Joint Optimization of IRS-assisted MIMO Communications through a Deep Contextual Bandit Approach

The multiple-input multiple-output (MIMO) communications and the intelligent re- flecting surfaces (IRSs) have been envisioned as key technologies for beyond 5G mobile networks. However, the computational complexity of conventional approaches to jointly optimize IRS-assisted MIMO communication systems constitutes a major limitation to their deployment. In this paper, we present an innovative contextual bandit (CB)-based approach for the optimization of the MIMO precoders and the IRS phase-shift matrix en- tries. The proposed optimization framework, termed as deep contextual bandit-oriented deep deterministic policy gradient (DCB-DDPG), considers a CB formulation with con- tinuous state and action spaces. The simulation results show that our proposal performs remarkably better than state-of-the-art heuristic methods in high-interference scenarios.