Adaptive Radio Resource Management in Cognitive Radio Communications Using Fuzzy Reasoning
Total Page：16
File Type：pdf, Size：1020Kb
Adaptive Radio Resource Management in Cognitive Radio Communications using Fuzzy Reasoning Hazem S. Shatila Dissertation submitted to the Faculty of the Virginia Polytechnic Institute and State University in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Electrical Engineering Committee Members: Jeffrey H. Reed, Chair Mohamed E. Khedr, Co-Chair A. A. (Louis) Beex Sandeep Shukla Anil Vullakanti March 20, 2012 Blacksburg, Virginia Keywords: Wireless broadband, cognitive radio, cognitive engine, WiMAX, fuzzy logic, fuzzy C-mean clustering (FCM), dynamic spectrum allocation, opportunistic decision making. Copyright 2012, Hazem S. Shatila Adaptive Radio Resource Management in Cognitive Radio Communications using Fuzzy Reasoning Hazem S. Shatila (Abstract) As wireless technologies evolve, novel innovations and concepts are required to dynamically and automatically alter various radio parameters in accordance with the radio environment. These innovations open the door for cognitive radio (CR), a new concept in telecommunications. CR makes its decisions using an inference engine, which can learn and adapt to changes in radio conditions. Fuzzy logic (FL) is the proposed decision-making algorithm for controlling the CR‘s inference engine. Fuzzy logic is well-suited for vague environments in which incomplete and heterogeneous information is present. In our proposed approach, FL is used to alter various radio parameters according to experience gained from different environmental conditions. FL requires a set of decision-making rules, which can vary according to radio conditions, but anomalies rise among these rules, causing degradation in the CR‘s performance. In such cases, the CR requires a method for eliminating such anomalies. In our model, we used a method based on the Dempster-Shafer (DS) theory of belief to accomplish this task. Through extensive simulation results and vast case studies, the use of the DS theory indeed improved the CR‘s decision- making capability. Using FL and the DS theory of belief is considered a vital module in the automation of various radio parameters for coping with the dynamic wireless environment. To demonstrate the FL inference engine, we propose a CR version of WiMAX, which we call CogMAX, to control different radio resources. Some of the physical parameters that can be altered for better results and performance are the physical layer parameters such as channel estimation technique, the number of subcarriers used for channel estimation, the modulation technique, and the code rate. iii In memoriam of Sarwat Shatila, my beloved father. iv Acknowledgements This dissertation represents years of hard work and mental effort. There were so many people involved throughout the process, and, without their help, encouragement, and support, I would have never accomplished what I have. I would like to thank my supervisor, Dr. Jeffrey. H. Reed, for his continuous support and guidance. He has been inspiring me with thoughts and ideas throughout this period. His flexibility and professionalism made everything feel easy and comfortable. Dr. Mohamed. E. Khedr, was not only my co-supervisor in Egypt, but also a true friend and brother. He helped me through hard times and always encouraged me forward. His technical knowledge made him a very important reference to me during my work. I would really like to thank Dr. Khedr and Dr. Reed because working together was great. My family has been a true source of encouragement and spirit as well. They were there when my spirit was low and hope was hard to imagine. I‘m so grateful to them, and I thank God for them. My lovely son, Yassien, is my source of joy and happiness. I will make up for all the days that work kept me away. I would like to thank the VT-MENA (Virginia Tech – Middle East and North Africa Region) family for their support and encouragement throughout the program. I thank Dr. Yasser Hanafy and Dr.Sedki Riad for everything they did. They were always of great help. I would like to extend my gratitude to my colleagues at the Arab Academy for Science and Technology and Maritime Transport (AASTMT) and at Vodafone Egypt for their encouragement and support and for giving me the time and space to complete my work. v Special thanks to the members of my committee, Dr. Aloysius Beex, Dr. Sandeep Shukla, and Dr. Anil Vullakanti for their patience and guidance. Thanks also goes to the Wireless@VT group for all the fruitful meetings and feedback to improve this work. I would especially like to thank Dr. Joseph Gaeddert for his last-minute finishing touches. I would also like to thank Nancy Goad and Cindy Hopkins for the help they have given me in completing my necessary paper work. Nancy repetitively sent me scanned copies of my corrected work, and I offer my thanks. Finally, I would like to thank my father, who encouraged me years ago to complete my Ph.D. I wish he were around, and I hope I made him proud. vi Contents List of Figures........ ....................................................................................................... x List of Tables………………………………………………………………………...………...xiv CHAPTER 1 Introduction .................................................................................. 1 1.1 Problem Statement .............................................................................................................. 1 1.2 Contributions....................................................................................................................... 7 1.3 Publications ......................................................................................................................... 8 1.4 Outline................................................................................................................................. 9 CHAPTER 2 Background .................................................................................11 2.1 WiMAX Standard ............................................................................................................. 11 2.1.1 Benefits of WiMAX .................................................................................................... 13 2.1.2 WiMAX Physical Layer Parameters ......................................................................... 13 2.2 Cognitive Radio ................................................................................................................ 15 2.2.1 Cognitive Tasks and Cognitive Cycle ....................................................................... 16 2.2.2 Dynamic Spectrum Utilization .................................................................................. 18 2.2.3 Capabilities of a Cognitive Radio ............................................................................. 20 2.2.4 Common Applications of Cognitive Radio ................................................................ 25 CHAPTER 3 CogMAX Architecture and Related Work ..............................29 3.1 Overview of the MAC (Media Access Control) Layer ..................................................... 29 3.2 Proposed Architecture Design .......................................................................................... 31 3.3 The Inference Engine ........................................................................................................ 34 3.3.1 Learning in Cognitive Radio ..................................................................................... 35 3.4 Fuzzy Logic ...................................................................................................................... 36 3.4.1 Type 2 Fuzzy Logic ................................................................................................... 45 3.5 Current Related Work ....................................................................................................... 48 vii CHAPTER 4 Fuzzy C-mean Clustering and Dempster Shafer Theory of Belief ……………………………………………………………………….50 4.1 Basic Algorithm ................................................................................................................ 50 4.2 Optimization Formulation of Crisp C-mean Clustering ................................................... 52 4.3 Fuzzy C-mean Algorithm ................................................................................................. 53 4.3.1 Convergence Test ...................................................................................................... 56 4.4 Validity Test...................................................................................................................... 57 4.4.1 Partition Coefficient and Exponential Separation Validity ...................................... 60 4.5 The Dempster Shafer Theory ............................................................................................ 62 4.5.1 The Basic Concepts ................................................................................................... 63 CHAPTER 5 Channel Type & Estimation in CogMAX ................................66 5.1 Introduction to Channel Estimation Techniques ............................................................... 66 5.1.1 Estimators ................................................................................................................. 70 5.1.2 Interpolators ............................................................................................................. 73 5.2 Determining Channel Type in CogMAX .......................................................................... 75 5.2.1 Algorithm