broad spectrum of application scenarios. In

this thesis, as a target application scenario, we consider wireless mesh access networks

deployed over wide geographic areas that provide broadband packet data communi-

cations to users. To support such an application scenario, we consider a layered

network structure shown in Fig. 2.1: one layer of the network structure comprises

wireless mesh routers (MRs), and the lowest layer of the network structure consists

of end users (EUs). A subset of MRs are co-located with gateway routers (GRs) that

are wired to the backbone network. MRs form a wireless mesh and relay user data to

7

CHAPTER 2. SIMULATED NETWORK 8

Figure 2.1: The wireless mesh network (WMN) scenario considered in this thesis:it has 2-layered network structure consisting of mesh routers (MRs) and end users(EUs) and serves as a large access network. A subset of MRs are co-located withgateway routers (GRs) that are wired to the backbone network.

the backbone network through GRs. EUs communicate only with the MRs and not

directly with one another.

2.3 MAC Framework

We consider a MAC framework that supports explicit cooperation among MRs for

resource reservation for data transmission. We consider a TDMA and TDD-based

framework. Although one can extend the MAC framework to employ multiple fre-

quency channels, we assume a single frequency channel in this thesis. MRs and EUs

share the same frequency channel. Fig. 2.2 shows the frame structure of the network.


Time is divided into time slots and there are two types of time slots: one for control

and the other for data. In our TDMA, a MR uses different data time slots for serving

different EUs. However, each data time slot is reused across the network. In TDD, a

MR or an EU cannot transmit and receive simultaneously.

Control time slots provide a random access channel for EUs so that EUs can

discover the network and request admission to the network. Control time slots also

provide a means for MRs to exchange control messages among themselves for such

operations as network discovery, routing table construction, and resource negotiation.

Each MR is associated with one control time slot. In Chapter 3, we develop a protocol

that assigns a control time slot to each of the MRs in the WMN. Through the protocol,

each MR acquires a broadcast control time slot that supports a minimum average

received SINR from the MR to all of its neighbor links. Here, a MR A is defined to

be a neighbor of another MR B if and only if the averaged received signal power at

A from B, PB→A, is at least as high as a threshold, PTH NBR. In other words, the

control time slot assignment protocol guarantees a target average received SINR for

a transmit MR to any other receive MR at which the average received signal power is

at least PTH NBR. Once MRs acquire control time slots, they continuously transmit

on their assigned control time slots afterwards. Time slots for MRs are synchronized

across the network as discussed in Section 3.8.

Each control time slot is partitioned into five subslots: the first control subslot

(tBUSY in Fig. 2.2) is where a MR broadcasts beacon signals and exchanges con-

trol messages with its neighbor MRs. The control time slot assignment protocols

guarantees the target minimum average received SINR over this subslot. The next

three control subslots (i.e., rBUSY, tBUSY/RTS, and NACK in Fig. 2.2) are used

for running the control time slot assignment protocol. The last control subslot (RA)

serves as a random access channel for EUs.

User data packets are transmitted on the first data subslot (DATA TX in Fig. 2.2),

and acknowledgment (ACK) packets on the second data subslot (DATA ACK in

Fig. 2.2). In Chapter 4, we create a protocol for controlling access over data time

slots. Operating in a fully cooperative and distributed manner, the protocol provides

two types of negotiation mechanism among network entities. One is between an


tBUSY rBUSY tBUSY/RTS NACK RA

CTRL SLOTDATA SLOT

... ... ...FRAME 0 FRAME 1

DATA_TXDATA_ACK

Figure 2.2: The network frame structure: time is slotted into control and data timeslots and each time slot comprises multiple subslots. See the text for detailed expla-nations of each of the subslots.

EU and its associated MR, and the other is among one-hop neighbor MRs. These

negotiations are performed over control time slots. The negotiation between an EU

and a MR is performed during the control time slot of the MR, and the negotiation

among one-hop neighbor MRs is done over the control time slots of the involved MRs.

Within this framework, the overall operation of the network is as follows. When

the network is deployed, it enters the pre-operation phase during which the network

runs the control time slot assignment protocol in Chapter 3. Once control time slots

are assigned to MRs, the network starts to run the routing protocol described in

Section 4.7 to construct routing tables at MRs. Once the network is set up, it enters

the normal operation phase during which it executes the data time slot access control

protocol in Chapter 4 to serve EUs. The network needs to re-run the control time slot

assignment protocol if a new MR is added to the network. In addition, routing tables

need to be re-constructed if a MR fails or a new MR is added to the network. The

network could perform such re-configuration of control time slots or routing tables

proactively by entering the pre-operation phase periodically.


2.4 Radio Propagation Environment and Simula-

tion Models

The propagation of radio signals highly depends on the environment, and/or sys-

tem parameters such as channel bandwidth and operating frequency, and/or antenna

configurations at both transmitter and receiver. There are mainly three components

in characterizing and modeling the propagation of radio signals: distance-dependent

path-loss, large-scale fading or shadowing, and small-scale fading or multipath fad-

ing. The distance-dependent path-loss describes the trend of the average received

signal power as a function of the distance between the transmitter and the receiver.

Large-scale fading or shadowing characterizes the fluctuation of the local received sig-

nal power averaged over several wavelengths. These two components jointly describe

the large-scale variation of the average received signal strength. Small-scale fading or

multipath fading, on the other hand, refers to the fluctuation around the local average

received signal strength on the order of a wavelength and results from the replicas of

the transmitted signal that add up at the receiver with random phase shifts. In this

section, we describe the radio propagation environment and its simulation models

considered in this thesis.

2.4.1 Radio Propagation Environment

As illustrated in Fig. 2.3, we consider heavily built-in urban areas where a square grid

of continuous multi-story buildings and streets is laid across a region. For heavily

built-in urban areas, there have been extensive efforts in measuring, characterizing,

and modeling radio propagation. Among them are [17–20]. Major characteristics of

the radio propagation in this type of environment are disparate propagation behavior

along streets of different orientation with respect to the transmitter, e.g., line-of-sight

(LOS) or radial streets, and non line-of-sight (NLOS) streets such as cross streets or

parallel streets; and a significant corner attenuation around a corner at an intersection.

MRs are placed at street corners on a square grid and assumed to be 5 m high.

The separation distance between adjacent mesh routers is one of the key parameters


Figure 2.3: The network topology of the simulated network.

of the network topology, and is determined based on various factors across the layers

of network functions, e.g., a required SINR for a target error performance under

the employed PHY configurations and MAC framework. Under the adopted PHY

configurations described in Section 2.5 and MAC framework in Section 2.3, we choose

100 m for the grid spacing of MRs so that adjacent MRs can support the highest PHY

transmission rate (i.e., 54 Mbps requiring 30 dB SINR in Table 2.3) under light or

moderate co-channel interference in the network.


corner attenuation

transmitter

radial street

cross street

Figure 2.4: Radio signal propagation scenario when the transmitter is at an intersec-tion: the signal propagates along radial or line-of-sight (LOS) streets and along crossstreets.

2.4.2 Radio Propagation Models for Large-Scale Radio Prop-

agation

The simulation models and parameters for the large-scale variation of the radio prop-

agation in this thesis are adopted largely from [17, 19] that considered very similar

environments. Reference [17] developed urban propagation models based on exten-

sive measurements conducted in San Francisco while reference [19] proposed models

based on measurements made in central Stockholm. On the other hand, the small-

scale fading of the radio propagation is not directly simulated in this thesis. Rather,

performance degradation due to the small-scale fading is implicitly incorporated into

the simulator by taking the error performance of the underlying PHY interface ob-

tained in the corresponding fading environment through separate simulations (see

Section 2.5). Table 2.1 summarizes the simulation models and parameters for radio

propagation. Entries in the table are described in the following sections.


Distance-Dependent Path-Loss: When Transmitter is At Intersection

We first consider the case in which the transmitter is located at an intersection.

Fig. 2.4 illustrates the radio propagation scenario when the transmitter is at an inter-

section. Streets are categorized into two types: radial or line-of-sight (LOS) streets,

and cross streets. For radial or LOS streets, a well-known dual-slope or two-ray

exponential model is adopted, also consistent with observations made in [17–19]:

d−n, n =

2, d ≤ df

4, d > df(2.1)

where the breakpoint, df = 4hthr/λ, explicitly takes into account antenna heights

(ht, hr) and the wavelength (λ) at the operating frequency. For example, at 2.4 GHz,

we have df = 240 m for ht = 5 m, hr = 1.5 m and df = 800 m for ht = 5 m and hr =

5 m. Fig. 2.5-(a) shows the received signal power along a LOS street employing the

simulation parameters in Table 2.1.

Along cross streets, a single-slope exponential model is used as seen in Fig. 2.5-(b).

Each curve in Fig. 2.5-(b) shows the received signal power (with path-loss only) along

a cross street designated by a dashed arrow in Fig. 2.4. For example, the uppermost

curve in Fig. 2.5-(b) corresponds to one of the cross streets one block away from the

transmitter in Fig. 2.4. The next uppermost curve in Fig. 2.5-(b) corresponds to one

of the cross streets two blocks away from the transmitter in Fig. 2.4, and so on.

A reference path-loss value for each cross street is calculated at the middle point

of the intersection that the cross street makes with a radial street, and the path-loss

exponent, n, is found as a function of the perpendicular distance, dperp, of the cross

street to the transmitter, as suggested in [17]:

d−n, n = 0.029 (dperp − 60) + 2.5 (2.2)

Here, the distance d is measured from the transmitter to the receiver.

Moreover, when turning around a corner from a radial street to a cross street,

we introduce an attenuation in the average received signal power. In this thesis, the

corner attenuation, offsetcorner, is determined as a function of the perpendicular


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receiver = mesh routerreceiver = end user

(a)

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(b)

Figure 2.5: Simulated received signal power (dBm) when the transmitter is at anintersection, path-loss only, transmitter = MR: (a) along a line-of-sight (LOS) street.Breakpoints are determined by Eqn. (2.1); (b) along cross streets, receiver = MR.Each curve corresponds to a cross street. Slope and corner attenuation are determinedby Eqn. (2.2) and Eqn. (2.3).


actual transmitter

radial street

cross street

virtual transmitter

parallel street

corner attenuation

Figure 2.6: Radio signal propagation scenario when the transmitter is off an intersec-tion: the signal propagates along radial, cross, and parallel streets.

distance, dperp, of the cross street to the transmitter, as suggested in [17]:

offsetcorner(dB) = 0.06 (dperp − 60) + 5 (2.3)

Given these propagation models along a cross street, when we calculate the re-

ceived signal power at an intersection where two cross streets meet, we add the power

components from the two cross streets. As a result, a boost is observed at those

intersections along a cross street as shown in Fig. 2.5-(b).

Distance-Dependent Path-Loss: When Transmitter is Off Intersection

Fig. 2.6 illustrates the radio propagation scenario in which the transmitter is located

off an intersection. Streets are divided into three groups: radial or LOS street, cross

streets, and parallel streets. Here, parallel streets refer to those streets that are

parallel to the LOS street. The first two groups, i.e., radial and cross streets, are

modeled in the same way as in the case in which the transmitter is at an intersection.

For parallel streets, on the other hand, we consider the two cross streets that are

closest to the transmitter and assume that virtual transmitters are placed at the two


intersections which these two closest cross streets make with the radial street, as

indicated by the dashed circles in Fig. 2.6. With respect to the virtual transmitters

at these intersections, the two closest cross streets can be seen as radial streets and

parallel streets can be seen as cross streets. Then, from each virtual transmitter, we

calculate the path-loss along parallel streets in the same way as with cross streets in

the case in which the transmitter is at an intersection. We combine the two path-loss

values derived from the two virtual transmitters to obtain the composite path-loss

values along parallel streets. Fig. 2.7 shows an example of the received signal power

along parallel streets employing the simulation parameters in Table 2.1. Each curve

in Fig. 2.7 shows the received signal power (with path-loss only) along a parallel

street designated by a dashed-dotted arrow in Fig. 2.6. For example, the uppermost

curve in Fig. 2.7 corresponds to one of the parallel streets one block away from the

transmitter in Fig. 2.6. The second uppermost curve in Fig. 2.7 corresponds to one

of the parallel streets two blocks away from the transmitter in Fig. 2.6, and so on.

Distance-Dependent Path-Loss: Reciprocity Consideration

When the transmitter and receiver are stationary and the environment does not

change, the average received signal power at the receiver is the same as the one

when the transmitter and receiver exchange their roles, i.e., when the previous re-

ceiver now transmits and the previous transmitter now receives. Here we assume the

same system configuration for the transmitter and receiver. While the propagation

model for distance-dependent path-loss along LOS streets given in Eqn. (2.1) guar-

antees this reciprocity, the propagation models for cross streets and parallel streets

described above do not guarantee the reciprocity. In this thesis, the reciprocity of

distance-dependent path-loss is guaranteed as follows: we first calculate the path-loss

component for each of the scenarios A → B and B → A for a transmit node A and

a receive node B according to the models explained above, and then take the (linear

power) average of the two.


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transmitter

intersection

Figure 2.7: Simulated received signal power (dBm) along parallel streets when trans-mitter is off an intersection, path-loss only, transmitter = MR (in the middle of ablock), receiver = MR. Each curve corresponds to a parallel street and peaks corre-spond to intersections with cross streets. Slopes and corner attenuation are deter-mined by Eqn. (2.2) and (2.3) with respect to virtual transmitters.

Large-Scale Fading or Shadowing

The fluctuation of the large-scale variation around the distance-dependent path-loss

is well modeled as a zero-mean lognormal random process [17–23]. Both [17, 19]

report 3 to 4 dB of standard deviation for both LOS and NLOS streets. In both

works, the standard deviation did not seem to vary much along different streets.

Thus in this thesis, we take a constant value of 4 dB for the standard deviation of

the lognormal shadow random process along every street. For an autocorrelation

model, we adopt a commonly used first-order autoregressive model proposed in [24],

and the parameter values are inferred from [17,19]; the correlation distance where the

normalized autocorrelation value becomes e−1 is taken to be 10 m which corresponds

to roughly 30λ in [17, 19].


... ...

...

...

map_H

map_V

mesh router street

(a)

......

... ...

map_VH

map_HV

mesh router street

(b)

Figure 2.8: Illustration of four shadow maps maintained for each mesh router: (a)shows two shadow maps, map V and map H, and (b) shows two shadow maps,map VH and map HV.


For each MR, we maintain four types of shadow maps. Each shadow map con-

tains shadow samples generated at every 50 m according to the model and parameters

described above. The maps are generated during the initialization phase of the sim-

ulation as explained in Section 2.7.2, and once generated, the shadow samples of the

maps remain fixed throughout each simulation. Shadow samples in different maps

are generated independently. Fig. 2.8 illustrates the four maps maintained for each

MR: map V, map H, map VH, and map HV. map V contains 2×Nstr intf +1 vertical

streets with the MR at the center of the map. The number of these vertical streets

in map V is chosen such that the received signal from the MR along a vertical street

beyond these streets is insignificant. We take Nstr intf = 5, i.e., we consider up to

5 parallel streets from the transmitter for calculating co-channel interference. This

is based on the observation that the received signal power beyond the five closest

cross streets or parallel streets from the transmitter are insignificant compared to the

receiver thermal noise power level of -98 dBm as seen in Fig. 2.5-(b) and Fig. 2.7,

respectively. Similarly, map H contains 2 × Nstr intf + 1 horizontal streets with the

MR at the center of the map. In addition, map VH contains shadow samples along

horizontal streets that overlap with those vertical streets contained in map V, and

map HV includes vertical streets that overlap with the horizontal streets of map H.

Shadow samples for locations that are contained in more than one shadow map of the

same MR are set to be the same with one another.

The shadow samples, which are generated and stored in the shadow maps, are

then referred to when a shadow sample is generated for a location for which a shadow

sample is not generated and stored in a shadow map. In this thesis, we linearly

interpolate the two closest shadow samples stored in the shadow maps.

Large-Scale Fading or Shadowing: Reciprocity Consideration

The reciprocity of the shadowing component for a pair of MRs, e.g., MR A and MR B,

is guaranteed as follows in this thesis. Consider a shadow map generated for MR A.

Let sA→B denote the shadow sample in that shadow map of MR A that is generated

at the location where MR B is placed. Similarly, let sB→A denote the shadow sample

in a shadow map of MR B that is generated at the location where MR A is placed.


Table 2.1: Simulation models and parameters for radio propagation

Carrier Frequency 2.4 GHz

LOS d−n, n =

2 for d ≤ df

4 for d > df

Path-loss df = 4hthr/λ

NLOS d−n, n = 0.029× (dperp − 60) + 2.5

Corner offset (dB) = 0.06× (dperp − 60) + 5

ShadowingLognormal (σS = 4 dB)

Exponential autocorrelation (dρ= 10 m)

Topology Mesh routers on a square grid, grid side = 100 m

Universe Toroidal universe, 12 km × 12 km

Then, we set sA→B = sB→A. On the other hand, when calculating the shadowing

component for a link between an EU and a MR, we take the shadow value calculated

at the location of the EU in the shadow maps of the MR.

2.4.3 Toroidal Universe and SINR Calculation

As in [25, 26], we simulate a universe wrapped around like a toroid. In a toroidal

universe, network entities of the same type experience a similar level of interference

across the universe for uniform traffic patterns, and thus there is no “edge effect”

and data can be collected from all entities in the simulated universe. In a toroidal

universe, a radio signal propagating out of the universe reappears at the opposite

edge and continues to propagate in the same direction.

Fig. 2.9 illustrates the simulated toroidal universe. The solid-lined square denotes

the original finite universe. Consider a transmitter at the center of the original uni-

verse (the circle within the solid-lined square in Fig. 2.9) and a receiver (the cross

within the solid-lined square in Fig. 2.9) and one interferer (the triangle within the

solid-lined square in Fig. 2.9). When calculating the received signal power at the

receiver, it is calculated as if the universe is finite (i.e., we consider the solid-lined

square only). On the other hand, when calculating the received interference power


transmitter receiver interferer

Figure 2.9: Illustration of the simulated toroidal universe with a transmitter at thecenter. The red rectangle in a solid line denotes the original finite universe, whosereplicas tessellate the infinite universe. The blue rectangle in a dashed line denotesthe universe considered in this thesis for calculating the received interference level atthe receiver.

at the receiver, we consider active network entities transmitting not only in the orig-

inal universe but also in all the replicated universes. That is, we include not only

the original interference in the original universe (the triangle within the solid-lined

square in Fig. 2.9) but also from all the replicas of the transmitter (the circles within

the dashed-lined squares in Fig. 2.9) and those of the interferer (the triangles within

the dashed-lined squares in Fig. 2.9).

The size of the original universe is determined by several factors including prop-

agation conditions (e.g., path-loss exponents), the extent of resource reuse, and the

number of replicas of each network entity considered for calculating interference. The


larger the path-loss exponents, the smaller the resource reuse factors (i.e., a resource

is reused more often), and the larger the number of replicas of each network entity

considered for calculating interference, the smaller the universe size can be [26].

In this thesis, when we calculate the received interference level at a receive node

from all active transmitting nodes, each of the active transmitting nodes in the original

universe is taken into account only once: among the original transmitting node in

the original universe and its replicas in the replicated universe, we include only the

interference from the closest node to the receive node . For example, for the receiver

in Fig. 2.9 (the cross within the solid-lined square), we consider the closest replica (the

triangle within the dashed-lined square around the receiver) of the original interferer.

With this criterion of interference calculation in the toroidal universe, it is critical

to have the original universe size large enough so that all significant interferers in

the toroidal universe are taken into account. This can be ensured when the received

signal power along the edges of the original universe from a transmitter at the center

of the universe becomes negligible or comparable to the receiver thermal noise floor.

For example, the universe size needs to be large enough such that any interference

coming from outside of the dashed-lined square around the receiver in Fig. 2.9 becomes

negligible at the receiver (the cross within the solid-lined square). For the noise floor

of -98 dBm in Table 2.2 and the propagation environment and models in Table 2.1, a

distance of about 10 km is found sufficient for the side length of the original square

universe.

Another factor that affects the universe size is the network topology. To ensure the

continuation of the topology of GRs after wrapping around the universe, the universe

dimension (i.e., the side length) must be a multiple of the horizontal displacement

K (in the number of MRs) and the vertical displacement L between adjacent GRs.

In addition, the numbers of MRs in the universe should be a multiple of K2 + L2.

From these considerations, for a 100 m grid size of MRs, we choose a 12 km x 12 km

square universe. This universe size meets these requirements for all the GR topologies

simulated in later chapters.


Table 2.2: PHY configuration

Noise floor -98 dBm

Transmit power MR = 20 dBm, EU = 15 dBm

Antenna gain (omni-directional) MR = 5 dBi, EU = 0 dBi

Antenna height MR = 5 m, EU = 1.5 m

Table 2.3: PHY transmission data rate vs. average received SINR

SINR (dB) 12 15 20 21 25 28 30

Data Rate (Mbps) 6 12 18 24 36 48 54

2.5 PHY Configurations

We consider a set of modulation and coding schemes similar to those of the IEEE

802.11a/g PHY interface that has multiple PHY modes supporting data rates of 6

to 54 Mbps. The carrier frequency band is assumed to be around 2.4 GHz, and one

frequency channel of about 20 MHz bandwidth is assumed.

