A Novel Deep Reinforcement Learning-based Approach for Enhancing Spectral Efficiency of IRS-assisted Wireless Systems
This addresses spectral efficiency enhancement for downlink multi-user MISO systems, representing an incremental improvement by applying existing DRL methods to a known bottleneck in IRS optimization.
The paper tackles the non-convex joint optimization of active transmit beamforming and passive phase shift matrices in IRS-assisted wireless systems to enhance spectral efficiency, using deep reinforcement learning frameworks like DDPG and TD3, with simulation results showing TD3 performs more satisfactorily in various situations.
This letter investigates an intelligent reflecting surfaces (IRS)-enhanced network from spectral efficiency enhancement point of view for downlink multi-user (MU) multi-input-single-output systems (MISO). In contrast to previous works which mainly focused on alternative optimization methods, we investigate the non-convex joint optimization problem of the active transmit beamforming matrix at the base station together with the passive phase shift matrix at the IRS by utilizing two deep reinforcement learning frameworks, i. e., deep deterministic policy gradient (DDPG) and twin delayed DDPG (TD3). Simulation results reveal that the neural networks in the latter scheme perform generally more satisfactorily in various situations.