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In this paper, we study the transmission design for reconfigurableintelligent surface (RIS)-aided multiuser communication networks. Differentfrom most of the existing contributions, we consider long-term CSI-basedtransmission design, where both the beamforming vectors at the base station(BS) and the phase shifts at the RIS are designed based on long-term CSI, whichcan significantly reduce the channel estimation overhead. Due to the lack ofexplicit ergodic data rate expression, we propose a novel deep deterministicpolicy gradient (DDPG) based algorithm to solve the optimization problem, whichwas trained by using the channel vectors generated in an offline manner.Simulation results demonstrate that the achievable net throughput is higherthan that achieved by the conventional instantaneous-CSI based scheme whentaking the channel estimation overhead into account.

Long-Term CSI-based Design for RIS-Aided Multiuser MISO Systems Exploiting Deep Reinforcement Learning