As mentioned in Section 2.4.2, the small-scale fading of the radio propagation

is not directly simulated in this thesis. Rather, we obtain the error performance of

the assumed PHY interface due to small-scale fading through separate simulations.

Table 2.3 shows the 7 transmission modes simulated, adopted from [27] for packet

error rate (PER) less than 10−1 for packet lengths of 54 to 512 bytes. Although

the data size transmitted over one data time slot is much larger than 512 bytes

(DATA TX in Fig. 2.2 is 0.95 msec), data are assumed to be transmitted in packets

of a similar size up to 512 bytes. Given the average received SINR calculated in a

simulation step, a packet is declared successfully received if the SINR exceeds the

threshold corresponding to the PHY transmission mode of the packet.

2.6 Traffic Scenario

We consider only outdoor EUs and they arrive uniformly along streets across the

network according to a Poisson process, and once generated, they remain at their


initial locations throughout their lifetime. Each EU generates one uplink session that

is best-effort (i.e., without delay or throughput constraints) web traffic. Each session

generates one page whose size is Pareto distributed with α = 1.7584 and β = 30458

bytes, resulting in the mean size of 70.6 kilobytes. The models and parameters for

the traffic statistics are based on the work in [28].

2.7 WMN Simulator

2.7.1 Overview

A simulator has been created for the WMN described in this chapter. The simula-

tor implements the radio propagation models described in Section 2.4 for a heavily

built-in urban area over a 12 km x 12 km wrapped-around universe. The simulator

also captures the stochastic network behavior resulting from random traffic arrivals,

admission and congestion control, and queueing. The enormous size and computa-

tional complexity of the simulator is addressed using a parallel-processing technique

that utilizes multiple processors interconnected with high-speed inter-processor links

and associated with large high-speed memory. The parallel-processing technique is

presented in Appendix A.

Each data point for the WMN performance presented in this thesis is obtained

from one long simulation run. Each simulation run uses 16 processors simultaneously

and typically takes several days and sometimes more than a week depending on

the simulation scenario. When calculating the final performance results from each

simulation run, we take only those data that are collected after the network reaches a

steady state. As the simulation evolves, we keep track of the number of active network

entities and each of the performance metrics. The network is seen to enter a steady

state when the number of active network entities and each of the performance metrics

start to converge. It is after this point that data are collected for final performance

results.


Pre-synchronize database for parallel processing

Update existing network entities

Introduce new traffic

Post-synchronize database for parallel processing

Initialize database for parallel processingInitialize database for network set-up

Place MRs and GRsSet up shadow maps

Run control time slot assignment protocol

Construct routing tables

Each simulation

time step

Figure 2.10: Overall simulation routine


2.7.2 Simulation Routine

The simulator is time-driven with a time step of 1 msec (the size of one control

or data time slot in Fig. 2.2). Fig. 2.10 illustrates the overall simulation routine.

Each simulation run begins with a set of operations that initializes the simulator for

parallel processing and sets up the network such as placing MRs and GRs, generating

shadow maps for MRs, etc. The simulator then runs the control time slot assignment

protocol in Chapter 3. It either runs the protocol from scratch or loads the results of

the protocol from a data file previously generated and stored for the same simulation

configuration. Once the control time slots are assigned, the simulator then runs

the routing protocol described in Section 4.7 to construct routing tables at MRs.

When these initializations are completed, the simulator repeatedly performs a set of

operations each simulation time step.

Operations performed during each simulation time step can be grouped into four

groups as shown in Fig. 2.10. First, databases are pre-synchronized for parallel pro-

cessing. Then, existing network entities (i.e., EUs and MRs) are updated. Next, new

traffic is introduced to the network. Finally, databases are post-synchronized for par-

allel processing. Operations for synchronization for parallel processing are described

in detail in Appendix A.

Operations that update existing network entities are performed as follows. When

the simulation time step corresponds to a control time slot, each of the MRs that

have been assigned the corresponding control time slot performs the following: 1) it

processes the resource requests made by its neighbor MRs and updates its database

accordingly, 2) it makes its own resource requests to its neighbor MRs, 3) it transmits

to corresponding neighbor MRs the resource requests results and its own new requests,

and 4) it also admits new EUs who requested admission during the current control

time slot. See Section 4.2 for details on the resource negotiation process among one-

hop neighbor MRs and between an EU and its associated MR.

If the simulation time step corresponds to a data time slot, each of the EUs

and MRs that have the corresponding data time slot assigned or reserved for its

data transmission transmits data packets and updates its database accordingly. See

Section 4.5 for details on data transmission and retransmission.


2.8 Chapter Summary

In this chapter, we described the WMN studied in this thesis. We first described the

network architecture and its envisioned application scenario. We then explained the

TDMA/TDD-based MAC framework and the overall network operations within the

framework. We also described the WMN simulator created for this thesis research

including the radio propagation environment and its simulation models and method-

ology, PHY considerations, and user traffic statistics. We also illustrated the overall

simulation flow.

Chapter 3

Control Time Slot Assignment

Protocol

3.1 Introduction

As described in Section 2.3, the WMN considered in this thesis utilizes control time

slots for various control operations. Assigning broadcast time slots to network entities

in a multi-hop wireless network has been studied in TDMA/FDMA-based multi-hop

wireless networks [14, 15, 29–32]. Reference [14] develops a medium access control

protocol for stationary ad-hoc networks within a time-slotted framework. Time is

divided into time slots and time slots are grouped into frames. Each time slot consists

of several control subslots (or mini-slots) and one data subslot. The protocol allows

nodes to contend for and reserve time slots through handshakes over control subslots

based on a collision-avoidance approach, and once a time slot is reserved, data packets

are exchanged over its data subslot free from collision. Another broadcast time slot

assignment protocol is created in [15] for mobile ad-hoc networks within a TDMA-

based framework. Time is divided into time slots and time slots are grouped into

frames. There are two types of frames: control frame and information frame. A

control frame is followed by a number of information frames. Similar to [14], the

protocol employs contention-based five-phase handshakes over control slots within a

control frame to reserve data slots within information frames.

29

CHAPTER 3. CONTROL TIME SLOT ASSIGNMENT PROTOCOL 30

Scheduling of broadcast time slots within a TDMA framework is also studied

in [29–31]. Reference [30] proposes distributed scheduling algorithms that utilize a

control channel that operates on a contention-based approach, similar to [14, 15].

Reference [31] shows that the problem of assigning broadcast time slots among nodes

for multi-hop wireless networks is NP-complete and proposes a distributed algorithm.

On the other hand, reference [32] provides a unified algorithm for assigning channels

(time, frequency, or code for a TDMA/FDMA/CDMA framework, respectively) to

network nodes or links between network nodes. Based on a directed graph model for

multi-hop networks, the author identifies atomic constrains that characterize such an

assignment problem and provides a unified algorithm to the problem.

In these studies, the underlying physical-layer behavior and the interactions among

network entities have been often oversimplified by not representing the vagaries of

radio propagation and interference encountered in actual networks. For example,

it is assumed that two nodes either perfectly communicate with each other or do

not interact with each other at all. In reality, however, even when two nodes cannot

communicate with each other, a node may still interfere with another node depending

on the propagation condition between the two nodes. Moreover, interference from

many interfering nodes may add up to a level significant enough to block any successful

packet reception at a node. Due to these different assumptions on models for radio

propagation and interference calculation, these protocols are not directly applicable

to the network considered in this thesis.

In this chapter, we design a protocol through which every MR in the WMN

acquires one broadcast time slot that supports a minimum average received SINR

from the MR to all of its neighbor MRs. Neighborhood is defined in Section 3.2.2.

The acquired broadcast time slot is used for exchanging control packets among one-

hop neighbor MRs and between MRs and their associated EUs. The protocol is based

on contention-based reservation mechanisms in a topology-dependent manner similar

to those in [14, 15], yet incorporating more realistic, measurement-based models for

the underlying physical-layer characteristics and behavior described in Section 2.4.

The protocol operates in a fully distributed manner.

As described in Section 2.3, once control time slots are assigned to MRs, the


network starts to run the routing protocol described in Section 4.7 to construct routing

tables at MRs. Once the network is set up, it enters the normal operation phase during

which it executes the data time slot access control protocol in Chapter 4 to serve EUs.

As described in Chapter 4, the data time slot access control protocol utilizes control

time slots for resource negotiation and allocation among MRs and EUs.

This chapter is organized as follows. It first explains the basic mechanisms of the

protocol and delineates the full operations. Selection criteria of the protocol param-

eters are also given. Furthermore, a power control scheme is introduced that allows

better utilization of resources for maintaining control time slots. Other design consid-

erations of the protocol are discussed including protocol initialization and deadlock

resolution. Extensive simulation results are then presented and discussed: the proto-

col is shown to support the target minimum average received SINR over all neighbor

pairs of MRs in all simulated scenarios. In addition, the sensitivities of the protocol

to the amount of shadowing of the radio propagation and to the GR topology are

determined. The benefit of the power control scheme is also demonstrated.

3.2 Protocol Overview

We consider a TDMA and TDD based framework as described in Section 2.3. The

frame structure is repeated in Fig. 3.1. Through the protocol, each MR in the network

becomes associated with one broadcast control time slot. The protocol guarantees a

target average received SINR over each control time slot (over tBUSY in Fig. 3.1)

between a MR associated with the control time slot and each of its one-hop neighbor

MRs so that control packets exchanged between the MR and its neighbor MRs are

received successfully in the presence of co-channel interferers. The protocol incorpo-

rates measurement-based models for radio propagation and interference calculation

described in Section 2.4.



CTRL SLOTDATA SLOT

... ... ...FRAME 0 FRAME 1

DATA_TXDATA_ACK

Figure 3.1: The network frame structure: time is slotted into control and data timeslots and each time slot comprises multiple subslots. See Section 3.3 for detailedexplanation of each of the control subslots.

3.2.1 Basic Mechanisms

MRs acquire a control time slot through contention, and once contention is successful,

they access the reserved slot exclusively. The protocol employs two basic mechanisms:

busy tones and negative acknowledgment. A busy tone is transmitted to inform other

nodes of the presence of an active communication link occurring during a specific

time slot and discourages other nodes from contending for the same time slot. There

are two types of busy tones: transmit busy tones (tBUSYs) and receive busy tones

(rBUSYs). For an active transmission over a specific time period over a link, the

transmit node of the active transmission may transmit a tBUSY packet to inform

other nodes of its active transmission over that specific time period. On the other

hand, the receive node of the link may transmit an rBUSY packet (over a different

time period when the active transmission is not occurring) to inform other nodes of

its active reception over that specific time period.

Utilizing rBUSY packets can help mitigate the hidden node problem as in [14,

15, 33]. The hidden node problem refers to the situation in which a node that does

not know of an active reception at a nearby node transmits a packet assuming the

wireless medium is available, and has its packet collide with the packets being received

at the nearby node. However, the hidden node problem cannot be completely solved

with rBUSY packets alone as illustrated in Section 3.6, and the protocol employs an

additional mechanism to mitigate the problem.


Another basic mechanism employed is negative acknowledgment (NACK) [14,15]

which is a notification of an unsuccessful transmission. For a broadcast transmission

for which more than one receive node can exist, it is not straightforward to arrange

or schedule each of the receive nodes to transmit positive acknowledgment packets

(ACKs), particularly when the number and topology of receive nodes are not known

a priori at the transmit node. We thus choose to utilize NACK packets. As explained

in Section 3.3.4, a receive node transmits a NACK packet when it measures a received

radio power level exceeding a threshold but cannot decode the received signal. It is

possible that there are more than one receive node that transmit a NACK packet for

the same transmission. In this case, the transmit node does not need to decode every

NACK message that it detects but only has to know whether there was any NACK

packet sent to it. Thus, to learn about a transmission failure, the transmit node

simply measures the received radio power level over a time period where a NACK

message may be received.

3.2.2 Notation and Terminology

We denote the average received signal power and SINR from node T to node R during

subslot SS as PT→R,SS and SINRT→R,SS, respectively, and the total power, i.e., signal

plus interference, as Pr,SS.

In the protocol, MR A is defined to be a neighbor MR of MR B if and only if:

PB→A ≥ PTH NBR. (3.1)

PTH NBR determines the neighborhood topology and its selection criteria are discussed

in Section 3.5.1. The average received signal power, PB→A, is determined by the large-

scale variation of radio propagation. It is constant for a fixed pair of transmit and

receive locations when the system configurations and the surrounding environment

remain the same as discussed in Section 2.4.2. This property is called the reciprocity

of the radio propagation. Therefore, the notion of neighborhood based on the average

received signal strength is symmetric when the transmit power level and other system

configurations and the surrounding environment remain the same: if MR A is a


neighbor of MR B, then MR B is also a neighbor of MR A.

3.3 Operations

In this section, we describe the protocol operations performed over each of the control

subslots in each of the time slots shown in Fig. 3.1.

3.3.1 tBUSY

Recall that over this subslot, MRs exchange control messages during the normal

network operation phase. The protocol ensures this subslot to be conflict-free, i.e., a

minimum average received SINR is supported on every link for a neighbor MR pair

active on the subslot. During this subslot, MRs that have acquired the corresponding

control time slot transmit tBUSY packets. On the other hand, contending MRs that

have not yet acquired a control time slot listen and measure received power level over

this subslot. A contending MR can contend for this control time slot only if:

Pr,tBUSY < PTH T (3.2)

That is, a contending MR must have its received power level over tBUSY below a

threshold for it to be eligible to contend for the corresponding control time slot. See

Section 3.5.3 for discussions on the effect of PTH T on the protocol performance and

its selection criteria.

3.3.2 rBUSY

During this subslot, a MR transmits an rBUSY packet if it has “discovered” the

network (see Section 3.8 for the network discovery process) and received a successful

transmission from one and only one of its neighbor MRs during the preceding subslot

tBUSY. In other words, a MR R transmits an rBUSY packet if it has a neighbor MR


T such that:

PT→R,tBUSY ≥ PTH NBR (3.3)

SINRT→R,tBUSY ≥ SINRTH (3.4)

PI→R,tBUSY < PTH NBR for all I 6= T. (3.5)

Here, SINRTH is the target minimum average received SINR that the protocol guar-

antees. See Section 3.5.2 for its selection criteria. Condition (3.4) can be checked

from whether the received message is successfully decoded or not because a tBUSY

packet is assumed to require SINRTH for successful decoding. If the received tBUSY

packet is successfully decoded (i.e., condition (3.4) holds true), then condition (3.3)

can be checked by extracting the received signal power PT→R,tBUSY from the aggre-

gate received power Pr,tBUSY . Condition (3.5), on the other hand, may not be strictly

checked even when Eqn. (3.3) and (3.4) hold true because the interference power from

each interfering node may not be extracted from the composite interference power due

to insufficient “SINR” for that interfering node to be successfully decoded and ex-

tracted. As a sufficient condition, instead, the protocol checks whether the composite

interference power satisfies Pr,tBUSY − PT→R,tBUSY < PTH NBR when conditions (3.3)

and (3.4) hold true.

Contending MRs, on the other hand, listen to the rBUSY packets and measure

the received power level. A contending MR is allowed to contend for this control time

slot only if:

Pr,rBUSY < PTH R (3.6)

That is, a contending MR must have its received power level over rBUSY below a

threshold for it to be eligible to contend for the corresponding control time slot. See

Section 3.5.4 for discussions on the effect of PTH R on the protocol performance and

its selection criteria.


3.3.3 tBUSY/RTS

During this subslot, two groups of MRs transmit. A MR transmits a tBUSY packet

if it transmitted a tBUSY packet during subslot tBUSY within the same control time

slot. Also, a MR transmits a request packet, ready-to-send (RTS), if it i) has not yet

acquired a control time slot, ii) finds the current time slot to be the least numbered

slot that satisfies conditions (3.2) and (3.6) (see Section 3.4 for slot selection strategy),

and iii) its backoff counter has decremented to zero. The simultaneous transmission

by both groups of MRs ensures that a new request for a control time slot is approved

based on the existing interference from all active transmissions over the same control

time slot.

3.3.4 NACK

During this subslot, a MR transmits a NACK message if it does not meet condi-

tions (3.3) - (3.5) during subslot tBUSY/RTS for a neighbor MR. Specifically, a MR

R transmit a NACK message if:

SINRT→R,tBUSY/RTS < SINRTH (3.7)

Pr,tBUSY/RTS > (1 +1

SINRTH

) PTH NBR (3.8)

are satisfied, or

PT→R,tBUSY/RTS ≥ PTH NBR (3.9)

SINRT→R,tBUSY/RTS ≥ SINRTH (3.10)

Pr,tBUSY/RTS − PT→R,tBUSY/RTS ≥ PTH NBR (3.11)

are met. Here, T is the strongest transmit MR received at R.

To interpret the above conditions, consider an example in which two MRs A and

B transmit during subslot tBUSY/RTS and a MR R receives them with an average

received signal power of PA and PB respectively. For the simplicity of illustration, we

ignore the receiver noise power N . Ideally, MR R has to transmit a NACK packet if


PA

PB

PTH_NBR

PTH_NBR R2

R1

R3

R1

R1

PB = (1 / SINRTH) x PA

PB = SINRTH x PAR2

Figure 3.2: Example of conditions for transmitting NACK packets for two transmitMRs A and B that are received at a MR with average received power PA and PBrespectively.

and only if (PA, PB) falls within the region R1 ∪ R2 in Fig. 3.2. However, a receive

MR cannot extract region R3 from R1 ∪ R3 because the best SINR from either MR

A or B is lower than the decodable threshold SINRTH . Thus, R1 ∪R3 is examined

together in conditions (3.7) - (3.8). In Section 3.10.5, we compare the simulation

results with and without the region R3 included in the conditions for transmitting

NACK packets. Cases corresponding to the region R2, on the other hand, can be

easily verified. The packet with the best SINR is decoded successfully as the SINR

exceeds the decoding threshold SINRTH . Then, the received signal power from the

weaker transmit node can be determined by extracting the stronger signal power from

the total received power, and can be checked whether it exceeds PTH NBR or not.

There are three possible scenarios in which the conditions for transmitting NACK

packets are satisfied at a MR.

1) The first scenario is that in which multiple RTS packets collide at a MR, which

then transmits a NACK message. Any contending MR backs off if:

Pr,NACK ≥ PTH NBR (3.12)


We use a uniform random backoff scheme with a fixed-sized window. A contending

MR gives up on a particular time slot if it fails to acquire the slot after a maximum

number of trials. A window size of 8 and a maximum number of trials of 3 were

found effective in all simulated scenarios. It is possible that two close MRs transmit

RTS packets simultaneously and no other MR is present close enough to detect the

collision. We discuss how to resolve this deadlock in Section 3.9.

2) A NACK packet is also transmitted when an RTS packet is not received suc-

cessfully due to interference from other active links on the same control time slot.

Although a contending MR finds the slot to satisfy condition (3.2), the actual inter-

ference from other active links received at one of its neighbor MRs may be too high

to support the minimum SINR on the requested link over the time slot. In this case,

those active interfering MRs are “hidden” at this contending MR.

3) The third scenario is the case in which an active link is degraded due to accumu-

lated co-channel interference from other active links on the same slot. For example,

consider two contending MRs both of which find a particular time slot to satisfy con-

ditions (3.2) and (3.6). Assume that the two contending MRs are placed far apart

from each other and successfully acquire the same slot. The additional interference

from these successful MRs now may cause some other active links to degrade, espe-

cially those links with the supported SINR near the minimum level SINRTH . When

the degradation is detected at a MR, the MR stops transmitting rBUSY packets over

subslot rBUSY and transmit NACK packets within the same control time slot. Upon

hearing such a NACK packet, the transmit MR of the degraded link learns about the

degradation at one of its neighbor MRs and releases its acquired control time slot.

Then, it repeats the acquisition process to acquire another control time slot.

3.4 Time Slot Selection Strategy

As a MR contends for a control time slot, it may find more than one slot feasible

for which to contend. The protocol thus needs to employ a slot selection strategy.

Assigning a broadcast time slot to a node in a wireless network can be converted

to vertex coloring in graph theory [31]. It is well-known that the problem of vertex


coloring with the minimal number of colors is NP-complete [34], and many heuristic

algorithms have been proposed [15,31,32,35]. Among them is a greedy algorithm [32,

35]. In the scheme, colors are numbered from 0 to n−1, where n is the total number of

colors, and each node is colored with the least numbered color that does not violate

coloring constraints. In the protocol developed in this section, we have adopted a

greedy approach in a fully distributed manner: each MR scans control time slots

within a frame sequentially and contends for the first time slot that satisfies the

criteria of the protocol.

3.5 Parameter Selection

In this section, the selection criteria of the four protocol parameters are discussed.

