Reflection Coefficients Optimization in IRS-assisted Communication: An Evolutionary Game Approach

Intelligent reflecting surface (IRS) alters the signal propagation via tuning a large number of passive reflection units, and is a promising solution to enhance the wireless communication. In this paper, we aim to improve the level of mutual information of the IRS-assisted communication system by optimizing the IRS reflection coefficients. Specifically, in order to solve the established IRS reflection coefficients optimization problem, the dynamic adjustment process is first modeled as an evolutionary game (EG) model, and then the replication dynamic equations about the revenue function are established. Next, for the single-mode constraints of the reflection coefficients, the reinforcement learning (RL) method is adopted, and the strategy iteration algorithm is used to solve the evolutionary stability strategy. Simulation results demonstrate that our proposed algorithm achieves substantially increased mutual information compared to the traditional scheme without IRS and another benchmark scheme.