Unknown combinatorial auction mechanisms for heterogeneous spectrum redistribution

With the growing deployment of wireless communication technologies, radio spectrum is becoming a scarce resource. Auctions are believed to be among the most effective tools to solve or relieve the problem of radio spectrum shortage. However, designing a practical spectrum auction mechanism has to consider five major challenges: strategic behaviors of unknown users, channel heterogeneity, preference diversity, channel spatial reusability, and social welfare maximization. Unfortunately, none of existing work fully considered these five challenges. In this paper, we model the problem of heterogeneous spectrum allocation as a combinatorial auction, and propose AEGIS, which is the first framework of unknown combinatorial Auction mEchanisms for heteroGeneous spectrum redIStribution. AEGIS contains two mechanisms, namely AEGIS-SG and AEGIS-MP. AEGIS-SG is a direct revelation combinatorial spectrum auction mechanism for unknown single-minded users, achieving strategy-proofness and approximately efficient social welfare. We further design an iterative ascending combinatorial auction, namely AEGIS-MP, to adapt to the scenario with unknown multi-minded users. AEGIS-MP is implemented in a set of undominated strategies and has a good approximation ratio. We evaluate AEGIS on two practical datasets: Google Spectrum Database and GoogleWiFi. Evaluation results show that AEGIS achieve much better performance than the state-of-the-art mechanisms.