  The realization of practical intelligent reflecting surface (IRS)-assisted
multi-user communication (IRS-MUC) systems critically depends on the proper
beamforming design exploiting accurate channel state information (CSI).
However, channel estimation (CE) in IRS-MUC systems requires a significantly
large training overhead due to the numerous reflection elements involved in
IRS. In this paper, we adopt a deep learning approach to implicitly learn the
historical channel features and directly predict the IRS phase shifts for the
next time slot to maximize the average achievable sum-rate of an IRS-MUC system
taking into account the user mobility. By doing this, only a low-dimension
multiple-input single-output (MISO) CE is needed for transmit beamforming
design, thus significantly reducing the CE overhead. To this end, a
location-aware convolutional long short-term memory network (LA-CLNet) is first
developed to facilitate predictive beamforming at IRS, where the convolutional
and recurrent units are jointly adopted to exploit both the spatial and
temporal features of channels simultaneously. Given the predictive IRS phase
shift beamforming, an instantaneous CSI (ICSI)-aware fully-connected neural
network (IA-FNN) is then proposed to optimize the transmit beamforming matrix
at the access point. Simulation results demonstrate that the sum-rate
performance achieved by the proposed method approaches that of the genie-aided
scheme with the full perfect ICSI.