3.5.1 PTH NBR

PTH NBR is the minimum average received signal power for a MR to be a neighbor

of another MR, and determines the neighborhood topology of MRs. Because the

average received signal power for a pair of transmit and receive locations depends

on the propagation environment, antenna configurations, transmit power, operating

frequency, etc., PTH NBR should be selected taking such factors into account. Several

selection criteria may be considered for PTH NBR. The protocol employs the follow-

ing criterion: at least 99.9 % of the MRs in the network have at least 2 neighbors

MRs within 400 m for the network topology and the radio propagation environment

described in Section 2.4 and Table 2.1. One can choose a different number than

99.9% for the fraction of MRs that have at least two neighbor MRs within a certain,

reasonable range in distance. In this thesis, 99.9% is chosen so that “essentially all”

MRs satisfy the neighborhood condition. Table 3.1 shows the probability for a MR

to have at least 2 neighbor MRs within 400 m for a range of standard deviation (σ)

values of the log-normal shadowing process under the simulated scenario. We choose

the maximum value of PTH NBR that satisfies the criterion: PTH NBR = -50 dBm for

σ = 4 dB and 8 dB, and -46 dBm for σ = 0 dB. As shown in the scatter plot of the


average received signal power versus the separation distance of neighbor MR pairs in

Fig. 3.3, the separation distance of a neighbor MR pair ranges widely for PTH NBR =

-50 dBm and σS = 4 dB, and the average received signal power level varies greatly

at a given separation distance due to shadowing.

Table 3.1: Probability for a MR to have at least 2 neighbor MRs within 400 metersunder the simulated network topology and radio propagation environment.

PTH NBR (dBm) σ = 0 dB σ = 4 dB σ = 8 dB

-45 0 0.7497 0.9254

-46 1.0000 0.8795 0.9606

-47 1.0000 0.9548 0.9813

-48 1.0000 0.9874 0.9921

-49 1.0000 0.9975 0.9970

-50 1.0000 1.0000 0.9990

-51 1.0000 1.0000 0.9997

-52 1.0000 1.0000 0.9999

-53 1.0000 1.0000 1.0000

3.5.2 SINRTH

SINRTH is the minimum average received SINR for a control packet to be decoded

successfully. In the thesis, we consider PHY configurations similar to those of the

IEEE 802.11a/g PHY standards [13] as mentioned in Section 2.5, and particularly,

we assume the lowest-rate PHY transmission mode for transmitting control packets.

Simulation results in [27] showed that 12 dB is a reasonable SNR value for packet error

rate (PER) of less than 10−1 for packet lengths of 54 - 512 bytes in the lowest-rate

PHY transmission mode. Thus, we choose SINRTH = 12 dB for the protocol. See

Table 2.2 for the PHY transmission modes and their corresponding threshold SINR

values adopted in this thesis.


0 100 200 300 400 500 600 700 800−52

−50

−48

−46

−44

−42

−40

−38

−36

−34

−32

separation (m)

Aver

age

rece

ived

pow

er (

dB

m)

Separation vs. average received power between two neighbors

Figure 3.3: Scatter plot of average received signal power vs. separation for a pair ofneighbor MRs, PTH NBR = -50 dBm and σS = 4 dB

3.5.3 PTH T

PTH T in Eqn. (3.2) is the maximum total radio power received at a contending MR

over subslot tBUSY in order for the MR to contend for the corresponding control

time slot. PTH T needs to be low enough so that the target minimum SINR level can

be supported not only for the new links between this contending MR and its neighbor

MRs but also for existing links even after this contending MR successfully acquires

this control time slot. PTH T can be chosen as follows:

PTH T =PTH NBR

αT × SINRTH

(3.13)

where αT ≥ 1 is a margin that incorporates the discrepancy between the co-channel

interference that the contending MR perceives and the actual level at the neighbor

MRs of the contending MR. Note that for a smaller value of PTH T or equivalently

for a larger value of αT , the contention among MRs for a specific slot becomes more


conservative, i.e., fewer MRs contend for the same control time slot. See Section 3.10.1

in which different values of αT are considered to determine its effect on the overall

protocol performance and its selection criterion is discussed.

3.5.4 PTH R

PTH R in Eqn. (3.6) is the maximum total radio power received at a contending MR

over subslot rBUSY in order for the MR to contend for the corresponding control time

slot. Similar to PTH T , PTH R needs to be low enough so that the target minimum

SINR level can be supported not only for the new links between this contending MR

and its neighbor MRs but also for existing links. PTH R can be chosen as follows:

PTH R =PTH NBR

αR × SINRTH

(3.14)

where αR ≥ 1. See Section 3.10.1 in which different values of αR are considered to

determine its effect on the overall protocol performance and its selection criterion is

discussed.

3.6 Hidden Node Problem Revisited

We now show that when an active link has co-channel interferers, a contending MR

can degrade the link even with a large αR in Eqn. (3.14). For the simplicity of

illustration, we ignore the receiver noise power N . Consider an example of a transmit

MR T transmitting to a receive MR R and a contending MR A. Let

SIRT→R,old =PT→R∑I∈SI

PI→R

≡ β × SINRTH β ≥ 1 (3.15)

PT→R ≡ γ × PTH NBR γ ≥ 1 (3.16)

PA→R ≡ PTH NBR

α× SINRTH

α ≥ 1 (3.17)


11/αR γ

1/β + 1/(αRγ) = 1R3

β

1

αR /(αR-1)

R1

R2

Figure 3.4: Illustration of the condition in Eqn. (3.18) for an active link to becomedegraded due to a new contending MR.

where SIRT→R,old is the average received SIR of the active link T → R during subslot

tBUSY before the contending MR A transmits, SI is the set of co-channel interferers

that transmit tBUSY packets over the same control time slot, PT→R is the average

received signal power at MR R from MR T , PA→R is the average received signal power

at MR R from the contending MR A. Then,

SIRT→R,new =PT→R

PA→R +∑I∈SI

PI→R

=1

β−1 + (α× γ)−1× SINRTH

The active link T → R does not get degraded by the contending MR A if and only if:

β−1 + (α× γ)−1 ≤ 1 (3.18)

In other words, an active link can be degraded by a new link if the associated time

slot is contended for by a MR A that satisfies αR < α < β/((β − 1)γ) where α, β,

and γ are as defined in Eqn. (3.17), (3.15) and (3.16), respectively.

Fig. 3.4 illustrates different regions of (γ, β) with respect to the boundary in


Eqn. (3.18) for α = αR. If (γ, β) ∈ R1 ∪ R2, the active link is not degraded by

the contending MR A with α > αR. That is, (γ, β) stays in R1 ∪ R2 even after

the contending MR succeeds in acquiring the control time slot. On the other hand, if

(γ, β) ∈ R3, the active link may become degraded by the contending MR A depending

on the value of α of the contending MR. That is, (γ, β) may fall in R3 due to the

transmission by the contending MR. This potential degradation of active links by

new links implies that the hidden node terminal problem cannot be resolved by using

rBUSY packets alone. The protocol provides a mechanism to detect and resolve

such degradation of active links by utilizing NACK packets as described by the third

scenario of transmitting NACK packets in Section 3.3.4.

3.7 Power Control of Receive Busy Tones

So far, we have assumed a constant and known transmit power level Ptx max for rBUSY

packets as well as other transmissions on control subslots. The transmit power level

of rBUSY packets can be adaptively controlled in order to reduce the number of

required control time slots while still meeting the protocol goal.

Consider an active receive MR R and a contending MR A. Let MR R transmit a

rBUSY packet at Ptx ≡ Ptx max/δR, δR ≥ 1. Let tBUSY packets and RTS packets be

transmitted at Ptx max as before. Assume that conditions (3.2) and (3.6) are met at

the contending MR A. Then, we have

Pr,rBUSY = PR→A(Ptx max/δR) +∑I∈SI

PI→A(Ptx max/δI) +N < PTH R

where Pr,rBUSY is the total average received radio power at the contending MR A

during subslot rBUSY, PR→A(Ptx max/δR) is the average received signal power from

MR R at the contending MR A with the transmit power level of Ptx max/δR, SI is the

set of MRs excluding MR R that transmit rBUSY packets over the same control time

slot, and PI→A(Ptx max/δI) is the average receive signal power from MR I at MR A


with the transmit power level of Ptx max/δI . We then have

PA→R(Ptx max) < δR × PTH R (3.19)

PA→I(Ptx max) < δI × PTH R for all I ∈ SI (3.20)

where PA→R(Ptx max) is the received signal power level at MR R from MR A with the

transmit power level of Ptx max and PI→R(Ptx max) is that from MR I ∈ SI .By substituting Eqn. (3.17) and Eqn. (3.14) to Eqn. (3.19) gives:

α >αRδR, δR ≥ 1 (3.21)

That is, a contending MR A with α < αR or equivalently PA→R > PTH R may now

contend for this control time slot, leading to more aggressive contention on the slot

and ultimately allowing better utilization of control time slots across the network.

We choose δR for the active receive MR R as follows:

δR = max (1, αRβ − 1

βγ) (3.22)

where αR, β, and γ are as defined in Eqn. (3.14), (3.15) and (3.16), respectively. Note

that δR in Eqn. (3.22) has the following property: if the active link satisfies Eqn. (3.18)

for α = αR (i.e., (γ, β) ∈ R1 ∪ R2 in Fig. 3.4), the link is not degraded (i.e., (γ, β)

stays in R1 ∪ R2 in Fig. 3.4) when the receive MR R of the link employs the above

power control policy and the contending MR A follows the original contention rules,

i.e., Eqn. (3.2) and Eqn. (3.6). On the other hand, if the active link does not satisfy

Eqn. (3.18) for α = αR (i.e., (γ, β) ∈ R3 in Fig. 3.4), δR in Eqn. (3.22) becomes 1

and the active link is thus not affected by the power control policy.

δR in Eqn. (3.22) can be interpreted as the margin in γ with respect to the value on

the boundary of Eqn. (3.18) for given β when (γ, β) satisfies Eqn. (3.18) for α = αR.

That is, δR corresponds to the horizontal distance between the value of γ in R1∪R2

in Fig. 3.4 and the corresponding value of γ on the boundary of R1 ∪ R2 in Fig. 3.4

for the same β. The power control scheme exploits the margin in the received signal

power over the neighbor link T → R with respect to PTH NBR without degrading


the link. We examine the protocol performance with the power control scheme in

Section 3.10.3.

3.8 Protocol Initialization

To support operations within the TDMA and TDD framework described in Sec-

tion 2.3, the WMN needs to be synchronized across the network. The network-wide

synchronization is achieved with the aid of the backbone network. More specifically,

MRs co-located with GRs are synchronized to GRs which in turn are synchronized

to the backbone network, and these MRs initiate the protocol. MRs not co-located

with GRs, on the other hand, first listen to control messages transmitted by those

MRs co-located with GRs in order to discover the network. A MR R discovers the

network if it identifies a MR T such that:

PT→R,tBUSY ≥ PTH NBR (3.23)

SINRT→R,tBUSY ≥ SINRTH (3.24)

In other words, a MR discovers the network by receiving and decoding a control

packet transmitted by one of its neighbor MRs that have already acquired a control

time slot.

The aforementioned sequential initialization procedure is effective when the den-

sity of GRs is sparse enough. In the cases of (#MRs):(#GRs) = 80:1, 40:1, 20:1,

10:1, and 5:1 studied in Section 3.10.4 and in Section 5.2, the sequential initialization

procedure is very effective in all simulated scenarios. In the case of a sparse GR topol-

ogy, it is rare but possible that before a MR discovers the network, all of its neighbor

MRs acquire a control time slot, but yet none of them supports the minimum SINR

to the MR. For example, in all simulations under (#MRs):(#GRs) = 40:1, we have

observed one instance where one MR out of 14400 MRs could not discover the net-

work by Eqn. (3.23) and (3.24). In that case, we let such a MR transmit a NACK

message over subslot NACK of a control time slot over which the MR receives a total

power level larger than PTH NBR in Eqn. (3.23). Then, the neighbor MR on that


control time slot releases the slot upon hearing the NACK message and re-contends

for another. Here, we assume that after listening to non-decodable messages with the

received power level in Eqn. (3.23) for an extended period of time, the MR can get

synchronized to the beginning of those slots, i.e., beginning of subslot tBUSY.

In the case of (#MRs):(#GRs) = 2:1 in which every other MR is co-located with a

GR the sequential initialization procedure is not effective. According to the sequential

initialization procedure, every other MR that is co-located with a GR is initialized to

transmit a tBUSY packet on subslot tBUSY of the first control time slot all at the

same time. In that case, the received interference level on the first control time slot

at a MR that is not co-located with a GR is too high so that the MR cannot find a

neighbor MR for which Eqn. (3.24) holds true.

Two approaches are possible to break this initialization deadlock. One is to have

each of those MRs that are not co-located with a GR transmit a NACK packet ac-

cording to the procedure described above for the case of a sparse GR topology and

have MRs co-located with a GR release the first control time slot upon hearing the

NACK packet. Another approach is to modify the sequential initialization procedure

as follows: we have MRs co-located with a GR initiate the protocol not simultaneously

but at different instants of time. For example, a random start time can be introduced

when a MR co-located with a GR initiates the protocol. Because the sequential ini-

tialization is found effective in the case of (#MRs):(#GRs) = 5:1 under the simulated

radio propagation environment and network configurations, a random start time cho-

sen over 5 or more possibilities would be effective. In the latter approach, it still

would be necessary to transmit NACK packets to resolve any remaining initialization

deadlocks seen in the case of a sparse GR topology.

In the case of (#MRs):(#GRs) = 1:1 in which every MR is co-located with a

GR (i.e., the WMN is not a multi-hop network any more but a traditional single-hop

network), a similar consideration is made. Because now all MRs are simultaneously

initialized to transmit a tBUSY packet on subslot tBUSY of the first control time

slot, there is no other MR that can NACK and break the deadlock. In this case, as

with the case of (#MRs):(#GRs) = 2:1, it would be effective to introduce different

start times, e.g., random start times, for MRs co-located with GRs to initiate the


protocol along with NACK packets.

For the simulation results studied later in Section 5.2 for the cases of (#MRs):(#GRs)

= 2:1 and 1:1, we adopt the control time slot assignment result for the case of

(#MRs):(#GRs) = 5:1. As demonstrated in Section 3.10.4, the effect of the GR

topology is negligible on the overall protocol performance. Thus, adopting the con-

trol time slot assignment result from another GR topology would not affect the overall

WMN performance for serving EUs. However, in real networks, the control time slot

assignment result for a different GR topology may not be available and thus, one

of the approaches for breaking an initialization deadlock mentioned described above

would be necessary.

3.9 Deadlock Resolution

As mentioned in Section 3.3.4, when two neighbor MRs transmit RTS packets at the

same time with no other MR to detect the collision, both of them acquire the same

slot and form a deadlock. In the simulations, we had such cases very rarely, e.g., a few

out of more than 45000 pairs of neighbor MRs in the case of (#MRs):(#GRs) = 40:1.

Such deadlock can be resolved as follows: after all MRs acquire a slot, they exchange

neighbor information, and construct routing tables. Then, the GRs sequentially,

e.g., the one with the lowest ID first, the one with the next lowest ID second, etc.,

command each MR in their database to stop transmitting on its control time slot

and detect any deadlock on the slot. If there is any, the MR releases the slot and

contends for another until it successfully acquires another one. In the simulations,

we examine MRs sequentially after all of them acquire a control time slot. If there

is any deadlock formed for a pair of MRs, we let the one with a lower ID release the

slot, and re-contend for another.

3.10 Simulation Results

In this section, we present the protocol performance under various simulation scenar-

ios. We first give the baseline performance under different contention levels among


contenting MRs. We then present the protocol performance sensitivity to different

levels of severity of the log-normal shadowing. In addition, the protocol performance

employing the power control policy of receive busy tones is demonstrated. Finally,

we vary the topology of GRs and discuss its effect on the protocol performance.

3.10.1 Baseline Performance

We present the protocol performance for the reference scenario of σS = 4 dB without

the power control scheme employed. Fig. 3.5-(a) shows the number of MRs that

have acquired each control time slot and Fig. 3.5-(b) presents the resultant SINR

distribution over links between a pair of neighbor MRs for different values of αR. In

all cases, we set αT = αR. Fig. 3.5-(b) confirms that the protocol indeed guarantees

the minimum target SINR of 12 dB in all scenarios. Moreover, the slot utilization

in Fig. 3.5-(a) is consistent with the slot selection strategy described in Section 3.4:

a control time slot with a lower index is generally reused more than one with a

higher slot index. Note that slot utilization drops quickly once it starts to decrease

noticeably toward the highest index, and the majority of the time slots become reused

to a similar extent. As αR increases, the number of control time slots used increases

and the average received SINR improves. This is because a larger value of αT and

αR translates to tighter contention rules in Eqn. (3.2) and Eqn. (3.6) as discussed in

Section 3.5.3 and Section 3.5.4, respectively. For 1 dB increase in αR = αT , we have

2 or 3 more time slots required and about 1 dB higher average SINR. We choose αR

= αT = 1 dB for subsequent simulations as the value requires the fewest control time

slots (and thus least system overhead) among the values considered in this section.

3.10.2 Effect of Shadowing

Fig. 3.6 presents the protocol performance for different values of σS where σS denotes

the standard deviation of the log-normal shadowing. Clearly, for a larger σS, more

control time slots are required. Moreover, the resultant SINR values are higher on

average and spread more. Recall that the neighborhood of MRs in this thesis is defined

based on the average received signal power as explained in Section 3.2.2. Thus, for a


0 20 40 60 80 1000

50

100

150

200

250

300

CONTROL SLOT INDEX

NU

MB

ER

OF

ME

SH

RO

UT

ER

S

SLOT DISTRIBUTION AMONG MESH ROUTERS

α

R = 1 dB

αR

= 2 dB

αR

= 3 dB

(a)

0 10 20 30 40 500

0.01

0.02

0.03

0.04

0.05

0.06SINR DISTRIBUTION OF NEIGHBOR LINKS

SINR (dB)

FR

AC

TIO

N O

F L

INK

S

α

R = 1 dB

αR

= 2 dB

αR

= 3 dB

(b)

Figure 3.5: (a) The number of mesh routers that acquired a specific control time slotvs. control time slot index and (b) the distribution of SINR over links between twoneighbor mesh routers, both for different values of αR. In all cases, we set αT = αR.


0 20 40 60 80 100 1200

100

200

300

400

500SLOT DISTRIBUTION AMONG MESH ROUTERS

CONTROL SLOT INDEX

NU

MB

ER

OF

ME

SH

RO

UT

ER

S

shadow = 0 dBshadow = 4 dBshadow = 8 dB

(a)

0 10 20 30 40 50 600

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

SINR DISTRIBUTION OF NEIGHBOR LINKS

SINR (dB)

FR

AC

TIO

N O

F L

INK

S

shadow = 0 dBshadow = 4 dBshadow = 8 dB

(b)

Figure 3.6: (a) The number of mesh routers that acquired a specific control timeslot vs. control time slot index and (b) the distribution of SINR over links betweentwo neighbor mesh routers, both for different values of the standard deviation of thelog-normal shadowing. In all cases, we set αT = αR = 1 dB.


larger value of σS, the neighborhood topology of MRs tends to spread more and in

a more random manner. For example, for σS = 0 dB, all neighbor MRs are located

at 100 m from a MR, while for σS = 4 dB, a non-negligible fraction of neighbor MRs

are located at or farther than 300 m as shown in Fig. 3.3.

In the case σS = 0 dB, one of the main differences in the protocol performance

compared to the reference scenario of σS = 4 dB is the fact that the number of MRs

that acquire the control time slot with slot index 0 coincides with the total number

of GRs deployed in the network as seen in Fig. 3.6-(a). Recall that these MRs co-

located with GRs initially acquire the control time slot with slot index 0 during the

initialization phase as explained in Section 3.8. In the case of σS = 0 dB, this initial

assignment allows all of these MRs to support the minimum target SINR on the

control time slot with index 0. At the same time, due to high interference on the slot

(i.e., the control time slot with slot index 0), MRs that are not co-located with GRs

cannot acquire the slot. As a result, the number of MRs that acquire the control

time slot with slot index 0 coincides with the total number of GRs deployed in the

network.

In other cases of σS > 0, on the other hand, some of the MRs co-located with

GRs have to release the control time slot with slot index 0 that is assigned during

the initialization phase because they cannot support the minimum target SINR to

their neighbor MRs over the initially assigned slot due to the random spread of MR

neighborhood topology. At the same time, there are some MRs not co-located with

GRs that acquire the control time slot with slot index 0. As a result, the overall

utilization of the control time slot with slot index 0 becomes comparable to that of

adjacent slots as seen in Fig. 3.6-(a).

The jaggedness of the SINR distribution in the case of σS = 0 dB follows from

the fact that the possible values for the receive signal power between two MRs con-

stitute a discrete set determined by the geometry of locations of MRs as opposed to

a continuous set in the case of σS > 0. Thus the resultant accumulated interference

level received at a MR tends to cluster around the set of discrete values determined

by the geometry.


3.10.3 Performance of Power Control of Receive Busy Tones

Fig. 3.7 shows the protocol performance when the power control scheme of receive

busy tones in Section 3.7 is employed. Compared to the case of no power control,

considerably fewer control time slots are required while all neighbor links still sup-

port the minimum target SINR of 12 dB. This improvement results from the fact that

the power control scheme exploits any margin in the received signal power between a

neighbor pair of MRs as explained in Section 3.7. However, employing the power con-

trol scheme, the protocol converges more slowly. For example, it took 112 frames and

130 frames, respectively, without and with the power control. The slower convergence

is due to increased contention among MRs. In addition, more active links become

degraded and as a result, more MRs are forced to release an acquired control time

slot and re-contend for another. Finally, the dip over the first few slots in Fig. 3.7-(a)

is due to the inclusion of region R3 in Fig. 3.2 into the conditions for transmitting

NACK packets as discussed later in Section 3.10.5. With R3 excluded, we observed

no such dip; each of the first 10 control time slots had about 300 MRs. Because the

power control scheme requires fewer control time slots (thus, less system overhead),

the scheme is adopted in the simulations presented in later chapters.

