Resource Allocation in Uplink OFDMA Wireless Systems: Optimal Solutions and Practical Implementations

Tackling problems from the least complicated to the most,Resource Allocation in Uplink OFDMA Wireless Systems providesreaders with a comprehensive look at resource allocation andscheduling techniques (for both single and multi–cell deployments)in uplink OFDMA wireless networks relying on convexoptimization and game theory to thoroughly analyze performance.

Inside, readers will find topics and discussions on:

Formulating and solving the uplink ergodic sum–rate maximizationproblem

Proposing suboptimal algorithms that achieve a close performanceto the optimal case at a considerably reduced complexity and leadto fairness when the appropriate utility is used

Investigating the performance and extensions of the proposedsuboptimal algorithms in a distributed base station scenario

Studying distributed resource allocation where users take partin the scheduling process, and considering scenarios with andwithout user collaboration

Formulating the sum–rate maximization problem in a multi–cellscenario, and proposing efficient centralized and distributedalgorithms for intercell interference mitigation

Discussing the applicability of the proposed techniques tostate–of–the–art wireless technologies, LTE and WiMAX, andproposing relevant extensions

Along with schematics and figures featuring simulation results,Resource Allocation in Uplink OFDMA Wireless Systems is a valuablebook for?wireless communications and cellular systems professionalsand students.

ACKNOWLEDGMENTS xv

ACRONYMS xvii

CHAPTER 1 INTRODUCTION 1

1.1 Evolution of Wireless Communication Systems 1

1.2 Orthogonal Frequency Division Multiple Access 2

1.3 Organization of this Book 5

CHAPTER 2 BACKGROUND ON DOWNLINK RESOURCE ALLOCATION IN OFDMAWIRELESS NETWORKS 9

2.1 Centralized Single Cell Scheduling 9

2.2 Distributed Scheduling 13

2.3 Scheduling in Multicell Scenarios 14

2.3.1 Multicell Scheduling in LTE 16

2.4 Summary 18

CHAPTER 3 ERGODIC SUM–RATE MAXIMIZATION WITH CONTINUOUS RATES19

3.1 Background 19

3.2 Problem Formulation 21

3.3 Problem Solution 23

3.4 Achievable Rate Region 28

3.5 Results and Discussion 35

3.6 Summary 41

CHAPTER 4 ERGODIC SUM–RATE MAXIMIZATION WITH DISCRETE RATES43

4.1 Background 43

4.2 Problem Formulation 44

4.3 Problem Solution 46

4.4 Results and Discussion 52

4.5 Summary 57

CHAPTER 5 GENERALIZATION TO UTILITY MAXIMIZATION 59

5.1 Background 59

5.2 Ergodic Utility Maximization with Continuous Rates 60

5.3 Ergodic Utility Maximization with Discrete Rates 64

5.4 Summary 68

CHAPTER 6 SUBOPTIMAL IMPLEMENTATION OF ERGODIC SUM–RATEMAXIMIZATION 69

6.1 Background 69

6.2 Suboptimal Approximation of the Continuous Rates Solution71

6.3 Suboptimal Approximation of the Discrete Rates Solution73

6.4 Complexity Analysis of the Suboptimal Algorithms 76

6.5 Results and Discussion 78

6.6 Summary 88

CHAPTER 7 SUBOPTIMAL IMPLEMENTATION WITH PROPORTIONALFAIRNESS 89

7.1 Background 89

7.2 Proportional Fair Scheduling 91

7.3 Low Complexity Utility Maximization Algorithms 94

7.4 Proportional Fair Utilities 100

7.5 Results and Discussion 101

7.6 Summary 112

CHAPTER 8 SCHEDULING WITH DISTRIBUTED BASE STATIONS113

8.1 Background 113

8.2 System Model 115

8.3 Scheduling with Distributed Base Stations 118

8.4 Results and Discussion 120

8.5 Distributed Base Stations Versus Relays 128

8.6 Distributed Base Stations Versus Femtocells 131

8.7 Summary 133

CHAPTER 9 DISTRIBUTED SCHEDULING WITH USER COOPERATION135

9.1 Background 135

9.2 Cooperative Distributed Scheduling Scheme 136

9.3 Distributed Scheduling Algorithm 140

9.4 Results and Discussion 142

9.5 Summary 149

CHAPTER 10 DISTRIBUTED SCHEDULING WITHOUT USER COOPERATION151

10.1 Background 151

10.2 Noncooperative Distributed Scheduling Scheme 153

10.3 Comparison to Existing Schemes 155

10.4 Analysis of Measurement Inaccuracies 156

10.5 Results and Discussion 160

10.6 Optimization of Transmission Probabilities 165

10.7 Practical Considerations 169

10.8 Summary 171

CHAPTER 11 CENTRALIZED MULTICELL SCHEDULING WITH INTERFERENCEMITIGATION 173

11.1 Background 173

11.2 Problem Formulation 175

11.3 Iterative Pricing–Based Power Control Solution 178

11.4 Pricing Game with Centralized Control 184

11.5 Suboptimal Scheduling Scheme Using Pricing–Based PowerControl 186

11.6 Suboptimal Scheduling Scheme Using ProbabilisticTransmission 190

11.7 Results and Discussion 191

11.8 Summary 201

CHAPTER 12 DISTRIBUTED MULTICELL SCHEDULING WITH INTERFERENCEMITIGATION 203

12.1 Background 203

12.2 System Model 204

12.3 Intracell Cooperation: Distributed Scheduling 205

12.4 Intercell Interference Mitigation/Avoidance 206

12.5 Results and Discussion 209

12.6 Practical Aspects 217

12.6.3 Application in a CR Network 219

12.6.4 Application in a Network with Femtocell Deployment219

12.6.5 Distributed Multicell Scheduling without User Cooperation220

12.7 Summary 221

CHAPTER 13 SCHEDULING IN STATE–OF–THE–ART OFDMA–BASEDWIRELESS SYSTEMS 223

13.1 WiMAX Scheduling Overview 223

13.2 LTE Scheduling Overview 228

13.3 SCFDMA Versus OFDMA Scheduling 235

13.4 Comparison to the LTE Power Control Scheme 240

13.5 Summary 245

CHAPTER 14 FUTURE RESEARCH DIRECTIONS 247

14.1 Resource Allocation with Multiple Service Classes 247

14.2 Network MIMO 247

14.3 Coalitional Game Theory 248

14.4 Resource Allocation with Femtocells 249

14.5 Green Networks and Self–Organizing Networks 249

14.6 Joint Uplink/Downlink Resource Allocation 250

14.7 Joint Resource Allocation in Heterogeneous Networks 251

14.8 Resource Allocation in Cognitive Radio Networks 252

BIBLIOGRAPHY 255

INDEX 269

ELIAS E. YAACOUB, PhD, is currently a research scientist at the Qatar University Wireless Innovations Center. His research interests include scheduling and interference mitigation in multi–cell OFDMA and LTE networks. He has authored numerous journal and conference papers on these topics. Dr. Yaacoub is a member of the IEEE and a member of the Lebanese Order of Engineers.

ZAHER DAWY, PhD, is an associate professor at the American University of Beirut (AUB). His research interests include cooperative and distributed communications, resource allocation, cellular technologies, and computational biology. He received the IEEE Communications Society Outstanding Young Researcher Award for Europe, Middle East, and Africa Region in 2011 and the AUB 2008 Teaching Excellence Award. Dr. Dawy is a senior member of the IEEE and Chair of the IEEE Communications Society, Lebanon Chapter.