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.
翻译:实现实用智能反映表面(IRS)辅助多用户通信(IRS-MUC)系统的实用智能化多用户辅助系统,关键取决于利用准确的频道状态信息(CSI)的适当波束成型设计。然而,IRS-MUC系统中的频道估计(CE)由于IRS涉及许多反射要素,需要大量培训间接费用。在本文件中,我们采取深层学习方法,隐含地学习历史频道特征,直接预测IRS阶段在下一个时段的变化,以最大限度地实现IRS-MUC系统的平均可实现总和率,同时考虑到用户的流动性。通过这样做,只需要低振荡式多输出单发式(MISO) CE来传输波形设计,从而大大减少CE的间接费用。为此,我们首先开发了一个位置感应变长长的短期记忆网络(LA-CLNet),以促进IRS的预测性成型,在IRS-MC系统中联合采用革命性和经常性单位来利用频道的空间和时间性特征。鉴于在IMISISISI系统预测性完美访问阶段,拟议的IMISA系统升级系统系统将全面转换为Simal化系统,因此,拟议的SIMISA系统升级系统升级系统升级系统升级系统将全面升级系统升级系统升级为S-S-S-SIMFA的系统升级系统升级系统升级系统升级系统升级系统升级系统升级系统升级系统升级系统升级系统。