The paper studies the problem of designing the Intelligent Reflecting Surface
(IRS) phase shifters for Multiple Input Single Output (MISO) communication
systems in spatiotemporally correlated channel environments, where the
destination can move within a confined area. The objective is to maximize the
expected sum of SNRs at the receiver over infinite time horizons. The problem
formulation gives rise to a Markov Decision Process (MDP). We propose a deep
actor-critic algorithm that accounts for channel correlations and destination
motion by constructing the state representation to include the current position
of the receiver and the phase shift values and receiver positions that
correspond to a window of previous time steps. The channel variability induces
high frequency components on the spectrum of the underlying value function. We
propose the preprocessing of the critic's input with a Fourier kernel which
enables stable value learning. Finally, we investigate the use of the
destination SNR as a component of the designed MDP state, which is common
practice in previous work. We provide empirical evidence that, when the
channels are spatiotemporally correlated, the inclusion of the SNR in the state
representation interacts with function approximation in ways that inhibit
convergence.

本文研究了设计智能反射面（IRS）相移器以适应时空相关信道环境中具有运动目标的多输入单输出（MISO）通信系统的问题。旨在最大化接收器无限时间内预期信噪比总和。提出了一种深度演员 - 评论家算法，将当前接收器位置和先前时间步窗口内对应于相移值和接收器位置的状态表示纳入考虑，以解决该问题。