Spectral Efficiency Optimization for RIS-Aided Multiuser MISO System Using Deep Reinforcement Learning

Intelligent reflective surface (IRS) is speculated to be one of the key enabling technologies for the beyond fifth-generation (B5G) of wireless communication systems since it reconfigures the wireless propagation environment by tuning the incoming waveform's phase shift and amplitude and effectively improve the efficiency and coverage of wireless networks. In this paper, we investigate the spectral efficiency (SE) of multiuser multiple-input single-output (MISO) system, where the direct channel between the user and the BS is blocked and the signal transmission between the user and the BS is assisted by IRS. To maximize the total SE of the system, we jointly optimize the phase shift matrix at the IRS and the user transmit power allocation under the constraints of the system's total power. Since the optimization problem is nonconvex and the objective function cannot be obtained in closed-form, we develop the twin delayed deep deterministic (TD3) policy gradient algorithm by leveraging recent advances in deep reinforcement learning (DRL). The algorithm obtains the optimal solution by continuously interacting with the system environment and learning, in which a set of channel state information (CSI) vectors is generated in an offline way, and then these data sets are used to train the neural networks. The numerical results show that the proposed algorithm can greatly improve the system SE after training and learning. In addition, the system performance and network convergence speed can be effectively improved by adjusting the network parameters.