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In this paper, the authors design distributed spectrum access mechanisms with both complete and incomplete network information. They propose an evolutionary spectrum access mechanism with complete network information, and show that the mechanism achieves an equilibrium that is globally evolutionarily stable. With incomplete network information, they propose a distributed learning mechanism, where each user utilizes local observations to estimate the expected throughput and learns to adjust its spectrum access strategy adaptively over time. They show that the learning mechanism converges to the same evolutionary equilibrium on the time average.
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