Distributed Optimization of Channel Access Strategies in Reactive Cognitive Networks

In reactive cognitive networks, the channel access and the transmission decisions of the cognitive terminals have a long-term effect on the network dynamics. When multiple cognitive terminals coexist, the optimization and implementation of their strategy is challenging and may require considerable coordination overhead. In this paper, such challenge is addressed by a novel framework for the distributed optimization of transmission and channel access strategies. The objective of the cognitive terminals is to find the optimal action distribution depending on the current network state. To reduce the coordination overhead, in the proposed framework the cognitive terminals distributively coordinate the policy, whereas the action in each individual time slot is independently selected by the terminals. The optimization of the transmission and channel access strategy is performed iteratively by using the alternate convex optimization technique, where at each iteration a cognitive terminal is selected to optimize its own action distribution while assuming fixed those of the other cognitive terminals. For a traditional primary-secondary user network configuration, numerical results show that the proposed algorithm converges to a stable solution in a small number of iterations, and a limited performance loss with respect to the perfect coordinated case.