3.10.4 Effect of GR Topology

Fig. 3.8 shows the protocol performance under different GR topologies (i.e., different

number and locations of GRs) while the number and locations of MRs remain the

same. It is clearly seen that the overall protocol performance in terms of the control

time slot utilization (i.e., distribution of control time slots among MRs) and the SINR

distribution is not sensitive to the GR topology. This is expected because the overall

protocol performance largely depends on the neighborhood topology of MRs which is

not affected by the GR topology. The GR topology only determines the set of MRs

that initiate the protocol during the initialization phase described in Section 3.8.


0 20 40 60 80 1000

50

100

150

200

250

300


CONTROL SLOT INDEX

NU

MB

ER

OF

ME

SH

RO

UT

ER

S

no PC, shadow = 4 dBwith PC, shadow = 4 dB

(a)

0 10 20 30 40 500

0.01

0.02

0.03

0.04

0.05


SINR (dB)

FR

AC

TIO

N O

F L

INK

S

no PC, shadow = 4 dBwith PC, shadow = 4 dB

(b)

Figure 3.7: (a) The number of mesh routers that acquired a specific control time slotvs. control time slot index and (b) the distribution of SINR over links between twoneighbor mesh routers, both for with and without the power control of receive busytones. In all cases, we set αT = αR = 1 dB.


0 20 40 60 80 1000

50

100

150

200

250

300


CONTROL SLOT INDEX

NU

MB

ER

OF

ME

SH

RO

UT

ER

S

(#MRs):(#GRs) = 10:1(#MRs):(#GRs) = 20:1(#MRs):(#GRs) = 40:1(#MRs):(#GRs) = 80:1

(a)

0 10 20 30 40 50 600

0.01

0.02

0.03

0.04

0.05


SINR (dB)

FR

AC

TIO

N O

F L

INK

S

(#MRs):(#GRs) = 10:1(#MRs):(#GRs) = 20:1(#MRs):(#GRs) = 40:1(#MRs):(#GRs) = 80:1

(b)

Figure 3.8: (a) The number of mesh routers that acquired a specific control time slotvs. control time slot index and (b) the distribution of SINR over links between twoneighbor mesh routers, both for different GR topologies. In all cases, we set αT = αR= 1 dB, and the power control of receive busy tones is employed.


3.10.5 Effect of NACK Transmission Conditions

When region R3 in Fig. 3.2 is included into the conditions for transmitting NACK

packets as mentioned in Section 3.3.4, more NACK packets are transmitted in the

network. This is because when (PA, PB) ∈ R3 in the example in Fig. 3.2, the receive

MR would now transmit a NACK packet which would not have been transmitted if

region R3 had not been included. With more NACK packets transmitted, a MR that

has already acquired a control time slot is more likely to falsely decide that one of

its neighbor links has degraded and thus release the acquired control time slot. We

observed that the first few control time slots were degraded and released particularly

heavily at the beginning of the simulation, when many MRs all at the same time

discover the network and start contending.

When the power control scheme was not employed, the overall protocol perfor-

mance was yet almost identical whether region R3 was included or not. On the other

hand, with the power control scheme employed, the first few control time slots were

contended so heavily that the number of MRs that acquired these control time slots

were lower than in the case without region R3 included, resulting in the dip over the

first few control time slots in Fig. 3.7-(a) as noted earlier in Section 3.10.3. However,

the number of control time slots required and the resultant distribution of the average

received SINR remained almost the same. In all cases, the protocol converged more

slowly when region R3 was included.

3.11 Chapter Summary

In this chapter, we proposed a control time slot assignment protocol through which

every MR in the WMN acquires a broadcast time slot that supports a minimum

average received SINR from the MR to all of its neighbor MRs. The protocol was

designed to work even with the radio propagation models that include the random

shadowing process as well as the deterministic path-loss; and with the cumulative

interference calculation model, both of which are often oversimplified in the literature.

The protocol operates in a fully distributed manner.


The basic mechanisms of the protocol and the full operations were illustrated.

Selection criteria of the protocol parameters were also given. Furthermore, a power

control scheme was introduced that allows better utilization of resources for maintain-

ing control time slots. Other design considerations of the protocol including protocol

initialization, deadlock resolution, and NACK transmission were discussed. Extensive

simulation results were presented and discussed: the protocol was shown to support

the target minimum average received SINR over all neighbor pairs of MRs in all

simulated scenarios. In addition, the sensitivities of the protocol to the standard

deviation of log-normal shadowing of the radio propagation and to the GR topology

were determined. The benefit of the power control scheme was also demonstrated.

Chapter 4

Data Time Slot Access Control

Protocol

4.1 Introduction

As described in Section 2.3, the WMN studied in this thesis utilizes two types of time

slots: control time slots and data time slots. Control time slots provide a means for

MRs to exchange control messages among themselves for such operations as network

discovery, routing table construction, and resource negotiation. Control time slots also

allow EUs to discover the network and to request and negotiate resources with their

associated MRs. Over data time slots, on the other hand, user data are transmitted.

In this chapter, we develop a protocol that controls the medium access over data

time slots. The protocol utilizes control time slots that are assigned among MRs

through the control time slot assignment protocol developed in Chapter 3. The data

time slot access control protocol is fully cooperative and distributed: network entities

negotiate among themselves for resource allocation, and the messages are exchanged

only among one-hop neighbor MRs or between an EU and its associated MR. Further-

more, the protocol supports adaptive resource allocation through dynamic allocation

of data time slots and PHY transmission modes over the slots as well as through

user/queue prioritization. As part of the data time slot access control protocol, we

introduce a new admission and congestion control (ACC) policy that incorporates the

58

CHAPTER 4. DATA TIME SLOT ACCESS CONTROL PROTOCOL 59

resource availability at the routers along the path to the destination router, and yet

utilizes only local information available at the admitting router and has a minimal

increase in control overhead.

This chapter is organized as follows. It first illustrates the resource negotiation

mechanisms provided by the protocol. Key elements of the protocol for dynamic

and effective resource allocation are then discussed including data time slot selection,

queue/session prioritization, data transmission/retransmission, and resource release.

The routing protocol employed by the WMN is then described including its opera-

tions and routing metrics. Two ACC policies, one denoted as AC RO and considered

in [36] and in Section 4.8.3 and the other denoted as AC RF and considered in [37]

and in Section 4.8.4, are then presented including their stability properties. Finally,

extensive simulation results are presented and discussed. Several fundamental perfor-

mance metrics are examined including the network throughput, average per-session

throughput and blocking and dropping rates. The impact of the two ACC schemes on

the network performance are compared and discussed. Particularly, AC RF is shown

to stabilize the network even under heavy traffic loads unlike AC RO .

4.2 Resource Negotiation

To transfer user data over data time slots, network entities (MRs and EUs) exchange

resource negotiation requests over control time slots. The data time slot access control

protocol developed in this thesis provides mechanisms that support such resource

negotiation and allocation in a fully cooperative and distributed fashion. The frame

structure is repeated in Fig. 4.1.

There are two types of negotiation among network entities. One is between an EU

and its associated MR, and the other is among one-hop neighbor MRs. In the former,

a new EU’s admission request is controlled, and in the latter, a resource request by a

neighbor MR is controlled.



CTRL SLOTDATA SLOT

... ... ...FRAME 0 FRAME 1

DATA_TXDATA_ACK

Figure 4.1: The network frame structure: time is slotted into control and data timeslots and each time slot comprises multiple subslots.

4.2.1 Between EU and MR

When an EU tries to access the network, it first selects a MR to associate with

(see Section 4.8.2 for the MR section criterion). Once the EU determines a MR,

it transmits an admission request packet over the MR’s random access subslot (RA

in Fig. 4.1). If the request packet is received successfully at the MR and the MR

decides to admit the request according to its employed ACC policy, the MR assigns

to the EU data time slots on subsequent frames and the PHY transmission modes

over the slots. See Section 4.3 for how a MR selects data time slots and Section 4.2.2

for how it determines the PHY transmission modes over the data time slots. Each

data time slot is associated with only one EU at any time instance. If the request

packet is not received successfully due to collision with other request packets for the

same MR or due to accumulated interference from far EUs, the EU will back off and

retry at a later time. The EU will also back off and retry when it is notified that

the request is not accepted due to insufficient resources available. The EU is blocked

after a maximum number of unsuccessful retries. A maximum of 2 additional retries

(a total of 3 transmissions) are allowed in the simulations presented in this thesis.

See Section 4.8 for more discussion on the admission control of EUs.


4.2.2 Among One-Hop Neighbor MRs

The resource negotiation among one-hop neighbor MRs is performed on the control

time slots of the involved MRs and involves a three-way handshake: compiling and

transmitting requests by a requesting MR, allocating data time slots and PHY trans-

mission modes for reception by a requested MR, and finally, confirming the assignment

by the requesting MR. Operations performed during each phase of the handshake are

explained in the following.

Compiling Requests

During its control time slot, a MR compiles a list of data time slot requests for serving

sessions in its queues. A MR allocates a separate queue for each EU. For each EU, a

MR requests resources for serving the following amount of data:

max

min

q −∑i

dtx,i, (Nmr −∑j

Itx,j) dmax

, 0

(4.1)

where q is the current queue size, dtx,i is the data size currently being transmitted on

data time slot i per frame for the EU, Itx,j is 1 when data time slot j is being used

for transmission for this EU and 0 otherwise, Nmr is the maximum number of data

time slots allowed for transmission for each EU at a MR, set to 10 in the simulations

presented in this thesis, and dmax is the data size transmittable over one data time

slot per frame using the highest-rate PHY transmission mode, set to 6,412 bytes in

the simulations presented in this thesis (using 54 Mbps in Table 2.3 and DATA TX

= 0.95 msec in Fig. 4.1).

With the resources corresponding to the first argument of the minimum operator

in Eqn. (4.1), the queue would be emptied during the next frame if no additional

data arrive (assuming no transmission errors). If additional data arrive, the requested

amount will be adjusted according to Eqn. (4.1) such that the queue would be emptied

during the subsequent frame. The second argument of the minimum operator in

Eqn. (4.1) puts an upper limit on the number of data time slots assigned to each EU

for transmission.


A requesting MR also constructs and transmits a list of data time slot indices

whose received interference level on DATA ACK is below a threshold, TH ACK,

indicating that the MR can transmit only on those data time slots without causing

excessive interference on active transmissions around itself. The list of usable data

time slot indices is then transmitted along with each resource request to the best

neighbor MR toward the requested session’s destination that is determined by the

routing protocol described in Section 4.7.

Processing Requests

Once a MR receives the lists of data time slot requests and usable data time slot

indices, it prioritizes requests according to the queue/session prioritization policy dis-

cussed in Section 4.4. Starting from the request with the highest priority, a data time

slot is assigned if and only if the data time slot is usable at the requesting MR, idle at

the requested MR, its received interference level on DATA TX at the requested MR is

below a threshold, TH TX, and finally, the data time slot can support the minimum

PHY transmission mode. The PHY transmission mode on a data time slot is calcu-

lated based on the received signal strength from the requesting MR at the requested

MR and the interference level on DATA TX received during the previous frame at

the requested MR. The highest PHY transmission mode that can be supported by

the estimated SINR is assigned to the allocated data time slot. The total number

of data time slots allocated to one session is limited to a maximum number, set to

10 in this thesis. Note that unlike control time slots that are statically associated

with MRs until the network topology changes, data time slots are dynamically dis-

tributed among network entities according to the varying traffic demand and resource

usage status. When there are multiple available data time slots, one is chosen by the

data time slot selection algorithm described in Section 4.3. Results of the processed

resource requests are transmitted back during the requested MR’s control time slot.


Confirming Requests

During its control time slot, a MR sorts the received resource request results according

to the queue/session prioritization policy discussed in Section 4.4. Starting from the

request with the highest priority, each of its assigned data time slots is examined to

determine whether the data time slot is still usable at the MR. A data time slot may

have become unusable due to an increased interference level on the data time slot

during the previous frame, or may have been assigned by the same MR to another

request with a higher priority during the same control time slot. If a data time

slot cannot be assigned as indicated in its resource request result, the MR notifies

the corresponding neighbor MR of the failed allocation so that the data time slot is

immediately released at the neighbor MR for another session. The failure notification

is transmitted during the MR’s control time slot. On the other hand, if a data time

slot is successfully assigned, no explicit acknowledgment is transmitted. Once a data

time slot is assigned to a session, the data time slot is used for the same session until

the MR completes transmitting the session data or gives up transmission on the data

time slot after a maximum number of retransmissions without success for the session.

4.3 Data Time Slot Selection

When a MR selects a usable data time slot among multiple available ones for reception

during the second phase of the resource negotiation handshake in Section 4.2.2, the

MR can employ different strategies. It is known [38] that for a given path and current

usage of data time slots along the path, calculating the set of new data time slots

that maximizes the throughput along the path is NP-complete. Thus, we consider

heuristic approaches to choosing data time slots. One strategy is to take the available

data time slot with the lowest received interference level on DATA TX, denoted as

lowest-interference-first (LIF-S) in this thesis, and another is to select the available

one with the lowest slot index [32], denoted as earliest-index-first (EIF-S) in this

thesis. In Section 4.9, we compare the protocol performance under the LIF-S and

EIF-S policies.


4.4 Queue/Session Prioritization

When a MR allocates resources among multiple requests from its one-hop neighbor

MRs, the MR employs a scheme to prioritize the requests. The prioritization is made

locally at the MR. One policy is to prefer the session with the earliest admission time,

a form of first-come-first-served policy, in an attempt to minimize the session delay.

Another approach is to give the session with the longest queue the highest priority.

A MR can also jointly optimize the prioritization with the data time slot selection

policy in Section 4.3. In this thesis, a first-come-first-served policy is adopted with

respect to the network admission time of the EU. That is, an EU who is admitted

to the network earlier receives a higher priority. Once priorities are determined, a

MR allocates as many resources as possible to the EU with the highest priority up to

the amount requested. If there are any remaining resources, the MR then allocates

the remaining resources to the EU with the second highest priority up to the amount

requested, and so on.

4.5 Data Transmission/Retransmission

The transmission result of a data packet is determined based on the average received

SINR at the receive node. If the average received SINR exceeds the threshold for

the associated PHY transmission mode of the packet, the transmission is declared

successful and the receive node transmits an ACK packet to the transmit node. Note

that one ACK packet suffices for all the data packets received during one data time

slot because packets transmitted over one data time slot experience the same level of

co-channel interference as MRs allocate resources by data time slots.

There are two cases in which a transmit node does not receive an ACK packet

from its intended receive node. The first case is when a data packet is not received

successfully at the intended receive node due to the average received SINR falling

below a threshold. In the second case, although a transmission is successful and the

receive node transmits an ACK packet, the ACK packet is not received successfully at

the intended transmit node due to high interference resulting from other ACK packets


transmitted over the same data subslot DATA ACK. In either case, the transmit

node does not receive an ACK packet and it retransmits data packets as follows: if a

maximum number of consecutive retransmissions, set to 2 (a total of 3 transmissions)

in this thesis, had been tried, the transmit node releases the data time slot. If not, the

next lower-rate PHY transmission mode is tried. If the lowest-rate PHY transmission

mode was previously tried on the data time slot, the same PHY transmission mode

is retried. Power control is not considered for data packets in this thesis.

4.6 Resource Release

Resource release is requested either on a random access subslot (RA in Fig. 4.1) by

EUs, or on a control time slot (tBUSY in Fig. 4.1) by neighbor MRs. When an

EU completes transmitting its data to its associated MR, the EU transmits a short

notification packet during the MR’s random access subslot (RA). If the packet is

successfully received, the MR releases the data time slots allocated for receiving data

from the EU and transmits an ACK packet to the EU. Otherwise, the EU backs off

and retransmits the notification packet until it succeeds. When it succeeds, the EU

leaves the network. On the other hand, when an EU runs out of data time slots

before it completes transmitting its data, the EU drops its session and sends a drop

notice to its associated MR. Upon receiving the notice, the MR releases resources for

the EU and forwards the notice to the neighbor MR toward the session’s destination

if resources were allocated at the neighbor MR. Once an EU completely transfers its

session data to its associated MR, the session data are not dropped afterwards, i.e.,

the data are kept in the network until they are delivered to the destination node. The

session lifetime is assumed to be sufficiently large so that no session is dropped due

to expired lifetime.

When the session data of an EU are completely transferred from one MR to

another, the transmit MR releases its resources for the session and notifies the receive

MR of the completion over its control time slot. The receive MR then releases the

data time slots allocated for receiving for the session from the transmit MR.


4.7 Routing Protocol

4.7.1 Overview

The WMN simulator developed in this thesis implements a proactive routing protocol

that is based on minimum-cost spanning trees, similar to the hybrid wireless mesh

protocol (HWMP) with mesh portals of the IEEE 802.11s standards [39,40]. MRs co-

located with GRs periodically generate and propagate announcements (ANNs) and

other MRs generate and transmit replies (ANN REPs) in response to the best ANN

received. ANN REPs passing through intermediate MRs form spanning trees with

MRs co-located with GRs at roots. All ANNs and ANN REPs are transmitted on

control time slots of involved MRs. As explained in Section 2.3, routing tables are

constructed at the beginning of each simulation run and remain the same throughout

the run. This is because the network topology does not change during each run and

neither does each of the routing metrics considered in Section 4.7.4 in this thesis.

In an actual network, the routing tables would be periodically updated as network

conditions changed.

4.7.2 Processing and Forwarding Announcements

Each ANN is tagged with the source MR ID, the arrival time, the neighbor MR from

which the ANN was received, and time-to-live (TTL) in hops. The maximum TTL

in hops, MAX TTL, varies according to the average number of MRs served by one

GR in the network, and MAX TTL is set to 6 for the ratio of (#MRs):(#GRs) =

40:1. For a larger ratio, MAX TTL is set to be larger. Upon receiving an ANN, a

MR compares it with the best ANN received thus far from the same source MR, and

keeps the one with the better routing metric. If the new ANN has the smaller cost,

it is forwarded to the one-hop neighbor MRs.

4.7.3 Generating and Forwarding Replies to Announcements

After a MR receives the first ANN, it waits for a certain period of time, initially

set to MAX TTL frames, and then determines the ANN with the smallest routing


cost. The MR then constructs a reply message (ANN REP), and transmits it to the

neighbor MR from which the best ANN was received.

4.7.4 Routing Metrics

Effective link and path metrics may vary depending on the traffic type as different

traffic types have different quality-of-service (QoS) requirements. Two link metrics are

investigated in this thesis for the best-effort web traffic type described in Section 2.6:

one link metric is a constant, and the other link metric is the PHY transmission time

(or air-time) per unit data size under the idealized scenario in which the interference

level across the network is uniform and is such that a neighbor link with the received

signal power of PTH NBR barely supports the minimum PHY transmission mode.

The path or routing metric is defined to be the sum of the link metrics along the

path. Under the former link metric, the path or routing metric becomes the number

of hops along the path, and a MR selects the shortest path toward the destination

node. Under the latter metric, the path or routing metric becomes the sum of the

air-times along the path under the aforementioned idealized scenario. Either of the

metrics does not include the processing delay and the medium access acquisition delay

at intermediate MRs. The former metric is denoted as ‘min hop’, and the latter as

‘min air-time’ in this thesis.

Consider the example in Fig. 4.2 for illustration of the two different routing met-

rics, in which the received signal power over the link A→ B, PA→B, and that over the

link A→ C, PA→C , are different. In the scenario, MR A tries to determine the best

neighbor MR for forwarding the user data toward the best destination MR co-located

with a GR (MR D in the figure). Under ‘min hop’ routing metric, both neighbors

(MR B and MR C) have the same link metric of 1 (and the same routing metric of

2). Under ‘min air-time’, on the other hand, the link metrics for links A → B and

A → C may differ. The idealized interference scenario mentioned above says that

for a link T → R with PT→R = PTH NBR, the supportable PHY transmission rate,


Figure 4.2: Example for illustrating the two different routing metrics. PA→B 6= PA→C .

phyT→R, is assumed to be the minimum PHY transmission rate, phymin. That is,

phyT→R = f(SINRT→R) = f(PT→RIR +N

) = f(PTH NBR

IU +N) = phymin

where f(·) denotes a functional relationship, SINRT→R is the average received SINR

at the receive MR R from the transmit MR T , IR is the average received co-channel

interference level at the receive MR R, IU is the average received co-channel inter-

ference level at a receive MR that barely supports phymin with the average received

signal power level of PTH NBR, and N is the receiver thermal noise power. In other

words, under the idealized interference scenario, the interference level is assumed to

be

IU =PTH NBR

f−1(phymin)−N (4.2)

For phymin = 6 Mbps and its corresponding SINR threshold of 12 dB given in Ta-

ble 2.3, and PTH NBR = -50 dBm as discussed in Section 3.5.1, and N = -98 dBm

given in Table 2.2, we have IU ≈ -62 dBm.

