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Truthful Auction Design and Analysis in Heterogeneous Secondary Spectrum Markets

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Truthful Auction Design and Analysis in Heterogeneous Secondary Spectrum Markets Truthful Auction Design and Analysis in Heterogeneous Secondary Spectrum Markets by Wei Li B.S. in Mathematics & Applied Mathematics, August 2008, China Agricultural University, Beijing, China M.S. in Computer Science & Technology, March 2011, Beijing University of Posts and Telecommunications, Beijing, China A Dissertation submitted to The Faculty of The School of Engineering and Applied Science of The George Washington University in partial satisfaction of the requirements for the degree of Doctor of Philosophy August 31, 2016 Dissertation directed by Xiuzhen Cheng Professor of Computer Science The School of Engineering and Applied Science of The George Washington University cer- tifies that Wei Li has passed the Final Examination for the degree of Doctor of Philosophy as of May 9, 2016. This is the final and approved form of the dissertation. Truthful Auction Design and Analysis in Heterogeneous Secondary Spectrum Markets Wei Li Dissertation Research Committee: Xiuzhen Cheng, Professor of Computer Science, Dissertation Director Hyeong-Ah Choi, Professor of Computer Science, Committee Member Nan Zhang, Associate Professor of Computer Science, Committee Member Hang Liu, Associate Professor of Electrical Engineering & Computer Science, Catholic University of America, Committee Member ii c Copyright 2016 by Wei Li All rights reserved iii Acknowledgments This dissertation could not have been completed without the great support that I have re- ceived from my advisor, collaborators, friends, family, and committee members over the years. I wish to offer my most heartfelt thanks to the following people. I would like to express my sincere appreciation and gratitude to my advisor, Prof. Xi- uzhen Cheng, for her patience, encouragement, support, and immense knowledge. Her patient guidance helped me in all the time from when I was first considering applying for the PhD program in the Department of Computer Science, through to completion of this degree. I could not have imagined having a better advisor and mentor for my Ph.D study. Also, I would like to thank Prof. Shengling Wang (Beijing Normal University), Prof. Liran Ma (Texas Christian University), Prof. Carl Sturtivant (University of Minnesota), Prof. Zhenkai Liang (National University of Singapore), Prof. Jian Mao (Beihang Uni- versity), Prof. Yan Huo (Beijing Jiaotong University), and Prof. Yajian Zhou (Beijing University of Posts and Telecommunicatins) for spending so much time with me to discuss research and to give me valuable advices and comments in all the way. I greatly appreciate the opportunities working with such excellent researchers. To my dear colleagues and friends, Dr. Dengyuan Wu, Dr. Bowu Zhang, Dr. Hongjuan Li, Dr. Chunqiang Hu, Dr. Mingyang Zhang, Dr. Zhuojie Zhou, Yuan Le, Tianyi Song, Ruinian Li, Bo Mei, Abdulrahman Alhothaily, Arwa Alrawais, Maya Larson, Xiaoshuang Xing, Hong Li, and Yunhua He, I cannot thank you enough for supporting and helping me. I will never forget the time we spent together. Moreover, I am very grateful to my parents for their unconditional love, encouragement, and support. While I have taken my own directions at times, I have always appreciated the path that they have blazed before me. Without their precious support, it would not be possible to complete my Ph.D. degree. Finally, my sincere thanks goes to my committee members, Prof. Heyong-Ah Choi, Prof. Nan Zhang, and Prof. Hang Liu (Catholic University of America), for their friendly iv guidance and thought-provoking suggestions. v Abstract Truthful Auction Design and Analysis in Heterogeneous Secondary Spectrum Markets Over the past years, a number of auction mechanisms have been widely proposed as a powerful market-based technique, to satisfy users’ growing demands on spectrum access service while improving channel utilization in secondary spectrum markets. The major design goal of these auction mechanisms focuses on truthfulness to prevent market manip- ulation, by guaranteeing that no seller/buyer can receive a higher utility via cheating on its ask/bid price. Almost all existing secondary spectrum auctions are based on three popular schemes, namely McAfee, Myerson’s Optimal Mechanism (MOM), and Vickrey Clarke Groves (VCG). However, some issues, including channel attribute diversity, location diversity, price diversity, and self-collusion, are overlooked by most of the existing work. This dis- sertation research focuses on (1) establishing practical auction models for heterogeneous secondary spectrum markets, by exploiting diversities of channel attribute, location, and price; (2) investigating the root causes of self-collusion in MOM and VCG; and (3) design- ing