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In this paper, spectrum access in cognitive radio networks is modeled as arepeated auction game subject to monitoring and entry costs. For secondaryusers, sensing costs are incurred as the result of primary users' activity.Furthermore, each secondary user pays the cost of transmissions upon successfulbidding for a channel. Knowledge regarding other secondary users' activity islimited due to the distributed nature of the network. The resulting formulationis thus a dynamic game with incomplete information. In this paper, an efficientbidding learning algorithm is proposed based on the outcome of pasttransactions. As demonstrated through extensive simulations, the proposeddistributed scheme outperforms a myopic one-stage algorithm, and can achieve agood balance between efficiency and fairness.

Repeated Auctions with Learning for Spectrum Access in Cognitive Radio Networks