Then, the link metric under ‘min air-time’ for link A→ B, ATA→B, in Fig. 4.2 is


given by

ATA→B =1

phyA→B

where

phyA→B = f(SINRA→B)

= f(PA→BIB +N

)

= f(PTH NBR + P∆(A→B)

IU +N)

≥ phymin

where IU is as given in Eqn. (4.2). The link metric for link A→ C, ATA→C , in Fig. 4.2

is similarly given. If P∆(A→B) ≥ P∆(A→C) and other conditions are the same, then

MR B is chosen as the best neighbor of MR A. Although this idealized interference

scenario does not occur in the simulated networks, the routing metric ‘min air-time’

does take into account the difference in the received signal power level for a neighbor

MR pair unlike the routing metric ‘min hop’.

Fig. 4.3 shows the number of hops along paths from MRs to their best GRs under

the two link metrics in the case of (#MRs):(#GRs) = 40:1. The statistics were found

from the routing tables constructed during the pre-operation phase of a simulation run

under the simulated scenario. MRs with 0 hop correspond to those MRs co-located

with GRs. Overall, paths generated under the metric ‘min air-time’ are longer than

those under the metric ‘min hop’. This is because the neighbor links along a path

chosen under the metric ‘min air-time’ tend to have higher received signal power than

those along a path chosen under the metric ‘min hop’, and a neighbor link with a

higher received signal power tends to be shorter in distance. As a result, a path under

the metric ‘min air-time’ tends to be longer in the number of hops. The average path

lengths are 2.16 and 2.51 hops, respectively, for the case shown in Fig. 4.3.


0 1 2 3 4 5 6 70

1000

2000

3000

4000

5000

6000

7000

PATH LENGTH (HOPS)

NU

MB

ER

OF

ME

SH

RO

UT

ER

S

DISTRIBUTION OF PATH LENGTH

min_hopmin_air−time

Figure 4.3: Distribution of the number of hops under two different link metrics con-sidered in this thesis. (#MRs):(#GRs) = 40:1

4.8 Admission and Congestion Control

4.8.1 Introduction

A crucial component of the data time slot access control protocol is admission and

congestion control (ACC). When admitting new users, it is critical to consider the

current resource usage of existing users. If the network admits more users than it can

support, the quality-of-service (QoS) as measured by delay or throughput of existing

users may degrade to an unacceptable level. For multi-hop networks, it is more

challenging to assess the resource usage of existing users because one has to examine

resources beyond the first hop, i.e., beyond the admitting router.

Various forms of admission control have been considered for multi-hop wireless

networks. Many MAC schemes that involve contention in medium acquisition [13–16]

incorporate admission control into the contention process by admitting only those


nodes that successfully acquire the medium. These schemes usually examine the

medium availability only for the first hop but not beyond it. Our work presented

in [36] and in Section 4.8.3 below takes a similar approach: a new user is admitted

based on whether resources are available for reception over the first hop between

the user and the admitting router. Although these schemes based on the resource

availability for the first hop may be simple and even work well under light traffic

loads, they fail to serve the admitted users under heavy traffic loads.

Another approach [38,41] explicitly probes the resource availability at each router

along the way to the destination node before admitting a new traffic request. The

network admits a new traffic request only if a resource is available at the time of

probe. Although this approach enables the network to detect congestion sooner, it

increases the setup delay before a new user is admitted. Another drawback is that

exchanging probe messages incurs excessive control overhead.

As part of the data time slot access control protocol, we introduce a new ACC

policy in [37] and in Section 4.8.4 that incorporates the resource availability at the

routers along the path to the destination router, and yet utilizes only local information

available at the admitting router and has a minimal increase in control overhead.

Through analysis and simulations, the scheme is shown to stabilize the network under

high traffic loads unlike our earlier scheme in [36] and in Section 4.8.3.

4.8.2 Framework

As briefly explained in Section 4.2.1, when an EU arrives in the network, it first listens

to beacon signals transmitted by nearby MRs to select a “best” MR to associate with.

In this thesis, the MR with the strongest received signal power at the EU is selected.

Once an EU determines a best MR, it transmits an admission request packet over the

random access control subslot (RA in Fig. 4.1) of the MR. The EU also indicates the

data size it wishes to transmit and the list of usable data time slot indices that have

the received interference level on DATA ACK below a threshold, TH ACK, during

the previous frame. If the request packet is received successfully at the MR, the MR

transmits an ACK packet to the EU, listing the available data time slot indices and


supportable PHY transmission modes over them. A particular PHY transmission

mode is selected from the set of PHY transmission modes given in Table 2.3 based

on the received signal strength from the EU at the MR and the received interference

level on DATA TX at the MR during the previous frame. Each EU is limited to a

total of 10 data time slots to limit cases where one EU with a very large data volume

would occupy all of the resources around the EU. If the request packet is not received

successfully due to collision with other request packets for the same MR or due to

accumulated interference from far EUs, the EU will back off and retry at a later time

for the same MR up to a maximum number of trials, set to 3 in this thesis. The EU

will also back off and retry when it is notified that no resource is available for the EU.

The EU is blocked after a maximum number of unsuccessful retries. In the following,

we explain the two ACC schemes considered in this thesis.

4.8.3 AC RO (Reception Only)

The ACC scheme denoted as AC RO in this thesis is stated as follows [36]: a MR

admits an admission request if and only if the MR has resources available for receiving

data for the request. Note that under this scheme, many of the MRs, particularly, the

peripheral ones with respect to GRs, may continue to admit new EUs even when they

cannot acquire enough resources under high traffic loads to forward the received data

toward their neighbor MRs. As a result, the number of active queues with ‘insufficient’

resources (Ni in Eqn. (4.5)), may keep increasing without bound in a highly congested

condition. See Section 4.8.4 below for the definition of ‘(in)sufficiency’ of resources

for a queue. We will examine later in Section 4.9 how this stability property of the

policy affects the network performance.

4.8.4 AC RF (Reception Forwarding)

We first introduce a definition. A queue is said to have been allocated sufficient

resources when the queue has been allocated resources as requested in Eqn. (4.1),


i.e., if and only if the following condition is satisfied:

min

q −∑i

dtx,i, (Nmr −∑j

Itx,j) dmax

≤ 0 (4.3)

or equivalently, ∑i

dtx,i ≥ q or∑j

Itx,j ≥ Nmr (4.4)

where q, dtx,i, Itx,j, and Nmr are as defined for Eqn. (4.1). Each queue contains a

flag that indicates this sufficiency information. The flag is set to ‘unknown’ when a

queue is created. It is then updated immediately after a resource request is made for

this queue and the result is known. If the requested neighbor MR has not assigned

enough resources for the request, the requesting MR sets the flag to ‘insufficient’, and

otherwise, to ‘sufficient’.

The ACC policy denoted as AC RF in this thesis is then stated as follows: a MR

admits an admission request if and only if 1) each of the queues at the MR has been

assigned ‘sufficient’ resources, and 2) the MR has additional resources for receiving

data for the request. Note that the decision can be made locally at the admitting

MR, and thus, the policy is implemented in a fully distributed manner.

4.8.5 Stability Properties of AC RF

We now show that the ACC policy AC RF can stabilize the network under any offered

traffic to the network. Here, we assume a uniform stochastic arrival process across

the network. We call the WMN stable if and only if the number of active EUs or the

number of active queues in the network remains bounded. We assume the case in

which the limit on the number of queues and the limit on the queue size at each MR

are large enough so that they do not restrict the network throughput.

We prove the stability by showing that the number of active queues at each MR,

Nq, is bounded. Consider

Nq = Ns +Ni +Nu (4.5)

where Ns is the number of active queues at a MR with ‘sufficient’ resources, Ni is the


number of active queues with ‘insufficient’ resources, and Nu is the number of queues

that have been just created and have the sufficiency flag set to ‘unknown’ .

Ns is bounded because the number of active queues allocated with transmit data

time slots at a MR is limited due to the finite and fixed number of data time slots

of the network. The fact that Ni and Nu are bounded is explained as follows. When

the network starts operations, Ni = 0 and Nu = 0 at a MR, and the MR starts

receiving new resource requests. The maximum number of such requests received

over one frame, Nu,max, is bounded due to the finite and fixed number of data time

slots of the network. Because it takes up to two frames before a MR determines the

sufficiency information of a queue for a newly admitted request, Nu can grow up to

2Nu,max while Ni = 0. If Ni still remains zero after the sufficiency information is

determined for those queues admitted during the first frame, the MR can admit new

requests and Nu could grow up to 2Nu,max. On the other hand, if we have Ni > 0

(note here that Ni ≤ Nu,max because it is only those queues admitted during the

first frame that just requested resources), no further resource request is considered

for admission at the MR, and Nu stops increasing. These Nu queues, that correspond

to those queues admitted during the second frame, will be set to either ‘sufficient’ or

‘insufficient’ during the following frame, resulting in Nu = 0. The MR continues to

deny additional resource requests until Ni diminishes to zero. In this situation, it is

guaranteed that Ni decreases to zero because, according to the queue prioritization

policy discussed in Section 4.4, each of the queues with ‘insufficient’ resources will

eventually have the highest priority among those queues requesting resources of the

common parent MR and will receive ‘sufficient’ resources. As soon as Ni becomes

zero, the MR starts admitting new resource requests and repeats the above process.

4.9 Simulation Results

In this section, we present the performance of the WMN that employs the data time

slot access control policy presented in this chapter along with the control time slot

assignment protocol in Chapter 3.


4.9.1 Simulation Setup

The two ACC schemes discussed in Section 4.8, AC RO and AC RF, and the two

data time slot selection algorithms mentioned in Section 4.3, EIF S and LIF S, are

considered. While both of the data time slot selection algorithms are simulated under

AC RO, only LIF-S is simulated under AC RF. There are 70 control time slots and

70 data time slots in each frame. Each data slot and control slot is 1 msec long so one

frame is 140 msec long. DATA TX = 0.95 msec in Fig. 4.1. TH TX and TH ACK

are both set to -63 dBm. (#MRs):(#GRs) = 40:1.

The simulator employs the parallel-processing simulation technique presented in

Appendix A, and each data point is obtained from one long simulation run that takes

several days up to a week using 16 processors simultaneously. See Section 2.7 for more

details on the WMN simulator and its data acquisition and processing procedure.

4.9.2 Performance Metric

Various performance metrics are determined for each simulation scenario. Two pri-

mary metrics are mean network throughput and per-session throughput: the mean

network throughput is calculated as the aggregate size of successfully completed ses-

sions across the network per unit time, and the per-session throughput is calculated as

the session data size divided by the session delay for a successfully completed session.

The session delay is measured from the time a session is admitted to the network to

the time the destination MR of the session completely releases resources allocated to

the session. We also find the blocking rate which is the ratio of the number of blocked

sessions to the number of arrived sessions, and the dropping rate which is the ratio

of the number of dropped sessions to the number of admitted sessions. Recall from

Section 4.2.1 and Section 4.8.2 that an EU is blocked if its admission request is not

accepted after a maximum number of retires. On the other hand, an EU is dropped if

it runs out of data time slots before it completes transmitting its data to the network

as discussed in Section 4.6.


4.9.3 Results under AC RO

Network Throughput

Fig. 4.4-(a) shows the mean network throughput under different simulated scenarios.

In all simulated scenarios, when the session arrival rate is small, the mean network

throughput increases almost linearly as a function of the session arrival rate, success-

fully serving most of the EUs arriving to the network. However, as the session arrival

rate keeps increasing, the mean network throughput starts to decrease. The decrease

can be explained as follows. As the arrival rate increases, the interference level across

the network also increases and the mean PHY transmission rate supportable on a

data time slot decreases as indicated in Fig. 4.5-(a). Consequently, the network uses

increasingly more data time slots. If the arrival rate keeps increasing, at some point,

all the data time slots become exhausted. At this point, the network must start

blocking new EUs. However, many of the MRs may still find resources available

for reception at themselves under AC RO. Those MRs continue to admit new EUs

and these admitted EUs incur additional interference to the network leading to even

lower PHY rates. The resultant aggregate data size transported by the network thus

decreases, leading to the decrease in the mean network throughput in Fig. 4.4-(a).

Per-Session Throughput

As seen in Fig. 4.4-(b), the mean per-session throughput decreases as the traffic arrival

rate increases although the corresponding mean network throughput increases. This

implies that, under a higher traffic arrival rate, each EU experiences a longer session

delay on average while more EUs are simultaneously served across the network. There

are two major factors to the longer session delay: one factor is the lower mean PHY

transmission rates on data time slots due to increased interference, and the other

factor is the longer delay in acquiring resources at MRs due to more EUs competing

for the same resources.

The mean per-session throughput does not exist for λ ≥ 0.1 as indicated in

Fig. 4.4-(b). This is because the network employing AC RO becomes unstable under

those arrival rates, i.e., the number of active EUs in the network keeps increasing and


0.04 0.06 0.08 0.1 0.12 0.14 0.16300

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Figure 4.4: (a) Mean network throughput vs. session arrival rate; (b) mean per-session throughput for successfully completed sessions vs. session arrival rate. Bothunder AC RO and (#MRs):(#GRs) = 40:1


the session delay also keeps increasing as the system evolves over time. As a result, the

session delay becomes non-stationry and thus does not have a time-invariant mean

value. The network employing AC RO thus cannot guarantee a stable per-session

throughput under modest or heavy traffic loads.

Blocking and Dropping Rates

The blocking rate, not shown here, is found negligible in all the three simulated sce-

narios for arrival rates λ = 0.05 - 0.075. The dropping rate shown in Fig. 4.5-(b)

is overall small, and increases as the traffic arrival rate increases. Recall from Sec-

tion 4.6 that an EU releases a data time slot after a maximum number of unsuccessful

retransmissions on the slot, and becomes dropped if it runs out of data time slots.

The consecutive transmission failures on a data time slot are primarily due to new

transmissions that were not seen when the data time slot was assigned to this new

EU. When the network is lightly loaded, the dropping rate increases with the increas-

ing arrival rate because there are more new transmissions on a data time slot when

more new EUs are admitted. However, we expect that the dropping rate would stop

increasing with the increasing arrival rate once the network becomes saturated. See

Section 4.9.4 and Section 5.2.4 for more results and discussions on the behavior of

the dropping rate under the ACC scheme AC RF and various network topologies and

traffic loads.

Effect of Data Time Slot Selection Strategy

As shown in Fig. 4.4 and Fig. 4.5, under light or modest traffic loads, i.e., under

λ = 0.05 - 0.075, the system performs noticeably better under LIF-S than under

EIF-S in terms of mean network throughput, per-session throughput and dropping.

This outperformance can be explained with the much higher PHY transmission rates

under LIF-S as seen in Fig. 4.5-(a). Recall from Section 4.3 that EIF-S selects the

available data time slot with the earliest slot index while LIF-S chooses the available

data time slot with the lowest interference level. When the traffic load is light or

modest such that not all the data time slots are utilized under EIF-S, data time


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DR

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%)


(b)

Figure 4.5: (a) Mean PHY transmission rate of successfully received data packetsamong MRs vs. session arrival rate; (b) dropping rate vs. session arrival rate. Bothunder AC RO and (#MRs):(#GRs) = 40:1


slots used under EIF-S tend to experience higher interference on average compared

to those under LIF-S. This is because the traffic load is distributed among data time

slots more evenly under LIF-S compared to EIF-S and thus each data time slot can

support higher PHY transmission rates on average under LIF-S, leading to better

overall performance.

The performance difference between LIF-S and EIF-S becomes smaller as the traf-

fic load becomes heavier, and the mean network throughput even becomes lower at

λ = 0.15. This trend can be partly explained by the decreased gap in the mean

PHY transmission rates between the two strategies as seen in Fig. 4.5-(a). As the

network becomes increasingly congested, the interference level across the network be-

comes higher and increasingly more time slots become utilized and as a result, all the

data time slots experience a similar level of interference. The higher mean network

throughput under EIF-S implies that utilizing data time slots with the earliest index

may lead to better reuse of data time slots than utilizing data time slots with the

lowest interference level. In fact, this better utilization of resources under EIF-S than

under LIF-S is consistent with the results in [25,26] in which EIF-S (or Autonomous

Reuse Partitioning in [25, 26]) performs better than LIF-S (or Least Interference Al-

gorithm in [25, 26]) when the algorithms are used as dynamic channel assignment

schemes in a mobile cellular network serving circuit-switched voice traffics.

Effect of Routing Metric

As seen in Fig. 4.4-(a), between the two routing metrics under EIF-S, the difference in

the mean network throughput is insignificant when the traffic load is light or modest,

e.g., λ = 0.05 - 0.075. This results from the fact that most of the EUs arriving

to the network successfully receive the service under such traffic loads. In fact, as

mentioned earlier, the blocking rate is negligible in all the three simulated scenarios,

and the dropping rate shown in 4.5-(b) is also small in either case over λ = 0.05 -

0.075.

The per-session throughput seen in Fig. 4.4-(b), however, is noticeably higher un-

der ‘min hop’ under light traffic loads, e.g., λ = 0.05. This implies that although PHY

transmission rates are higher on average with ‘min air-time’ as shown in Fig. 4.5-(a),


the PHY transmission rates in both routing scenarios are high enough that the delay

in transmitting data packets is comparable to the delay in acquiring data time slots

through the resource negotiation handshake described in Section 4.2. On the other

hand, when the traffic load becomes modest, e.g., λ = 0.075, the PHY transmission

rates decrease on average due to higher interference across the network, and the de-

lay in transmitting data packets increases and tends to dominate the session delay.

As a result, under ‘min air-time’, the mean session delay, not shown here, becomes

shorter and the per-session throughput seen in Fig. 4.4-(b) becomes higher compared

to ‘min hop’.

Session Fairness

In Fig. 4.6, we present the mean per-session throughput for successfully completed

sessions versus the path length in hops that is measured from each session’s associated

MR to the session’s destination MR co-located with a GR. Note that the per-session

throughput greatly varies among sessions with different path lengths. On average,

sessions with 0 hops, i.e., sessions directly transmitted to MRs co-located with GRs

have the mean per-session throughput 3.5 times higher than those with 4 hops. This

unfairness among sessions at different geographic locations with respect to GRs is

one of the important issues for multi-hop wireless networks. Addressing the issue is,

however, beyond the scope of this thesis.

Instability of AC RO Revisited

The network behavior under overloads suggests that AC RO which does not consider

the resource availability beyond the admitting MR tends to admit more sessions

than the network can support. Several extensions can be pursued. One approach is

to incorporate the resource availability at relaying MRs along the path toward the

session’s destination before admitting a session. It would then increase the setup time

before a session is admitted to the network as discussed in Section 4.8.1. Another

approach is to drop sessions at relaying MRs even after EUs completely transfer their

session data to their associated MRs. In the latter approach, an additional mechanism


0 0.5 1 1.5 2 2.5 3 3.5 40.1

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EIF−S, min_hop, λ = 0.05EIF−S, min_air−time, λ = 0.05LIF−S, min_air−time, λ = 0.05EIF−S, min_hop, λ = 0.075EIF−S, min_air−time, λ = 0.075LIF−S, min_air−time, λ = 0.075

Figure 4.6: Mean per-session throughput for successfully completed sessions vs. pathlengths (hops) under AC RO and (#MRs):(#GRs) = 40:1

would be needed by which a failure or a dropping at a relaying MR is notified back

to the EU. The mechanism would then need to deal with the situation in which such

an EU leaves the network before it receives such a failure notice. A better approach

is to use a better ACC as discussed in the next section.

4.9.4 Results under AC RF

In this section, we present the network performance under AC RF which incorporates

the resource availability at the MRs along the path to the destination MR, and yet

utilizes only local information available at the admitting MR and has a minimal

increase in control overhead compared to AC RO. We also compare the network

performance with that under AC RO presented in Section 4.9.3.


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(a)

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Figure 4.7: (a) Mean network throughput for successfully completed sessions vs. ses-sion arrival rate; (b) mean per-session throughput for successfully completed sessionsvs. session arrival rate. Both are under (#MRs):(#GRs) = 40:1.


Network Throughput

As seen in Fig. 4.7, when the traffic arrival rate is small, the network throughput

behaves quite similarly under both AC RO and AC RF: the network successfully

serves most of the EUs arriving to the network, resulting in the almost linear network

throughput in Fig. 4.7-(a) and insignificant blocking and dropping rates in Fig. 4.8-

(a) and (b). However, as the arrival rate increases, the network throughput shows a

remarkable difference: the network throughput starts to decrease under AC RO while

it continues to increase under AC RF.

Under AC RF, the network begins to block new EUs as the network starts to sat-

urate unlike AC RO. When the MRs near GRs start to become congested such that

some of their existing queues do not receive sufficient resources, the MRs stop accept-

ing admission requests. Then, their child MRs successively stop accepting admission

requests until their existing queues receive sufficient resources from these parent MRs,

and so on. As a result, the network reaches a balance in which the network admits

only as many EUs as it can support without destabilizing itself. As a result, as shown

in Fig. 4.7-(a), the network throughput still increases as the arrival rate keeps increas-

ing. However, the increment in the network throughput diminishes. This diminishing

increment results from the fact that the network becomes increasingly utilized and

the idling fraction of the network diminishes as the network becomes increasingly

loaded. Eventually, all the network resources would be exhausted, and the network

throughput would converge to a level called the network capacity.

