Distributed optimization of transmission strategies in reactive cognitive networks

A framework for the distributed optimization of reactive cognitive networks with multiple secondary users is presented. The secondary users iteratively locate the policy maximizing their aggregate performance under bounds on the maximum performance loss caused to the primary users. The policy accounts for the impact of interference on the dynamics of the primary users´ network due to transmission and channel access protocols. To minimize coordination overhead, it is assumed that the secondary users only coordinate the policy, whereas actions in each slot are independently selected by the individual secondary user based on the agreed policy. The distributed optimization technique proposed herein is based on alternating convex optimization. Numerical results are presented assessing the performance of the obtained transmission policy with respect to the optimal centralized and fully-coordinated policy.