Revenue-maximizing and truthful online auctions for dynamic spectrum access

Abstract

Secondary spectrum auctions have been suggested as a strategically robust mechanism for distributing idle spectrum to competing secondary users. However, previous work on such auction design have assumed a static auction setting, thus failing to fully exploit the inherently time-varying nature of spectrum demand and utilization. In this paper, we address this issue from the perspective of the primary user who wishes to maximize the auction revenue. We present an online auction framework that dynamically accepts bids and allocates spectrum. We prove rigorously that our online auction framework is truthful in the multiple dimensions of bid values, as well as bid timing parameters. To protect against unbounded loss of revenue due to latter bids, we introduce controlled preemption into our mechanism. We prove that preemption, coupled with the technique of inflating bids artificially, leads to an online auction that guarantees a 1 5 –fraction of the optimal revenue as obtained by an offline adversary. Since the previous guarantee holds only for the optimal channel allocation, we further provide a greedy channel allocation scheme which provides scalability. We prove that the greedy scheme also obtains a constant competitive revenue guarantee, where the constant depends on the parameter of the conflict graph.