We note that the difference resulting from the two different routing metrics is

insignificant compared to the difference from the two different ACC policies. This

somewhat small difference follows from the fact that the simulated WMN has rather

shallow spanning trees for routing. As mentioned in Section 4.7.4, the average path

length from MRs to their corresponding best GRs is 2.16 hops under ‘min hop’, and

2.51 hops under ‘min air-time’ with (#MRs):(#GRs) = 40:1. See Section 5.3 for

more results and discussions on the effect of the two routing metrics on the network

performance under different GR topologies.


Per-Session Throughput

As seen in Fig. 4.7-(b), under AC RF, the mean per-session throughput decreases

as the traffic arrival rate increases similarly to AC RO. However, unlike AC RO, the

network remains stable under heavy traffic loads. The number of active EUs in the

network and the session delay remain stationary over time and consequently, the net-

work can guarantee a stable mean per-session throughput even under heavy traffic

loads. As shown in Fig. 4.7-(b), the decrement in the per-session throughput dimin-

ishes with the increasing traffic arrival rate. We expect that the mean per-session

throughput would converge to a constant as the network resources are increasingly

utilized with the increasing traffic arrival rate. See Section 5.2.2 for more results and

discussions on the behavior of the mean per-session throughput under various GR

topologies and traffic loads.

Blocking and Dropping Rates

The blocking rate increases with the increasing session arrival rate for AC RF as

seen in Fig. 4.8-(a). This follows from the fact that under a higher arrival rate, a

new EU arriving to the network sees more EUs, on average, being served at the

MR to associate with, and thus has a higher probability of being blocked. As the

network starts to saturate, the blocking rate starts to increase very sharply as seen

in Fig. 4.8-(a) because the number of EUs supportable by the network also starts to

saturate. The blocking rate would asymptotically converge to 100%. On the other

hand, the blocking rates for AC RO are found negligible in all the simulated scenarios

as indicated in Fig. 4.8-(a). See Section 5.2.3 for more results and discussions on the

behavior of the blocking rate under various GR topologies and traffic loads.

Similarly to AC RO, the dropping rate increases with the increasing arrival rate

when the network is lightly loaded as seen in Fig. 4.8-(b). However, we expect that the

dropping rate would stop increasing with the increasing arrival rate once the network

becomes heavily saturated. See Section 5.2.4 for more results and discussions on the

behavior of the dropping rate under various GR topologies and traffic loads. Note

that in Fig. 4.8-(b), the dropping rate is not shown for AC RO when the network


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Figure 4.8: (a) Blocking rate vs. session arrival rate; (b) dropping rate vs. sessionarrival rate. Both are under (#MRs):(#GRs) = 40:1.


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Figure 4.9: Mean PHY transmission rate of successfully received packets among MRs.(#MRs):(#GRs) = 40:1.

is heavily loaded because the network becomes unstable under a heavy traffic load

under AC RO and thus the dropping rate does not accurately reflect the network

performance.

PHY Transmission Rate

Similarly to AC RO, the mean PHY transmission rate of successfully transmitted

packets among MRs generally decreases with the increasing session arrival rate for

AC RF as shown in Fig. 4.9. This is due to the fact that at a higher arrival rate,

there are more EUs admitted in the network and the resultant interference level

becomes higher. However, the decrement diminishes and the mean PHY transmission

rate remains rather constant when the network becomes heavily saturated as seen

in Fig. 4.9. Note that the mean PHY transmission rate is higher under ‘min air-

time’ compared to that under ‘min hop’. This is partly due to the fact that the


received signal strength over neighbor links under the former routing metric tends to

be noticeably higher (a few dB) than that under the latter metric.

4.10 Chapter Summary

In this chapter, we developed a protocol that controls the medium access over data

time slots for the WMN studied in this thesis. The protocol utilizes control time

slots and operates in a fully cooperative and distributed manner. Furthermore, the

protocol supports adaptive resource allocation through dynamic allocation of data

time slots and PHY transmission modes over the slots as well as through user/queue

prioritization. Particularly, we introduced an ACC scheme that stabilizes the network

under heavy traffic loads, and yet utilizes only local information available at the

admitting router and has a minimal increase in control overhead.

Resource negotiation mechanisms provided by the protocol were illustrated and

key elements of the protocol for dynamic and effective resource allocation were dis-

cussed including data time slot selection, queue/session prioritization, data trans-

mission/retransmission, and resource release. The routing protocol employed by

the WMN was also described including its operations and routing metrics. Two

ACC policies were then introduced and their stability properties were analyzed. Fi-

nally, extensive simulation results were presented and discussed. Several fundamental

performance metrics were examined including the network throughput, per-session

throughput and blocking and dropping rates. The impact of the two ACC schemes

on the network performance were compared and discussed. Particularly, AC RF was

shown to stabilize the network under heavy traffic loads whereas AC RO is unstable

under heavy traffic loads.

Chapter 5

Scalability

5.1 Introduction

In this chapter, we investigate the performance of the WMN with focus on the scala-

bility under different scenarios of network topology and routing metric. Specifically,

we vary the number and locations of GRs deployed in the network while holding the

locations and total number of MRs constant, and also consider two different routing

metrics. The WMN runs the control time slot assignment protocol in Chapter 3 and

the data time slot access control protocol in Chapter 4. The network employs the

data time slot selection algorithm LIF-S in Section 4.3 and the ACC scheme AC RF

in Section 4.8.4 for all simulations presented in this chapter.

We examine the scalability behavior of several fundamental performance metrics

including the network throughput, per-session throughput, and blocking and dropping

rates, and identify major factors that affect the scalability behavior under the simu-

lated scenarios. We show that the PHY, MAC and routing layers of network functions

interact intricately with one another to determine the network performance. Specifi-

cally, we demonstrate that different mesh sizes (i.e., number of MRs served by one GR)

and different routing paths affect the tolerable interference level across the network

and that they consequently determine the usage of radio resources, i.e, supportable

PHY transmission rates and data time slots, across the network. Particularly, we

show that with more deployed GRs, i.e., more backbone support to the network, the

89

CHAPTER 5. SCALABILITY 90

Figure 5.1: The network topology of the simulated network.

network throughput and per-session throughput improve significantly, and explain

the improvement based on the aforementioned interactions across the layers of net-

work functions. We also study the impact of the two different routing metrics on the

overall network performance.

5.2 Scalability with Different GR Topology

We vary the network topology by deploying different numbers of GRs in the network

while keeping the number and locations of MRs the same. We consider the ‘min air-

time’ routing metric for all the simulations presented in this section. Let K denote

the horizontal separation of adjacent GRs in the number of MRs and L the vertical

separation as shown in Fig. 5.1. Recall that GRs are co-located with MRs. The


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Figure 5.2: Distribution of the number of hops along paths from MRs to their bestGRs under different scenarios of GR topology and routing metric.

number of MRs served by one GR is then K2 + L2, and is varied over 1, 2, 5, 10, 20,

40, and 80. Table 5.1 shows the values of K and L for each of the topology scenarios

simulated. Fig. 5.2 shows the distribution of the number of hops along paths from

MRs to their best GRs under each of the simulated scenarios. Note that for the case

of ‘min air-time’ and (#MRs):(#GRs) = 2:1, the number of MRs with 0 hop and that

with 1 hop are similar to each other (the numbers are 7200 and 7172, respectively),

leading to the flat start of the corresponding distribution in Fig. 5.2. On the other

hand, for other cases, the distribution starts low and increases with the number of

hops and then decreases again.


Table 5.1: Parameter values for simulated GR topology

K 8 6 4 3 2 1 1

L 4 2 2 1 1 1 0

(#MRs) : (#GRs) 80 40 20 10 5 2 1

5.2.1 Network Throughput

As discussed in Section 4.9 and seen in Fig. 5.3, the mean network throughput gener-

ally increases as the session arrival rate increases given the network topology and the

routing metric. When the session arrival rate is small, the mean network throughput

increases almost linearly with the arrival rate, successfully serving most of the EUs

arriving to the network. However, as the arrival rate keeps increasing, the network

becomes overloaded and starts to saturate, and the increment in the mean network

throughput decreases and eventually diminishes as shown in Fig. 5.3.

The network throughput behavior can be explained with two related quantities:

the mean PHY transmission rate of successfully received data packets, and the mean

aggregate number of active data time slots used across the network. Fig. 5.4 shows

the mean PHY transmission rate of successfully received data packets among MRs

under different GR topologies. Note that the figure does not include the case of

(#MRs):(#GRs) = 1:1 because data packets are transmitted only between EUs and

MRs but not among MRs under that GR topology. Fig. 5.5 shows the mean aggregate

number of data time slots used for reception at MRs across the network under different

GR topologies. As more EUs arrive and are admitted to the network under a higher

session arrival rate, the interference level on data time slots becomes higher and

thus the supportable PHY transmission rate becomes lower as seen in Fig. 5.4, and

more data time slots are used as shown in Fig. 5.5. As the session arrival rate

keeps increasing, all the usable data time slots eventually become exhausted, and

both of the aggregate number of used data time slots across the network and the

mean supportable PHY transmission rate saturate. In such a congested condition, as

discussed in Section 4.9.4 and shown in Fig. 5.8, the employed ACC scheme AC RF

blocks excessive EUs and thereby controls the interference level on data time slots so


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5:1

10:120:1

40:180:1

1:1

2:1

Figure 5.3: Mean network throughput vs. session arrival rate under different gatewayrouter topologies. In all cases, the routing metric ‘min air-time’ is used.

that the network reaches a balance in which the network admits only as many EUs

as it can support providing stable per-session delays. The behavior of the blocking

rate is discussed more later in Section 5.2.3.

Fig. 5.3 shows that the behavior of the network throughput remains consistent

when different numbers of GRs are deployed in the network while the locations and

number of MRs are kept the same. Furthermore, the network throughput improves

significantly as more GRs are deployed. There are two major factors that lead to

such improvement. One is the shorter average path length, i.e., fewer MRs, that each

EU data go through to reach the backbone network. Consequently, serving each EU

requires fewer transmissions and induces less interference, and ultimately uses fewer

radio resources. Accordingly, more EUs can be served simultaneously in the network.

Another key factor to the improved network throughput is the fact that each GR


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5:1

2:1

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80:1

Figure 5.4: Mean PHY transmission rate of successfully received data packets amongMRs under different gateway router topologies. In all cases, the routing metric‘min air-time’ is used.


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1:1

5:1

20:1

80:140:1

Figure 5.5: Mean aggregate number of data time slots used for reception at MRsacross the network under different gateway router topologies. In all cases, the routingmetric ‘min air-time’ is used.


serves fewer MRs on average, and thus each MR interacts with fewer other MRs.

This leads to fewer constraints on the usage of a data time slot at a MR and in turn

to increased reuse of each data time slot. The aggregate number of data time slots

used for reception at MRs across the network shown in Fig. 5.5 indicates the degree

each data time slot is reused across the network. With more deployed GRs, it is seen

that significantly more data time slots can be used simultaneously and each data time

slot can be reused considerably more.

5.2.2 Per-Session Throughput

As discussed in Section 4.9 and seen in Fig. 5.6, the mean per-session throughput

generally decreases with the increasing session arrival rate given the network topol-

ogy and the routing metric. As discussed in Section 4.9.4, the lower per-session

throughput under a higher arrival rate results from a longer session delay, which is in

turn attributed to two main factors: the lower mean PHY transmission rate on data

time slots due to increased interference, and the longer delay in acquiring resources

at MRs due to more EUs competing for the same resources. However, the decre-

ment in the per-session throughput diminishes as the arrival rate keeps increasing

as shown in Fig. 5.6. Fig. 5.6 also demonstrates that for an admitted EU, a stable

mean per-session throughput is provided under each session arrival rate and that the

mean per-session throughput does not diminish even under heavy traffic loads. It

also indicates that the mean per-session throughput considerably improves with more

deployed GRs.

Due to the tree structure of the routing tables, the mean per-session throughput

provided at a MR greatly varies depending on the path length from the MR to its

best GR and the number of MRs served by the same GR as seen in Fig. 4.6 and

Fig. 5.7. Generally, a significantly larger mean per-session throughput is provided

at a MR that is fewer hops away from its best GR. Let MRn denote a MR that is

n hops away from its best GR under the routing metric being considered. In the

case of (#MRs):(#GRs) = 40:1 and λ = 0.05 shown in Fig. 5.7-(a), 1.1 Mbps can be

provided at MR0, i.e., at a MR co-located with a GR, and 0.23 Mbps at MR5. As


0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

0.2

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0.8

1


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HR

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Mbp

s)


10:1

2:1

1:1

5:1

80:140:1

20:1

Figure 5.6: Mean per-session throughput for successfully completed sessions vs. ses-sion arrival rate under different gateway router topologies. In all cases, the routingmetric ‘min air-time’ is used.


0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 50

0.2

0.4

0.6

0.8

1

1.2

1.4(#MRs):(#GRs) = 40:1

HOP

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Mbp

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rate = 0.05rate = 0.1rate = 0.15rate = 0.2

(a)

0 0.5 1 1.5 2 2.5 30

0.2

0.4

0.6

0.8

1

1.2

1.4(#MRs):(#GRs) = 10:1

HOP

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HR

OU

GH

PU

T (

Mbp

s)

rate = 0.1rate = 0.2rate = 0.3rate = 0.4rate = 0.5rate = 0.6

(b)

Figure 5.7: (a) Mean per-session throughput for successfully completed sessions vs.path lengths (hops) under (#MRs):(#GRs) = 40:1; (b) mean per-session throughputfor successfully completed sessions vs. path lengths (hops) under (#MRs):(#GRs) =10:1. In both cases, AC RF and the routing metric ‘min air-time’ are considered.


more GRs are deployed, the per-session throughput provided at each MRn increases

as exemplified in Fig. 5.7-(b) while the overall trend with the increasing session arrival

rate remains the same under each GR topology scenario.

5.2.3 Blocking Rate

As mentioned in Section 4.9.2, the blocking rate is calculated as the ratio of the

number of blocked sessions to the number of arriving sessions. As explained in Sec-

tion 4.8.4, the employed ACC scheme blocks a new EU from accessing the network

if either any of the existing EUs being served at the admitting MR has not been

allocated sufficient resources for forwarding, or there is no resource left at the MR for

receiving data for the new EU. As seen in Fig. 5.8, the blocking rate increases with

the increasing session arrival rate given the network topology and the routing metric.

This monotonicity follows from the fact that under a higher arrival rate, a new EU

arriving to the network sees more EUs on average that are being served at the MR

to associate with, and thus has a higher probability of being blocked. As the network

starts to saturate with the increasing session arrival rate, the blocking rate increases

very sharply as seen in Fig. 5.8. This sharp increase results from the fact that the

number of EUs supportable by the network saturates with the increasing arrival rate.

Eventually, the blocking rate would asymptotically converge to 100%.

We also calculate the blocking rate for each MRn, n ≥ 0. The blocking rate at

each MRn behaves in a more complicated manner than the overall blocking rate.

Fig. 5.9 shows the blocking rates at different MRn’s in the cases of (#MRs):(#GRs)

= 40:1 and 10:1. The blocking rate at MR0 is significantly smaller than those at

MRn, n > 0 in all cases. This can be explained as follows. Recall that destinations of

user data are assumed to be MRs that are co-located with GRs and thus all existing

EUs served by those MRs are assumed to have been allocated sufficient resources for

forwarding. Therefore, blocking at MR0 occurs only when there is no resource left

for receiving data for a new EU. It turns out that MR0 still tends to have a few data

time slots available for reception for a new EU even when the overall blocking rate

becomes as high as 50%.


0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

10

20

30

40

50

60

70


BL

OC

KIN

G R

AT

E (

%)


1:1

5:140:1

80:1

2:1

20:1 10:1

Figure 5.8: Blocking rate vs. session arrival rate under different gateway routertopologies. In all cases, the routing metric ‘min air-time’ is used.


0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 50

10

20

30

40

50

60(#MRs):(#GRs) = 40:1

HOP

BL

OC

KIN

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(a)

0 0.5 1 1.5 2 2.5 30

10

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40

50

60

70

80

90

100(#MRs):(#GRs) = 10:1

HOP

BL

OC

KIN

G R

AT

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(b)

Figure 5.9: (a) Blocking rate at MRs as a function of path lengths from MRs to bestGRs under (#MRs):(#GRs) = 40:1; (b) blocking rate at MRs as a function of pathlengths from MRs to best GRs under (#MRs):(#GRs) = 10:1. In both cases, AC RFand the routing metric ‘min air-time’ are considered.


On the other hand, one has to consider two main factors to understand the block-

ing rates at MRn, n > 0. One factor is the amount of opportunity that MRn acquires

for forwarding data, and the other factor is the amount of radio resources, i.e., data

time slots and the supportable PHY transmission rates over them, that are available

at MRn. Assume that an EU is successfully completed at a MR0 and the MR0 can

now accept new transmission requests from a new EU and/or from its child MR1’s.

If resources are available for its child MR1’s, the child MR1’s would be able to accept

new transmission requests from their respective new EUs or their respective child

MR2’s and so on. Therefore, the chance or opportunity that MRn2 acquires for for-

warding data is smaller than that of MRn1, n1 < n2. On the other hand, due to

the tree structure of the routing tables, MRn2 serves fewer EUs on average and thus

tends to have more available data time slots and higher supportable PHY rates than

MRn1, n1 < n2.

When the mesh size, i.e., the number of MRs, served by one GR is large enough and

the traffic load in the network is light enough so that MRn2 has significantly higher

supportable PHY rates and more available data time slots than MRn1, n1 < n2, it

is possible that MRn2 may transmit more data for a given forwarding opportunity

and thus serve more EUs than MRn1, leading to a smaller blocking rate. This trend

is seen in the case of (#MRs):(#GRs) = 40:1 shown in Fig. 5.9-(a). A similar trend

was observed in the case of (#MRs):(#GRs) = 80:1, not shown here. On the other

hand, when the mesh size served by one GR decreases or the traffic load increases,

the differences in supportable PHY rates and the number of available data time slots

at different MRn’s tend to decrease, and the difference in the amount of opportunity

for forwarding data tends to dominate the blocking rate, leading to a higher blocking

rate at MRn2 than at MRn1, n1 < n2. This trend is clearly seen in the case of

(#MRs):(#GRs) = 10:1 shown in Fig. 5.9-(b) and for smaller mesh sizes not shown

here.


5.2.4 Dropping Rate

As mentioned in Section 4.9.2, the dropping rate is calculated as the ratio of the num-

ber of dropped sessions to the number of admitted sessions. Recall from Section 4.6

that an EU releases a data time slot after a maximum number of unsuccessful re-

transmissions on the slot, and becomes dropped if it runs out of data time slots. The

successive retransmission failures are primarily due to new interfering transmissions

on the same data slot that were not seen when the data slot was assigned to this

new EU. As seen in Fig. 5.10, when the network is lightly loaded, the dropping rate

generally increases with the increasing session arrival rate. This monotonic increase

under light traffic loads follows from two factors: one is that there are more new

transmissions on each data time slot when more new EUs are admitted across the

network under a higher arrival rate. The other factor is that when the network is

lightly loaded, the interference level on a data time slot is low enough so that multiple

MRs across the network may see the same data time slot available and assign the slot

to new transmissions.

As the arrival rate keeps increasing and the network becomes increasingly satu-

rated, the number of new EUs that are admitted at once across the network tends to

saturate as well. Consequently, the probability of a new EU getting dropped due to

additional new transmissions also tends to saturate. Furthermore, as seen in Fig. 5.10,

the dropping rate may even start to decrease with the increasing arrival rate in some

cases. For example, the dropping rate decreases at around λ = 0.5 in the case of

(#MRs):(#GRs) = 10:1, and at around λ = 0.7 in the case of (#MRs):(#GRs) =

5:1. This decrease can be explained as follows. If the mesh size is small enough and

the traffic load in the network is high enough so that the interference level on data

time slots is too high for the same data time slot to be assigned to multiple new

transmissions simultaneously, then, each data time slot assigned to a new EU or new

transmission may not suffer from as much increase in the co-channel interference as

it did under a lower arrival rate, leading to a decrease in the dropping rate. However,

if the session arrival rate still keeps increasing, the dropping rate would eventually

become rather constant with the increasing arrival rate because the employed ACC

policy would have the number of admitted EUs and the interference level in the


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5

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15


DR

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%)


2:1

10:1

20:1

40:1

5:1

1:1

80:1

Figure 5.10: Dropping rate vs. session arrival rate under different gateway routertopologies. In all cases, the routing metric ‘min air-time’ are considered.


0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 50

5

10

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(a)

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8

10

12

14

16

18

20(#MRs):(#GRs) = 10:1

HOP

DR

OP

PIN

G R

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(b)

Figure 5.11: (a) Dropping rate at MRs as a function of path lengths from MRs tobest GRs under (#MRs):(#GRs) = 40:1; (b) dropping rate at MRs as a function ofpath lengths from MRs to best GRs under (#MRs):(#GRs) = 10:1. In both cases,AC RF and the routing metric ‘min air-time’ are considered.


network remain rather constant with the increasing arrival rate eventually.

