Deep Reinforcement Learning for IRS Phase Shift Design in Spatiotemporally Correlated Environments. (arXiv:2211.09726v1 [cs.IT])

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 …

arxiv design environments irs reinforcement reinforcement learning shift