Proceedings of the twenty-third annual ACM symposium on Principles of distributed computing
Auction-based spectrum sharing
Mobile Networks and Applications
Resource Allocation for Wireless Networks: Basics, Techniques, and Applications
Resource Allocation for Wireless Networks: Basics, Techniques, and Applications
IEEE Transactions on Mobile Computing
Performance of distributed dynamic frequency selection schemes for interference reducing networks
MILCOM'06 Proceedings of the 2006 IEEE conference on Military communications
Power minimization under throughput management over wireless networks with antenna diversity
IEEE Transactions on Wireless Communications
Iterative water-filling for Gaussian vector multiple-access channels
IEEE Transactions on Information Theory
Pricing and revenue sharing strategies for Internet service providers
IEEE Journal on Selected Areas in Communications
Non-Cooperative Resource Competition Game by Virtual Referee in Multi-Cell OFDMA Networks
IEEE Journal on Selected Areas in Communications
IEEE Journal on Selected Areas in Communications
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Recent studies on spectrum usage reveal poor utilization, both spatially and temporally. Opportunistic use of licensed spectrum while limiting interference to primary users can enhance spectrum reuse and provide orders of magnitude improvement in available channel capacity. This calls for spectrum sharing protocols that are dynamic, flexible, and efficient, in addition to being fair to end users. We employ cooperative game theory to address the opportunistic spectrum access problem. Specifically, we develop a game-theoretic model to analyze a scenario in which nodes in a wireless network seek to agree on a fair and efficient allocation of spectrum. First, we show that in high interference environments, the utility space of the game is non-convex, making certain optimal allocations unachievable with pure strategies. To mitigate this, we show that as the number of channels available increases, the utility space approaches convexity, thereby making optimal allocations achievable with pure strategies. Second, by comparing and analyzing three bargaining solutions, we show that the Nash Bargaining Solution achieves the best tradeoff between fairness and efficiency, using a small number of channels. Finally, we develop a distributed algorithm for spectrum sharing that is general enough to accomodate non-zero disagreement points, and show that it achieves allocations reasonably close to the Nash Bargaining Solution.