We also calculate the dropping rate for each MRn, n ≥ 0. Fig. 5.11 shows

the dropping rates at different MRn’s in the cases of (#MRs):(#GRs) = 40:1 and

10:1. The dropping rate at MRn2 tends to be smaller than that at MRn1, n1 < n2.

This is because MRn2 tends to have more available data time slots on average than

MRn1, n1 < n2, and thus a data time slot chosen for a new EU at MRn2 tends to

have a lower interference level under the employed data time slot selection algorithm

LIF-S that selects the available data time slot with the lowest interference level. The

reason why the dropping rate at MR0 is noticeably higher than those at MRn, n > 0

is that the blocking rate at MR0 is significantly smaller than those at MRn, n > 0,

and thus an admitted EU tends to get assigned data time slots with far stronger inter-

ference. As the difference between the blocking rate at MR0 and those at MRn, n > 0

increases in a more congested condition over the ranges of arrival rates considered in

this work, the dropping rate at MR0 tends to become increasingly poorer compared

to those at MRn, n > 0, as shown in Fig. 5.11.

5.3 Scalability with Different Routing Metrics

In this section, we present the network performance under the two different routing

metrics, ‘min hop’ and ‘min air-time’, discussed in Section 4.7.4, and examine the

scalability behavior of the performance under two different GR topologies. Recall from

Section 2.3 and Section 4.7 that once routing tables are generated at the beginning

of a simulation run under each network topology and the routing metric, they remain

the same throughout the run. Major differences under the two routing metrics are the

structure of the mesh served by each GR as shown in Fig. 5.2, and the distribution of

the received signal strength and supportable PHY transmission rates over neighbor

MR links as seen in Fig. 5.13.


5.3.1 Network Throughput

Fig. 5.12 shows the mean network throughput under the two different routing metrics,

‘min air-time’ and ‘min hop’, in the cases of (#MRs):(#GRs) = 40:1 and 80:1. In

both topology scenarios, the mean network throughput is higher under ‘min air-time’.

The difference in the mean network throughput under the two routing metrics can

be explained with two main factors: the mean PHY transmission rate and the mean

number of hops along routing paths. Generally, under ‘min air-time’, the mean PHY

transmission rate is higher as shown in Fig. 5.13 and the mean path length is longer.

As seen in Fig. 5.2, the mean path length from MRs to their best GRs is 2:16 under

‘min hop’ and 2.51 hops under ‘min air-time’ with (#MRs):(#GRs) = 40:1, and is

2.74 under ‘min hop’ and 3.22 under ‘min air-time’ with (#MRs):(#GRs) = 80:1.

When the difference between the two routing metrics in the mean PHY transmission

rate dominates, the network throughput improves, and when the difference in the

mean path length dominates, the network throughput worsens. The higher network

throughput under ‘min air-time’ in both topology scenarios implies that the difference

between the two routing metrics in the mean PHY transmission rate dominates. The

improvement in the network throughput under ‘min air-time’ is smaller in the case

of (#MRs):(#GRs) = 80:1. This is partly because under (#MRs):(#GRs) = 80:1,

the difference between the two routing metrics in the number of hops along routing

paths increases while the difference in the mean PHY transmission rate remains rather

similar, compared to the case of (#MRs):(#GRs) = 40:1.

5.3.2 Per-Session Throughput

As seen in Fig. 5.14, the per-session throughput is higher under ‘min hop’ when the

network is lightly loaded. This is because the mean supportable PHY transmission

rate is high enough in a lightly loaded condition that the time in transmitting data

is comparable to the delay in acquiring resources through the negotiation process

described in Section 4.2. As the network becomes more congested, the mean sup-

portable PHY transmission rate decreases and the time in transmitting data becomes

longer and thus more dominant compared to the delay in acquiring resources. This


0 0.05 0.1 0.15 0.2 0.25350

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(#MRs):(#GRs) = 80:1, min_air−time(#MRs):(#GRs) = 80:1, min_hop(#MRs):(#GRs) = 40:1, min_air−time(#MRs):(#GRs) = 40:1, min−hop

40:1

80:1

Figure 5.12: Mean network throughput vs. session arrival rate under the two differentrouting metrics considered and AC RF.


0 0.05 0.1 0.15 0.2 0.2510

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Figure 5.13: Mean PHY transmission rate of successfully received data packets amongMRs under the two different routing metrics considered and AC RF.


0 0.05 0.1 0.15 0.2 0.250

0.05

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Figure 5.14: Mean per-session throughput for successfully completed sessions vs.session arrival rate under the two different routing metrics considered and AC RF.

explains why the difference in the per-session throughput decreases as the arrival rate

increases in each GR topology scenario over the range shown in Fig. 5.14.

5.3.3 Blocking and Dropping Rates

The blocking rate shown in Fig. 5.15-(a) is smaller under ‘min air-time’ in both topol-

ogy scenarios but the difference is not significant, which can be inferred from the

difference in the mean network throughput in Fig. 5.12. On the other hand, the

dropping rate shown in Fig. 5.15-(b) is generally found larger under ‘min air-time’

when the network is saturated. Recall from Section 5.2.4 that dropping occurs mainly

due to new transmissions that were not seen at the time of resource allocation. A


0 0.05 0.1 0.15 0.2 0.250

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5

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80:1

40:1

(b)

Figure 5.15: (a) Blocking rate vs. session arrival rate; (b) dropping rate vs. sessionarrival rate under the two different routing metrics considered and AC RF.


higher network throughput and a larger number of hops along routing paths un-

der ‘min air-time’ result in more new transmissions per time slot and thus a higher

dropping rate.

5.4 Chapter Summary

In this chapter, we determined the performance of the WMN with focus on the

scalability under different scenarios of network topology and routing metric. We first

varied the number and locations of GRs deployed in the network while holding the

total number and locations of the MRs constant and examined the scalability behavior

of several fundamental performance metrics including the network throughput, per-

session throughput, and blocking and dropping rates. We identified major factors

that affect the scalability behavior and showed that the PHY, MAC and routing

layers of network functions interact in a complicated manner with one another to

determine the network performance under each simulated scenario. Those factors

include the co-channel interference that determines supportable PHY transmission

rates; the constraints that the structure of routing tables imposes on the usage of radio

resources at MRs; and the complicated interactions between these factors. Especially,

those factors that result from the multi-hop nature of WMNs were seen to severely

limit the network throughput.

With more deployed GRs, i.e., more backbone support to the network, the network

throughput and per-session throughput were shown to improve significantly, and the

improvement was explained based on the aforementioned interactions across the layers

of network functions. The impact of two different routing metrics on the overall

network performance was also considered. For the network topologies and network

configurations considered in this chapter, the impact of the two different routing

metrics was found insignificant compared to those resulting from the two different

admission and congestion control policies studied in Chapter 4 or different numbers

of GRs deployed in the network.

Chapter 6

Conclusions

6.1 Thesis Summary

In this thesis, we evaluated the performance of WMNs that serve as access networks

over large geographic areas, based on more realistic models for physical and network-

ing layers of network functions that have been often oversimplified in the literature.

We first created a set of new MAC protocols that incorporate such models for the

WMNs. We also developed a large WMN simulator that implements the protocols

and incorporates measurements-based models for radio propagation and interference

calculation for a large built-in urban area. The simulator also captures the stochastic

network behavior resulting from random traffic arrivals, admission control, and queue-

ing. Through extensive simulations incorporating such details, we determined the per-

formance of the WMNs. We investigated the behavior of fundamental performance

metrics including the network throughput, per-session throughput, and blocking and

dropping rates. We identified major factors that affect the performance behavior and

showed that the PHY, MAC and routing layers of network functions interact with

one another in a complicated manner to determine the network performance under

each simulated scenario.

Chapter 2 described the WMN studied in this thesis including its network architec-

ture and envisioned application scenario. The TDMA/TDD-based MAC framework

and the overall network operations within the framework were also illustrated. The

113

CHAPTER 6. CONCLUSIONS 114

WMN simulator was also described including the radio propagation environment and

its simulation models and methodology, PHY considerations, user traffic model and

parameters, and the overall simulation flow.

Chapter 3 proposed a control time slot assignment protocol through which every

MR in the WMN acquires a broadcast time slot that supports a minimum average

received SINR from the MR to all of its neighbor MRs. The protocol incorporates

measurement-based models for radio propagation and interference calculation and

operates in a fully distributed manner. The basic mechanisms and full operations of

the protocol were illustrated along with selection criteria of the protocol parameters

and other design considerations including protocol initialization, deadlock resolution,

and conditions for transmitting NACK packets. Furthermore, a power control scheme

was introduced that allows better utilization of resources for maintaining control time

slots. Extensive simulation results were presented and discussed: the protocol was

shown to support the target minimum average received SINR over all neighbor pairs

of MRs in all simulated scenarios. In addition, the sensitivities of the protocol to

the amount of shadowing of the radio propagation and to the GR topology were

determined. The benefit of the power control scheme was also demonstrated.

In Chapter 4, we developed a protocol that controls the medium access over data

time slots. The protocol utilizes control time slots and operates in a fully cooperative

and distributed manner. Furthermore, the protocol supports adaptive resource allo-

cation through dynamic allocation of data time slots and PHY transmission modes

over the slots as well as through user/queue prioritization. We introduced an ACC

scheme that stabilizes the network under heavy traffic loads, and yet utilizes only

local information available at the admitting router and has a minimal increase in

control overhead. Key elements of the protocol were described including resource

negotiation mechanisms, data time slot selection, queue/session prioritization, data

transmission/retransmission, and resource release. The employed routing protocol

was also presented including its operations and routing metrics. Extensive simulation

results were presented and discussed. Several fundamental performance metrics were

examined including the network throughput, per-session throughput and blocking

and dropping rates. The impact of two ACC schemes on the network performance


were compared and discussed. Particularly, the ACC scheme AC RF was shown to

stabilize the network even under heavy traffic loads unlike the ACC policy AC RO

which becomes unstable at heavy traffic loads.

In Chapter 5, we determined the performance of the WMN with focus on the

scalability under different scenarios of network topology and routing metric. We

first varied the number and locations of GRs deployed in the network and examined

the scalability behavior of fundamental performance metrics including the network

throughput, per-session throughput, and blocking and dropping rates. We identified

major factors that affect the scalability behavior and showed that the PHY, MAC

and routing layers of network functions interact intricately with one another to de-

termine the network performance under each simulated scenario. We demonstrated

that with more deployed GRs, i.e., more backbone support to the network, the net-

work throughput and per-session throughput improve significantly, and explained the

improvement based on the aforementioned interactions across the layers of network

functions. We also studied the impact of two different routing metrics on the overall

network performance. For the network topologies and network configurations con-

sidered in this chapter, the impact of the two different routing metrics was found

insignificant compared to those resulting from the two different admission and con-

gestion control policies studied in Chapter 4 or different numbers of GRs deployed in

the network.

6.2 Contributions

The major contributions of the work in this thesis can be summarized as follows.

• We quantified the performance of WMNs that serve as access networks over large

geographic areas based on measurement-based models for radio propagation

and interference calculation and on more detailed interactions among network

entities, often oversimplified in the literature due to the prohibitive complexity

of analysis and simulations [36,37,42].

• We determined the scalability behavior of the WMN under different scenarios


of network topology and routing metric. Specifically, we varied the number and

locations of GRs deployed in the network while holding the locations and total

number of MRs constant, and also considered two different routing metrics. We

investigated the behavior of several fundamental performance metrics including

the network throughput, per-session throughput, and blocking and dropping

rates, and identified major factors and their interactions with one another that

affect the network performance and scalability behavior [37, 42].

• We created an ACC scheme for the WMN that stabilizes the WMN even under

heavy traffic loads. The ACC scheme incorporates the resource availability at

the intermediate MRs along the path to the destination MR, and yet utilizes

only local information available at the admitting MR and has a minimal increase

in control overhead [37].

• We developed a control time slot assignment protocol through which every MR

in the WMN acquires a broadcast time slot that supports a minimum aver-

age received SINR from the MR to all of its neighbor MRs. The protocol

incorporates measurement-based models for radio propagation and interference

calculation which are often oversimplified in the literature, and operates in a

fully distributed manner [43].

• We created a protocol that controls the medium access over data time slots for

the WMN. The protocol provides mechanisms for network entities (MRs and

EUs) to negotiate and allocate resources in a fully cooperative and distributed

manner. Moreover, the protocol supports adaptive resource allocation through

dynamic allocation of data time slots and PHY transmission modes over the

slots as well as through user/queue prioritization [36,37].

• We developed a high-fidelity and large-scale WMN simulator that incorporates

measurement-based models for radio propagation and interference calculation

for a large built-in urban area. The simulator also captures the stochastic

network behavior resulting from random traffic arrivals, admission control, and

queueing. Furthermore, it employs a parallel-processing simulation technique


that addresses the size and computational complexity of the simulator [36, 37,

42–44].

• We created a parallel processing technique for time-driven simulation of large

and complex wireless networks with substantial PHY details such as radio prop-

agation and interference. Running over a supercomputing platform that com-

prises multiple processors with large high-speed memory and interconnected

with high-speed links, the technique was demonstrated to reduce runtimes sig-

nificantly for two different wireless network simulators [44, 45].

6.3 Future Work

The study performed in this thesis can be extended in various directions. Below is a

partial list of such extensions.

• In all simulations presented in this thesis, we placed MRs at street corners as

illustrated in Section 2.4. We can place them along city blocks, e.g., at mid-

points of block sides. The neighborhood topology of each MR would then be

changed. For example, each MR would now have only one LOS street and would

thus have fewer neighbor MRs on average if other conditions remain the same.

On the other hand, the amount of interference each MR would impose on nearby

MRs would be reduced due to the corner effect mentioned in Section 2.4.2. It is

however not clear how these factors altogether would affect the overall network

performance.

• In Section 4.3, we examined two different algorithms for data time slot selection,

namely, EIF-S and LIF-S. We can consider different algorithms, and determine

their impact on the overall network performance and compare it to that pre-

sented in this thesis.

• In Section 4.4, we considered one user/queue prioritization scheme, i.e., a first-

come-first-served policy with respect to the network admission time of the user.

We can adopt different strategies that utilize additional information such as the


data size that each user requests of the network, the current queue size of the

user at each MR, etc. We could also optimize the queue prioritization jointly

with the data time slot selection algorithm in Section 4.3.

• In Section 4.7.4, two routing metrics were considered for the employed routing

protocol. Although both of them incorporate the underlying network topol-

ogy and one of them integrates the different levels of received signal strengths

over different pairs of neighbor MRs, both metrics do not incorporate the in-

stantaneous traffic status. Moreover, either of the metrics does not include the

processing delay and the medium access acquisition delay at intermediate MRs.

We can consider different routing metrics that integrate instantaneous traffic

statistics of the network as well as the delays at intermediate MRs.

• Throughout this thesis, we considered only best-effert (i.e., without throughput

or delay constraints) web traffic as described in Section 2.6. Different user traffic

types can be considered. One can also consider supporting a mixture of different

traffic types. Delay-sensitive traffic types such as voice or video would impose

an additional set of challenges on the protocol design. Supporting a different

traffic type would require various elements of the data time slot access control

protocol related to resource negotiation and allocation to be modified.

• For the same network topology and configurations, we could implement other

types of MAC protocols that are based on contention-based approaches, e.g., the

IEEE 802.11 MAC protocol, and compare their performance to that presented

in this thesis.

Appendix A

Parallel Time-Driven Simulation

A.1 Introduction

Wireless networks are growing rapidly in both size and complexity, and it is becom-

ing increasingly difficult to investigate such large and complex networks analytically.

Powerful computer simulation tools are thus turning indispensable in studying such

networks. However, accurate and realistic simulation of large wireless networks incor-

porating all relevant models requires significant computing power such that runtimes

on the order of tens of days are needed for a single network configuration and param-

eter set.

In order to reduce the execution time of large wireless network simulation, there

have been several studies for parallelizing the simulation using discrete event simu-

lation (DES) techniques [46–55]. WiPPET [46, 47] is a parallel simulation testbed

for wireless mobile networks. It partitions the network into zones and distributes the

task of received power calculation among them. As a result, it was able to obtain a

speedup gain of almost 6 for 8 processors in some scenarios. However, when the net-

work was partitioned geographically, essentially no speedup gain was achieved due to

substantial communication overhead among zones. SWiMNet [48] is another parallel

simulation framework for wireless mobile cellular networks. It divides the network

based on cells and assigns cells to logical processes (LPs). LPs then execute the sim-

ulation in parallel utilizing pre-computed events. For a simple channel assignment

119

APPENDIX A. PARALLEL TIME-DRIVEN SIMULATION 120

scheme, the simulator was able to achieve a speedup gain of 12 using 16 processors.

However, speedup gains remain unclear for channel assignment schemes which en-

tail interactions among LPs. Moreover, the simulator does not include radio signal

propagation and thus does not consider the communication overhead among LPs re-

quired for calculating received signal and interference levels. The parallel version [49]

of GloMoSim [50] is yet another example of a parallel wireless network simulator.

Work [49] proposes optimization techniques to reduce communication and synchro-

nization overheads for parallel simulation based on DES techniques, and demonstrates

performance improvements.

For time-driven wireless network simulation, however, there have been few studies

on parallelization techniques. Time-driven simulators are particularly useful when

details of the physical layer characteristics and behavior are to be incorporated into

the simulator because the large amount and high frequency of events from the de-

tailed models would lead to too heavy implementation overhead for event-driven sim-

ulators, as demonstrated in [47, 49]. Because parallelization techniques based on

event-driven simulation do not directly apply to time-driven simulators, we present a

new parallelization technique for time-driven simulators of large and complex wireless

networks [44,45].

Similar to the works previously mentioned, the technique partitions the network

into subareas and assigns one processor to each subarea. Yet it focuses on time-

driven simulation with substantial PHY details such as radio signal propagation and

interference. We identify issues related to the time-driven nature of the simulation

in such key areas as database synchronization, database design, and inter-processor

communication, and create schemes in those areas for effective and efficient paral-

lelization. The enormous computational complexity and communication overhead

for incorporating such details is addressed by a unique computing platform which

comprises multiple processors with large high-speed memory and interconnected with

high-speed links. Such a computing platform has been made possible thanks to recent

advances in supercomputing blade technologies and parallel processing techniques.

The remainder of this appendix is organized as follows. In Section A.2, we describe

the simulation platform and illustrate the principles and issues of the parallelization


technique. In Section A.3, the technique is applied to two different types of wireless

networks, i.e., circuit-oriented mobile cellular networks with comprehensive resource

management schemes and packet-oriented large WMNs investigated in this thesis,

and is shown to significantly reduce the runtimes. Finally, Section A.4 summarizes

the appendix with final remarks.

A.2 Principles of the Technique

There are three major issues to consider in developing a parallel simulator: workload

partitioning, database synchronization among multiple processors, and data structure

design of the simulator. In this section, we discuss each of the issues in detail. We

first describe our simulation platform.

A.2.1 Simulation Platform

We use a modular supercomputing platform comprising 32 AMD Opteron 64-bit

processors running at a 2.0 GHz clock speed with 2 GB memory each. Groups of

4 processors are interconnected via 2 GB/s Infiniband links and processors within a

group are connected via 8 GB/s links. Fig. A.1 shows our supercomputing platform.

On the platform, we use a version [56] of Message Passing Interface (MPI) which is

a widely used standard library for parallel processing.

A.2.2 Workload Partitioning

Overview

To gain from parallel processing, workload should be efficiently distributed among

multiple processors. For simulating a large wireless network, two general approaches

can be considered [47]: geography-based partitioning and channel-based partitioning.

In geography-based partitioning, we divide the network into subareas and assign one

processor to each subarea. In this case, because co-channel interferers are handled by

multiple processors, processors have to exchange co-channel interference information


Figure A.1: Our supercomputing platform

among themselves. In channel-based partitioning, on the other hand, processors are

assigned a subset of channels and co-channel interferers on each channel are han-

dled by a single processor. Operations across different subsets of channels assigned

to different processors are handled by a more powerful processor, called a master

processor.

Geography-Based Partitioning

Fig. A.2 illustrates the primary communication pattern among processors performed

in one simulation time step under both types of workload partitioning. Under geography-

based partitioning in Fig. A.2-(a), processors first process their assigned set of network

entities independently without communicating with one another. Once all the pro-

cessors complete updating the network entities, they collectively communicate with

one another to exchange the updates. These exchanged updates are mainly related to

updated interference information, e.g., changes in locations of mobile users, changes

in transmit power levels, etc., which has to be known to other processors before the


...P1 P2 P3 Pn

...P1 P2 P3 Pn

Master Processor

...P1 P2 P3 Pn

...P1 P2 P3 Pn

(a) (b)

Figure A.2: Primary communication pattern among processors in one simulationtime step: (a) geography-based; (b) channel-based workload partitioning. Pi denotesa processor.

processors proceed to the next time step. The size of each subarea is calculated

proportional to the workload in the subarea, primarily the traffic load, so that work-

load is distributed uniformly among processors. For example, for a uniform traffic

load, subareas are equally sized. In the current version of the technique, workload

is distributed at the beginning of simulation according to the input traffic load, and

remains the same throughout the simulation. That is, workload partitioning is not

adaptive to varying network conditions over the course of simulation.