truthful and self-collusion resistant spectrum auction schemes. First, we design a market-based channel allocation scheme for cognitive radio networks by exploiting multi-attribute channel-aware auctions to consider channel diversity in fre- quency, time, and space domains. Different from existing research, our objective is to max- imize the winning SUs’ service satisfaction degree while enhancing the utilities of winning PUs and SUs, which can effectively encourage them to join the auction and improve the sustainability of the spectrum market. Based on an elaborately devised preference func- tion, we allocate channels to SUs satisfying their demands while considering spatial and temporal channel reuse to enhance channel utilization. Moreover, we propose a discrimi- natory pricing method to enhance the utilities of winning PUs and SUs. A comprehensive analysis indicates that our multi-attribute auction is individually-rational, ex-post budget vi balanced, value-truthful, and attribute-truthful. Our simulation results indicate that the pro- posed multi-attribute auction can significantly increase the winners’ utilities and ensure SUs’ service satisfaction. Second, we propose an extensible and flexible truthful auction framework that is in- dividually rational, truthful, and self-collusion resistant. By properly setting one simple parameter, this framework yields efficient auctions (like VCG), (sub)optimal auctions (like MOM), and budget balanced double auctions; by carefully choosing virtual valuation func- tions for the bidders, it can produce attribute-aware auctions that take the channel diversity into consideration. The framework adopts a novel procedure that can prevent bidder self- collusion resulted from the bid diversity. In order to reduce the computational complexity of our framework, we propose a greedy auction scheme that possesses all the economic properties of our auction framework. We also prove the performance bound of the greedy algorithm under certain condition. Theoretical analysis and case studies demonstrate the strength of our auction framework in handling various considerations in a practical hetero- geneous spectrum market. Third, we consider a more practical multiunit heterogeneous spectrum market in which each buyer may request multiple channels with different bid prices at different geographical regions and each channel is associated with a reserve price indicating the desired revenue of the seller. The degree-of-freedom brought by multiunit trading and (reserve and bid) price diversity in such a market can be exploited to break the truthfulness of the two most popular schemes, VCG and MOM, adopted by secondary spectrum auctions via bidder self-collusion. We conduct a thorough analysis on the root causes of untruthfulness in VCG and MOM and prove the fundamental theories addressing when VCG and MOM are truthful and when their truthfulness is broken by bid rigging. Particularly, we demonstrate how self-collusion is exploited in VCG and MOM to improve the untruthful bidders’ utility. The critical findings provide a guidance to our design of Siri, a truthful and self-collusion resistant auction mechanism for multiunit heterogeneous spectrum markets with reserve vii prices. We analyze the economic properties of Siri and prove its truthfulness via rigorous theoretical analysis. viii Table of Contents Acknowledgments .................................. iv Abstract ........................................ vi List of Figures ..................................... xii List of Tables ..................................... xiv Chapter 1-Introduction ............................... 1 1.1 Secondary Spectrum Markets ......................... 1 1.2 Spectrum Auctions............................... 2 1.3 Existing Auction Mechanisms......................... 3 1.4 Proposed Auction Schemes........................... 5 Chapter 2-Preliminaries ............................... 6 2.1 Basic Concepts and Economic Properties ................... 6 2.2 Auction Process................................. 7 2.3 Classic Auctions ................................ 8 2.3.1 Vickery-Clarke-Groves (VCG)..................... 8 2.3.2 Myerson’s Optimal Mechanism (MOM)................ 9 2.3.3 McAfee................................. 11 Chapter 3-Related Prior Work ........................... 12 3.1 Truthful Auctions................................ 12 3.2 Collusion Resistance.............................. 14 3.3 Self-Collusion Resistance ........................... 14 Chapter 4-Truthful Multi-Attribute Auction with Discriminatory Pricing in Cognitive Radio Networks .............................. 15 4.1 Auction Model................................. 17 4.2 Problem Formulation.............................. 18 4.3 Multi-Attribute Auction ............................ 20 4.3.1 Potential Winner Determination.................... 20 4.3.2 Preference-Based
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