Truthful Auction Mechanisms with Performance Guarantee in Secondary Spectrum Markets

We study a spectrum auction problem where each request from new spectrum users has spatial, temporal, and spectral features. Our goal is to design truthful auction mechanisms that maximize either the overall social efficiency of new users (a.k.a buyers) or the revenue of the spectrum owner (a.k.a seller). Given that the optimal conflict-free spectrum allocation problem is NP-hard, this paper proposes a series of near-optimal auction mechanisms based on the following approximation techniques: linear programming (LP) relaxation, randomized rounding, derandomized rounding, monotone derandomization, and Lavi-Swamy method. Comparing with the prior art, we make two significant advances: First, our auction mechanisms are not only truthful but also provide theoretically-provable performance guarantee, an important feature that existing work under the same auction model does not have. Second, our auction mechanisms support both spatial and temporal spectral reuse, which makes the problem more challenging than existing work that deals with only spatial or temporal reuse. We perform extensive simulations to study the performance of the proposed mechanisms, and the simulation results corroborate our theoretical analysis.