Distributed Channel selection in multilink MISO networks: Stochastic learning under time-varying channel states

In this paper, we study the channel selection problem for selfish and altruistic precoding in multilink multiple-input single-output (MISO) networks from a distributed game-theoretic perspective. Our goal is to find for each link a proper channel selection strategy that is robust against time-varying channel states. This motivates the development of stochastic learning that finds Nash equilibrium (NE) of an expected game. The convergence properties of the proposed learning algorithm are theoretically and numerically verified. The proposed algorithm demonstrates good sum-rate performance in the system-level simulation of a multilink MISO network based on the 3GPP-LTE model.