Truthful Auction Design and Analysis in Heterogeneous Secondary Spectrum Markets
Open AccessOver 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 manipulation, 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 dissertation 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) designing 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 frequency, time, and space domains. Different from existing research, our objective is to maximize 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 function, we allocate channels to SUs satisfying their demands while considering spatial and temporal channel reuse to enhance channel utilization. Moreover, we propose a discriminatory 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 balanced, value-truthful, and attribute-truthful. Our simulation results indicate that the proposed 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 individually 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 functions 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 heterogeneous 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 prices. We analyze the economic properties of Siri and prove its truthfulness via rigorous theoretical analysis.
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