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We consider the problem of multi-user spectrum access in wireless networks.The bandwidth is divided into K orthogonal channels, and M users aim to accessthe spectrum. Each user chooses a single channel for transmission at each timeslot. The state of each channel is modeled by a restless unknown Markovianprocess. Previous studies have analyzed a special case of this setting, inwhich each channel yields the same expected rate for all users. By contrast, weconsider a more general and practical model, where each channel yields adifferent expected rate for each user. This model adds a significant challengeof how to efficiently learn a channel allocation in a distributed manner toyield a global system-wide objective. We adopt the stable matching utility asthe system objective, which is known to yield strong performance inmultichannel wireless networks, and develop a novel Distributed Stable StrategyLearning (DSSL) algorithm to achieve the objective. We prove theoretically thatDSSL converges to the stable matching allocation, and the regret, defined asthe loss in total rate with respect to the stable matching solution, has alogarithmic order with time. Finally, simulation results demonstrate the strongperformance of the DSSL algorithm.

Distributed Learning over Markovian Fading Channels for Stable Spectrum Access