Game Theory Based Resource Management In Non-cooperative Wireless Networks

With the growing deployment of wireless communication technologies, radio spectrum is becoming a scarce resource. The current static spectrum manage-ment leads to low spectrum utilization in the spatial and temporal dimensions. Large chunks of radio spectrum are left idle most of the time at a lot of places, while new wireless applications are starving for the radio spectrum. 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 rational users, channel heterogeneity, channel spatial reusability, preference diversity and social welfare maximization. Unfortunately, none of existing work fully consid-ered these five challenges. In this paper, we model the problem of heteroge-neous spectrum allocation as a combinatorial auction, and propose four efficient mechanisms in different network scenarios. We first consider one-parameter sce-nario, in which the rational users can manipulate the auctions by cheating their valuations on channels, and proposed SMASHER, which includes two mecha-nisms:SMASHER-AP and SMASHER-GR. SMASHER-AP is a strategy-proof, approximately efficient combinatorial auction mechanism for indivisible channel redistribution. We consider another case, in which channels can be shared by the users in a paradigm of time-division multiplexing and propose SMASHER-GR, which is a strategy-proof channel allocation and scheduling mechanism. We further consider the multiple parameter scenario, in which both the valuations and channel demands are private information, and can be manipulated by the rational users. We proposed a unknown combinatorial auction framework, name- ly AEGIS. AEGIS contains two complementary mechanisms: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 un-known multi-minded users. AEGIS-MP is implemented in a set of undominated strategies and has a good approximation ratio. We evaluate the four mechanisms on practical datasets. Evaluation results show that the four mechanisms achieve much better performance than the state-of-the-art mechanisms in terms of social welfare, buyer satisfaction ratio, and channel utilization.