The interference information exchange among processors in every time step incurs

considerable communication overhead among processors, and it may overshadow the

gain from distributing workload among processors as reported in [47, 49]. It is thus

critical to have fast communications links among processors to gain from geography-

based workload partitioning. Thanks to the high-speed Infiniband links, the technique

implemented in this thesis, which employs geography-based workload partitioning,

was indeed able to achieve significant speedup gains as demonstrated in Section A.3.

Channel-Based Partitioning

In channel-based partitioning illustrated in Fig. A.2-(b), processors first update the

network entities on their subset of channels independently, similar to the case of


geography-based partitioning. Once they complete updating, all the processors trans-

fer the updates to the master processor. The master processor then performs oper-

ations across different subsets of channels such as handoff, channel selection for new

users, etc. based on the updates from the processors, and finally broadcasts the

results back to the processors. We note that since co-channel interferers on a chan-

nel are handled by a single processor, operations performed by different non-master

processors are orthogonal; thus, they do not need to exchange interference informa-

tion among themselves directly. They communicate only with the master processor

for inter-processor operations. As a result, the approach incurs less communication

overhead than the geography-based approach.

Because this partitioning approach places significantly more workload on the mas-

ter processor, it is more appropriate when one processor is more powerful than the

others. In addition, this approach may not be applicable to a wireless network in

which orthogonal channels are not clearly defined such as one running the IEEE

802.11 MAC protocol on a single frequency channel. Moreover, parallelization gain

may be limited when the number of orthogonal channels available in the simulated

network is less than that of available processors.

Adopted Scheme

Considering our computing platform with 32 equal processors and a very fast commu-

nication architecture among the processors, we choose a geography-based partitioning

approach: the network is divided into subareas and each subarea is handled by one

processor. Network entities are then assigned to one of these processors. The as-

signment is primarily based on their geographical locations: an entity is assigned to

the processor that handles the subarea where the entity is located. However, users

are typically assigned to the processor that handles the network entity with which

the users are primarily associated, e.g., base stations in a cellular network. This is

because if users were assigned to a processor according to their locations, any data

exchange between a user and its associated network entity located in a different sub-

area would have to be transferred between the two corresponding processors resulting

in large inter-processor communication overhead. Section A.2.4 provides more details


on designing the database according to the chosen geography-based partitioning.

A.2.3 Synchronization

Since the database of a parallel simulator is updated by multiple processors simulta-

neously, it is critical to properly synchronize the database update among processors.

In a single processor sequential program, any update is immediately written to the

database and the processor instantly refers to the updated database for subsequent

operations; in this way, the synchronization is handled trivially. In a parallel program,

on the other hand, when a processor handles its assigned set of network entities during

a simulation time step, it does not have access to the updates that other processors

make during the same simulation time step until the processors communicate with

one another. This limited access leads to two major considerations: one is to main-

tain a duplicate of part of database for intra-processor updates, and the other is to

transfer updates for inter-processor updates.

Intra-Processor Synchronization

To manage intra-processor synchronization, we maintain a duplicate of the part of

database which is constantly referred to and updated by other processors, e.g., the

part which contains interference related information. With the duplicate, processors

refer to the original database when computing updates, and write the updates onto

the duplicate. Consequently, the original database remains intact over one simulation

time step and the processors are ensured to be referring to the identical database.

Keeping the duplicate is necessary because if we let processors immediately write

updates to the database as in the sequential program, the replicas of the database in

different processors would become dissimilar and even inconsistent from one another

over one simulation time step. As a result, processors could operate on incompatible

replicas of the database.

In maintaining the duplicate, it is critical to ensure that a particular resource,

e.g., a frequency channel, which is found available at the beginning of a simulation

time step is not allocated to multiple network entities over the same time step. To


resolve such conflicts internal to a single processor, the processor checks the resource

availability in the duplicate database as well as the original one when computing

updates. If a conflict occurs between different processors, the involved processors

collectively resolve it as explained below.

Finally, we note that any part of a database which is accessed by only one processor

does not need a duplicate. More details on database design in consideration of the

duplicate maintenance are described in Section A.2.4.

Inter-Processor Synchronization

Inter-processor synchronization deals with situations in which a processor needs to

update a part of database assigned to other processors. Typically, these updates

are made at boundaries of subareas where adjacent network entities interacting with

each other may be located in different subareas handled by different processors. For

example, in a mobile cellular network, a base station may have to hand-off a user

located close to a subarea boundary to another base station outside of the subarea

which the sending base station belongs to. Or in a multi-hop wireless network, a

router near a boundary may have to transmit a packet to an adjacent router in

another subarea. These updates are stored in an additional data structure. More

details on database design for inter-processor updates are provided in Section A.2.4.

Once processors finish updating their assigned network entities, they transfer the data

structure of inter-processor updates to corresponding destined processors. Destined

processors then incorporate the transferred updates into their updated database.

When incorporating updates transferred by different processors during a simula-

tion time step, there may occur conflicts in which the same resource is claimed by

multiple network entities. This type of conflict seems inherent in any parallel pro-

gram in which more than one processor can modify the same part of database. Such

a conflict has to be resolved depending on the context of the simulated network. For

example, consider a mobile cellular network and a user in a subarea handed-off to

a base station in another subarea. In that case, a simple scheme can be devised as

follows. When a destined base station processes the transferred users from other sub-

areas in other processors, it checks for each of the users whether the initially assigned


channel has been taken by other users during the same simulation time step. If not,

it simply confirms the assignment and updates the database accordingly. Otherwise,

it attempts another assignment process to find a new channel. If a channel is found,

the new channel is assigned to the user. Otherwise, the user is blocked or dropped.

A.2.4 Database Design

The entire database of the simulator can be divided into four groups, and Fig. A.3

illustrates the different groups for an example of two processors.

DB unchanged

This group includes those data structures which are determined during the initializa-

tion phase of simulation and do not change afterwards, e.g., shadowing maps which

store shadowing values for a set of geographical locations across the simulated uni-

verse. All processors have the same copy of this part and thus, do not exchange it

among themselves. With N processors involved, the parallel simulator has N copies of

this part across the processors; hence, the memory overhead is linear in the number

of processors.

DB single

This group contains those data structures which are updated as simulation evolves

but by only one processor. Any data structure that other processors do not need to

access for their operations belongs to this part of database. For example, in a wireless

mesh network, a routing table of a wireless router may not be needed or accessible

by other routers. In that case, a processor does not exchange with other processors

the routing tables of the routers assigned to itself. Note that this part of database is

distributed among processors according to the workload partitioning scheme discussed

in Section A.2.2. For equal-sized subareas, processors hold 1/N of the total for N

processors involved. Regardless of the partitioning strategy, the aggregate size across

the processors remains constant for different numbers of processors.


computation phase

P1

DB_request_send (p1)

DB_request_rcv (p1)

DB_unchanged

DB_single (p1)

DB_multiple

DB_multiple_dup

P2


DB_request_rcv (p2)

DB_unchanged

DB_single (p2)

DB_multiple

DB_multiple_dup

DB_request_rcv (p1)


DB_request_rcv (p2)


DB_multiple

DB_multiple_dup

DB_multiple

DB_multiple_dup

inter-processor update transfer

duplicate DB exchange

communication phase

Figure A.3: Database structure and operations executed in one simulation time stepfor an example of two processors. A computation phase is followed by a communica-tion phase.


DB multiple and DB multiple dup

This group includes segments which are referred to and updated by multiple pro-

cessors in each simulation time step. The data are mainly related to calculating

co-channel interference. Every processor has the same copy of this group, and thus,

for N processors used, the parallel simulator has a total of N copies across proces-

sors, resulting in a linear memory overhead in the number of processors. The shaded

region within DB multiple denotes the subarea that the corresponding processor han-

dles. For example, processor p1 handles the first half and processor p2 processes the

second half. Moreover, as explained in Section A.2.3, processors keep a duplicate

of this group, denoted as DB multiple dup in Fig. A.3. When a processor needs to

update part of DB multiple assigned to itself, it writes the update onto the corre-

sponding location in DB multiple dup. As a result, the same copy of DB multiple is

referred to by all processors during each simulation time step.

It is important to design this part of database to be amenable to exchange among

processors. We choose to store each type of network entity into an array according

to the subarea in which the entities are assigned because arrays are very efficiently

exchanged among processors using MPI [56], a standard library for parallel processing

used in this parallel simulation platform. Consider a 2-processor parallel simulator,

for example. For simulating a cellular network, we create an array with 2 entries and

each entry contains base stations located in one subarea. Similarly, for simulating a

wireless mesh network, wireless mesh routers are stored into an array according to

their locations in the network. While each processor keeps all the entries of such an

array in its memory, it updates only the entry corresponding to the subarea assigned

to itself.

DB request send and DB request rcv

The last group contains the data structures for storing and transferring inter-processor

updates discussed in Section A.2.3. Each type of update is stored into an array, simi-

lar to the data structures in DB multiple discussed above in this section. Each entry

of such an array corresponds to one destined processor. We note that the entry


size of an array for each type of update has to be determined from the frequency of

such updates between two processors over one simulation time step. For example,

for storing data packets in a wireless mesh network to be transferred to one destined

processor, each entry needs to be large enough to account for the number of such data

packet transmissions between two adjacent routers and the number of routers near

the subarea boundary between the two corresponding processors. We finally mention

that the size of such an array combined across the processors tends to increases with

the number of processors because more network entities are near subarea boundaries

as the size of each subarea shrinks with more processors. Consequently, the commu-

nication overhead of transferring these data structures tends to increase with more

processors for a given simulated network.

A.2.5 Examples

Interference Calculation

When a processor calculates the received co-channel interference level at a receive

node in its subarea, it refers to DB multiple for transmission status, transmit power

level, geographical location, etc. to compute the path-loss from each of the co-channel

interferers in the simulated universe. Recall that transmission related information for

all network entities is kept in DB multiple of each processor. The shadowing value,

on the other hand, is determined based on the shadowing maps in DB unchanged

which is also held in each processor.

Inter-Processor Updates

Consider a wireless router transmitting a data packet to another router in a subarea

handled by another processor. The router will store the data packet into an entry

in DB request send corresponding to the destined processor. There may be multiple

such data packets to be transferred to other processors when all the routers in a

subarea complete transmitting data packets. Processors wait until all routers in all

processors finish updating. Then, they transfer DB request send to DB request rcv in


destined processors. Upon receiving updates from other processors, destined proces-

sors process and incorporate the updates into their database. For example, a router

receiving a data packet from another router in a different processor would update the

queue size and other related information accounting for the newly arrived packet.

A.2.6 System Routine

Given the workload partitioning, synchronization schemes, and database structure

discussed so far, operations executed in each simulation time step can be grouped

into two groups. The first group forms a computation phase where each processor

independently processes its assigned set of network entities, and the second group

comprises a communication phase where all the processors collectively communicate

with one another to exchange updates made during the computation phase. Fig. A.3

illustrates the flow of operations performed in one simulation time step for an example

of two processors.

Arrows in the figure between different parts of database and processors indicate

the flow of read/write operations: processors refer to DB unchanged, DB single, and

DB multiple when computing updates for their assigned subarea; processors write

updates into the corresponding location within DB single and DB multiple dup, and

DB request send for intra- and inter-processor updates, respectively. In the subse-

quent communication phase, processors transfer DB request send to DB request rcv

of other processors and, incorporate the inter-processor updates into their database.

Then finally, they exchange DB multiple dup with one another to update interference

related information made over the current simulation time step. In the next time step,

the entire routine is repeated with the updated database.

A.3 Numerical Results

In this section, we present and discuss numerical results from two different simulators

that employ the technique illustrated in this appendix. One is a mobile cellular

network simulator developed in [25, 26] as a sequential program and modified in [45]


for parallel processing. The other is a large WMN simulator created for this thesis.

The WMN simulator was created as a parallel program from the beginning.

A.3.1 Mobile Cellular Network Simulator

The simulator is developed to examine the effects of various dynamic schemes for

channel allocation, power control, and adaptive antennas on system performance.

The simulated network consists of a 16 x 16 grid of base stations with 850 m grid

spacing in a toroidal universe in which a radio signal propagating out of the universe

reappears at the opposite edge and continues to propagate in the same direction.

Each base station can access up to 128 channels. Signal propagation is modeled by

path-loss and correlated shadowing. Interference from all co-channel interferers is

included in calculating the received SINR.

The two lower sets of curves in Fig. A.4-(a) show the runtimes of the parallel pro-

gram versus the number of processors for two systems with different computational

complexity. Each runtime is obtained from one long simulation run. One system em-

ploys a channel assignment scheme, called autonomous reuse partitioning (ARP); the

other system incorporates not only the channel assignment scheme but also schemes

for power control and beamforming with multiple antennas, designated in the figure

as PC and OB, respectively. Each system was run under various network traffic load

levels, measured in Erlangs (Erl) per base station. The runtimes with a single pro-

cessor were obtained from the existing sequential simulator. We first note the huge

difference in runtimes between the two systems. For the more complex system, one

simulation run on a single processor took longer than 7 days. On the other hand, most

of the runs for the system of lighter complexity took less than one day on a single

processor. This difference in runtimes is mainly due to the different computational

complexity and simulated traffic load levels; the more complex system was simulated

under much heavier traffic loads. The more complex system also requires more time

to stabilize the statistical variation in the results.

The runtime behavior of the parallel simulator is more clearly seen in Fig. A.4-

(b) where the speedup gain versus the number of processors is presented. For the


0 5 10 15 20 25 30 350

2000

4000

6000

8000

10000

12000

number of processors

run

tim

e (m

inu

tes)

cellular, Erl 15, ARPcellular, Erl 20, ARPcellular, Erl 25, ARPcellular, Erl 30, ARPcellular, Erl 35, ARP+PC+OBcellular, Erl 40, ARP+PC+OBWMN

4 days 2 hrs

2 days 2 hrs

17 hrs

6 days 22 hrs

7 days 17 hrs

1 day 5 hrs18 hrs 14mins

6 hrs 30 mins

(a)

0 5 10 15 20 25 30 350

5

10

15

20

25

30

35

number of processors

spee

du

p

cellular, Erl 15, ARPcellular, Erl 20, ARPcellular, Erl 25, ARPcellular, Erl 30, ARPcellular, Erl 35, ARP+PC+OBcellular, Erl 40, ARP+PC+OBWMN

(b)

Figure A.4: Performance of the parallel simulation technique for a mobile cellularnetwork simulator and a WMN simulator: (a) runtime vs. number of processors; (b)speedup gain vs. number of processors.


more complicated system, the speedup gain is almost linear up to 8 processors and

noticeably less than linear but more than 11 with 16 processors. The almost linear

gain implies that the inter-processor communication overhead for the parallel pro-

cessing is almost negligible as compared to the computation load. On the other hand,

the speedup gain for the less complicated system is far less than linear. This rather

low gain suggests that the communication phase in the system routine is compara-

ble to the computation phase so that the gain from distributing the computational

workload among processors is offset by the communication overhead. We also note

the increased runtime (or speedup less than one) with the parallel processing in the

case of 15 Erlangs. In this simulation scenario, the network is very lightly loaded

so that the computation workload is minimal and the inter-processor communication

overhead tends to dominate the overall runtime, resulting in a longer runtime with

the multiple processors. This observation is, in fact, consistent with the results of

the parallel version of GloMoSim given in [49]. It is thus seen that a more complex

program benefits more from the parallel processing.

Fig. A.5 plots grade of service (GOS) values for each simulation run. GOS is a

combination of blocking and dropping rates and is approximately the sum of the two

rates. Although GOS is observed to be consistently a little higher for the parallel

program, the increase due to the database synchronization discussed in Section A.2.3

is negligible. Overall, the GOS results of the parallel program are consistent with

those of the sequential program in all simulated scenarios.

Example: Capacity of the Mobile Cellular Network Employing Optimal

Integrated Resource Allocation Schemes

We now give a set of numerical results on the capacity of the simulated mobile cel-

lular network that employs the integrated resource allocation schemes, i.e., with a

dynamic channel assignment scheme called autonomous reuse partitioning (ARP),

power control and optimal beamforming together. The capacity of the network is

represented by the offered traffic supported at a specific GOS. In [26], the capacity of

the integrated system with a reduced set of 40 channels was found from simulations.

However, due to the prohibitive runtimes, the capacity for the original 128 channels


0 2 4 6 8 10 12 14 160

5

10

15

20

25

30

number of of processors

GO

S (

Gra

de

of

Ser

vice

, %)

Erl 15, ARP

Erl 20, ARP

Erl 25, ARP

Erl 30, ARP

Erl 35, ARP+PC+OB

Erl 40, ARP+PC+OB

Figure A.5: Grade of service (GOS) vs. number of processors of a mobile cellularnetwork.

could not be obtained. Instead, the capacity was extrapolated based on the results

for the reduced 40 channels. In Fig. A.6, the extrapolated curve is plotted along with

the capacity found from simulations in [45]. As can be seen, the extrapolation un-

derestimates the capacity noticeably, which shows that the capacity behavior of the

simulated network is very complex and thus difficult to extrapolate accurately even

for a change in the number of channels available to the system. The GOS values for

110 and 120 Erlangs were obtained using both a single processor and 16 processors.

Each of the runs with a single processor took more than 22 days while it took about

2 days with 16 processors, yielding a speedup of more than 11. For 130 Erlangs, 32

processors were used and it took less than 35 hours.

We also plot the results for a system employing the Erlang-B queuing discipline

(dashed curves). In the Erlang-B discipline, a channel is assigned as long as it is not

occupied. Such detrimental factors as path loss, shadowing, interference, or mobility

to the channel quality are not considered in the discipline and consequently no call


100 105 110 115 120 125 1300

1

2

3

4

5

6

7

8

Offered traffic (Erlang/Cell)

GO

S (G

rade

of S

ervic

e, %

)

ARP+PC+OB, chan 128, extrapolatedARP+PC+OB, chan 128, simulated Erlang!B, chan 128

Erlang!B, chan 132

Erlang!B, chan 136

Erlang!B, chan 140

Figure A.6: Grade of service (GOS) vs. offered traffic (Erlang/cell) for the simulatedmobile cellular network with and without extrapolation for 128 channels, along withErlang-B system.

is dropped. Thus, an Erlang-B curve serves as an upper bound in performance for a

system with the same number of channels. However, in Fig. A.6, it is seen that the

integrated system outperforms an Erlang-B system with 128 channels. This outper-

formance was also observed for 40 channels in [26]. This extra gain was explained

in [26] to come from the directed retry (DR) mechanism, where more than one base

station is tried for new calls and hand-offs. DR essentially redirects transient overflow

of call attempts to other favorable base stations when a channel in the best base sta-

tion is not available. Thus, the capacity of the integrated system with 128 channels is

effectively higher than that of the Erlang-B system with the same number of channels.

From Fig. A.6, the effective number of channels of the integrated system is found to

be more than 136 channels for 1% GOS and more than 132 channels up to 5% GOS,

which translates to effective channel reuse factors of 0.94 and 0.97, respectively.


A.3.2 Large Wireless Mesh Network Simulator

The uppermost curve in Fig. A.4-(a) shows the runtimes of the WMN simulator

developed in this thesis versus the number of processors for a single parameter set

and network configuration. Each runtime is obtained from one long simulation run. A

single run took longer than 4 days using 8 processors, and 1 day and 5 hours with 32

processors. These long runtimes are due to the substantial computational complexity

of the simulator resulting from the huge network size and the large number of mesh

routers.

The corresponding speedup of the parallel WMN simulator is shown in the upper-

most curve in Fig. A.4-(b). For that curve, the speedup up to 8 processors is approxi-

mately linear in the number of processors from the observation that the speedup gain

with 16 processors is almost a factor of 2 compared to the case with 8 processors.

Then, we obtain a speedup gain of almost 27 using 32 processors. This tremendous

gain from parallel processing implies that the inter-processor communication over-

head is negligible compared to the computation workload. We note that the speedup

performance of this simulator is better compared to the cellular network simulator

discussed in Section A.3.1. This is because the WMN simulator has been designed

based on the parallel processing technique from the beginning so that it has been

more optimized to the technique than the cellular network simulator which has been

modified from an existing sequential simulator.

A.4 Summary

In this appendix, we presented a parallel processing technique for time-driven sim-

ulation of large and complex wireless networks with substantial PHY details such

as radio signal propagation and interference. We identified and demonstrated issues

of the technique related to the time-driven nature of the simulation with regards to

workload partitioning, synchronization, and database design, and proposed schemes

in those areas for effective and efficient parallelization. The enormous computational

complexity and inter-processor communication overhead of the parallel simulator is


addressed on a supercomputing platform that comprises multiple processors with

large high-speed memory and interconnected with high-speed links.

We applied the technique to two different wireless network simulators, a mobile

cellular network simulator and a large WMN simulator, and demonstrated its per-

formance. The technique was shown to achieve significant runtime speedup gains

for both simulators; for one set of simulation parameters and network configuration,

a speedup gain of more than 11 was obtained using 16 processors from the mobile

cellular network simulator, and almost 27 using 32 processors from the large WMN

simulator. The substantial gains from both simulators indicate that the technique is

general enough to be applied to different types of complex wireless networks.

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