Reciprocal learning for cognitive medium access

This paper considers designing efficient medium access strategies for secondary users (SUs) to select frequency channels to sense and access in cognitive radio networks. The interaction among the SUs is considered as a learning problem, in which every SU behaves as an intelligent agent. Each SU believes that its competitors alter their future medium access strategies in proportion to its own current strategy change. These beliefs adapt in accordance with limited information exchange. In this way, each SU can obtain the behavior feature of other users through conjecture, optimize the medium access strategy, and finally achieve the goal of reciprocity, based on which two learning algorithms are proposed. We show that the SUs' stochastic behaviors and beliefs converge to a steady state under some conditions. Numerical results are provided to evaluate the performance of the two algorithms, and show that the achieved system performance gain outperforms some existing protocols.