Distributed best-relay node selection in underlay cognitive radio networks: A restless bandits approach

In this paper, we propose a distributed best-relay node selection scheme to maximize the achievable data rate for cooperative communications over underlay-paradigm based CR networks, and meanwhile guarantee that the primary link is provided with a minimum-rate for a certain percentage of time. Specifically, we formulate the CR relay network as a restless bandit system, where the finite-state Markov channel model is used to characterize the time-varying channel state. With this stochastic optimization formulation, the optimal relay node selection policy is obtained by a primal-dual priority-index heuristic, which can dramatically reduces the computation and implementation complexity. Simulation results are presented to show the effectiveness of the proposed scheme.