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Enabling Cognitive Radios Through Radio Environment Maps

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Enabling Cognitive Radios Through Radio Environment Maps Enabling Cognitive Radios through Radio Environment Maps Youping Zhao 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 Dr. Jeffrey H. Reed, Chair Dr. Brian G. Agee Dr. R. Michael Buehrer Dr. Scott F. Midkiff Dr. Hanif D. Sherali May 8, 2007 Blacksburg, Virginia Keywords: Cognitive Engine, Cognitive Radio, Radio Environment Map, 802.11 WLAN, 802.22 WRAN © Copyright 2007, Youping Zhao Enabling Cognitive Radios through Radio Environment Maps Youping Zhao Abstract In recent years, cognitive radios and cognitive wireless networks have been introduced as a new paradigm for enabling much higher spectrum utilization, providing more reliable and personal radio services, reducing harmful interference, and facilitating the interoperability or convergence of different wireless communication networks. Cognitive radios are goal- oriented, autonomously learn from experience and adapt to changing operating conditions. Cognitive radios have the potential to drive the next generation of radio devices and wireless communication system design and to enable a variety of niche applications in demanding environments, such as spectrum-sharing networks, public safety, natural disasters, civil emergencies, and military operations. This research first introduces an innovative approach to developing cognitive radios based on the Radio Environment Map (REM). The REM can be viewed as an integrated database that provides multi-domain environmental information and prior knowledge for cognitive radios, such as the geographical features, available services and networks, spectral regulations, locations and activities of neighboring radios, policies of the users and/or service providers, and past experience. The REM, serving as a vehicle of network support to cognitive radios, can be exploited by the cognitive engine for most cognitive functionalities, such as situation awareness, reasoning, learning, planning, and decision support. This research examines the role of the REM in cognitive radio development from a network point of view, and focuses on addressing three specific issues about the REM: how to design and populate the REM; how to exploit the REM with the cognitive engine algorithms; and how to evaluate the performance of the cognitive radios. Applications of the REM to wireless local area networks (WLAN) and wireless regional area networks (WRAN) are investigated, especially from the perspectives of interference management and radio resource management, which ii illustrate the significance of cognitive radios to the evolution of wireless communications and the revolution in spectral regulation. Network architecture for REM-enabled cognitive radios and framework for REM-enabled situation-aware cognitive engine learning algorithms have been proposed and formalized. As an example, the REM, including the data model and basic application programmer interfaces (API) to the cognitive engine, has been developed for cognitive WRAN systems. Furthermore, REM-enabled cognitive cooperative learning (REM- CCL) and REM-enabled case- and knowledge-based learning algorithms (REM-CKL) have been proposed and validated with link-level or network-level simulations and a WRAN base station cognitive engine testbed. Simulation results demonstrate that the WRAN CE can adapt orders of magnitude faster when using the REM-CKL than when using the genetic algorithms and achieve near-optimal global utility by leveraging the REM-CKL and a local search. Simulation results also suggest that exploiting the Global REM information can considerably improve the performance of both primary and secondary users and mitigate the hidden node (or hidden receiver) problem. REM dissemination schemes and the resulting overhead have been investigated and analyzed under various network scenarios. By extending the optimized link state routing protocol, the overhead of REM dissemination in wireless ad hoc networks via multipoint relays can be significantly reduced by orders of magnitude as compared to plain flooding. Performance metrics for various cognitive radio applications are also proposed. REM-based scenario-driven testing (REM-SDT) has been proposed and employed to evaluate the performances of the cognitive engine and cognitive wireless networks. This research shows that REM is a viable, cost-efficient approach to developing cognitive radios and cognitive wireless networks with significant potential in various applications. Future research recommendations are provided in the conclusion. iii Acknowledgments I wish to thank my advisor, Dr. Jeffrey H. Reed, for his motivational discussions, consistent support, and encouragement throughout my