Distributed Stochastic Learning and Adaptation to Primary Traffic for Dynamic Spectrum Access

We design distributed online learning and channel access for secondary users in a cognitive radio network. Our goal is to design channel selection and access that can effectively adapt to a wide range of traffic load patterns in the primary network. We propose a distributed adaptive learning and access policy by applying stochastic learning automata (SLA), where each secondary user (SU) probabilistically chooses one of the most available channels to access, with the channel selection probabilities being updated based on the collision events. Our design includes two underlying distributed learning algorithms: learning of primary channel availabilities from each SU's own sensing history, and SLA-based learning of channel selection from each SU's own collision history for collision avoidance. We show that some existing distributed access policies can be viewed as special cases of our proposed adaptive policy, with a set of fixed channel selection probabilities. Next, we formulate the distributed channel selection and access problem as a noncooperative game. We show that it is an exact potential game with at least one pure strategy Nash equilibrium (NE). We prove that, under our proposed adaptive policy, the channel selection probabilities converge toward a pure strategy NE of the game. Simulation demonstrates the effectiveness of our proposed adaptive policy in a wide range of distributions of mean channel availabilities, as compared with other existing policies.