Distributed channel assignment for network MIMO: game-theoretic formulation and stochastic learning

Abstract

The cooperative frequency reuse among base stations (BSs) can improve the system spectral efficiency by reducing the intercell interference through channel assignment and precoding. This paper presents a game-theoretic study of channel assignment for realizing network multiple-input multiple-output (MIMO) operation under time-varying wireless channel. We propose a new joint precoding scheme that carries enhanced interference mitigation and capacity improvement abilities for network MIMO systems. We formulate the channel assignment problem from a game-theoretic perspective with BSs as the players, and show that our game is an exact potential game given the proposed utility function. A distributed, stochastic learning-based algorithm is proposed where each BS progressively moves toward the Nash equilibrium (NE) strategy based on its own action-reward history only. The convergence properties of the proposed learning algorithm toward an NE point are theoretically and numerically verified for different network topologies. The proposed learning algorithm also demonstrates an improved capacity and fairness performance as compared to other schemes through extensive link-level simulations.