Ph.D. study. Dr. Reed’s encouraging words, “I have faith in your capability,” always kept my spirits up and enabled me to pass through the hard times at the initial stage of my Ph.D. study. Dr. Reed’s expertise and insight in software- defined radio and cognitive radio greatly help me to formalize the idea of the radio environment map and exploit it as the navigator for cognitive radio. I would also like to thank Dr. Brian G. Agee, Dr. R. Michael Buehrer, Dr. Scott F. Midkiff, and Dr. Hanif D. Sherali for serving on my committee and for their constructive comments and suggestions. I am very grateful to Dr. Kyung K. Bae, Dr. Claudio da Silva, Dr. Carl Dietrich, Dr. Shiwen Mao, Dr. Chris Anderson, Dr. James Neel, Dr. Max Robert, Dr. Ramesh Chembil Palat, Dr. Swaroop Venkatesh, Dr. Bruce Fette, Dr. Y. Thomas Hou, our cognitive radio research team members, Joseph Gaeddert, Lizdabel Morales, Kyouwoong Kim, and Rekha Menon, and my friends, David Raymond, Carlos R. Aguayo Gonzalez, Ingrid Burbey, Bin Le, and Thomas Rondeau, for their productive discussion, collaboration, and great help on my research. I would also like to thank Lori Hughes for her careful revisions to the initial draft of this dissertation. The MPRG staff, Shelby, Hilda, Jenny, and Cindy, are also acknowledged for their kind help and support. My research work has been supported by funding from Army Research Office (ARO), CISCO Systems, Inc (CISCO), Electronics and Telecommunications Research Institute (ETRI), Tektronix Instruments, Texas Instruments (TI), and Wireless@Virginia Tech Partners. Any opinions, findings, and conclusions or recommendations expressed in this dissertation are those of the author(s) and do not necessarily reflect the views of the sponsors. Portions of this dissertation were previously published in Cognitive Radio Technology (edited by Dr. Bruce Fette) by Elsevier/Newnes in 2006, ISBN 0-7506-7952-2. iv This dissertation is dedicated to my parents, especially to my father who has been ill for years during my Ph.D. studies. I also appreciate my mother’s words, “Conduct your research with passion.” Without passion, it is almost impossible to get original ideas and meaningful results. Finally, I would like to thank my wife and daughter for their love, patience, and support. v Contents Abstract ………………………………………………………………….…... ii Acknowledgments ………………………………………………………………..….. iv Table of Contents …………………………………………………………………..….. vi List of Figures …………………………………………………………………......... x List of Tables ………………………………………………………………….…. xiii Glossary ………………………………………………………………….......xiv 1 INTRODUCTION .........................................................................................................................................1 1.1 INTRODUCTION TO COGNITIVE RADIO ......................................................................................................2 1.2 MOTIVATIONS OF THIS RESEARCH AND SPECIFIC PROBLEMS...................................................................5 1.3 RESEARCH CONTRIBUTIONS .....................................................................................................................6 1.4 RESULTING IP DISCLOSURES, TESTBEDS, AND PUBLICATIONS .................................................................8 1.5 OVERVIEW OF THE DISSERTATION..........................................................................................................12 2 INTRODUCTION TO REM AND INTERDISCIPLINARY TECHNICAL BACKGROUND ...........14 2.1 INSIGHTFUL ANALOGIES FOR CR AND REM...........................................................................................14 2.1.1 An Analogy between a Taxi Driver and a Cognitive Radio ...........................................................14 2.1.2 An Analogy between City Map and Radio Environment Map (REM)............................................17 2.1.3 What Can We Learn from the Analogies?......................................................................................19 2.2 NEW CONCEPTS AND TERMS IN THE FIELD OF CR RESEARCH ................................................................19 2.2.1 Interference Temperature ..............................................................................................................20 2.2.2 Spectrum Hole (Spectrum Opportunity).........................................................................................22 2.2.3 Cognitive Engine............................................................................................................................22 2.2.4 Policy Engine.................................................................................................................................22 2.3 INTERDISCIPLINARY TECHNICAL BACKGROUND.....................................................................................23
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