Precoding Strategy Selection for Cognitive MIMO Multiple Access Channels Using Learning Automata

In this paper, we study the quantized precoding strategy selection for multiple-input multiple-output (MIMO) multiple access channels (MAC) in cognitive radio (CR) networks through a game-theoretic perspective. Since the secondary users in such system are difficult to be coordinated by a centralized authority, they are noncooperative and attempt to maximize their own payoffs selfishly in a distributed method. We propose a noncooperative precoding strategy selection game and find that it is a potential game which possesses at least one pure strategy Nash equilibrium. A decentralized learning algorithm with a small amount of feedback is proposed to obtain Nash equilibrium. We prove that the proposed algorithm can converge to a pure strategy Nash equilibrium. Simulation results are provided to verify our analysis.