In this paper, we consider the problem of jointly controlling the configuration of a Reconfigurable Intelligent Surface (RIS) with unit elements of discrete responses and a codebook-based transmit precoder in RIS-empowered Multiple-Input Single-Output (MISO) communication systems. The adjustable elements of the RIS and the precoding vector need to be jointly modified in real time to account for rapid changes in the wireless channels, making the application of complicated discrete optimization algorithms impractical. We present a novel Multi-Branch Attention Convolutional Neural Network (MBACNN) architecture for this design objective which is optimized using NeuroEvolution (NE), leveraging its capability to effectively tackle the non-differentiable problem arising from the discrete phase states of the RIS elements. The channel matrices of all involved links are first passed to separate self-attention layers to obtain initial embeddings, which are then concatenated and passed to a convolutional network for spatial feature extraction, before being fed to a per-element multi-layered perceptron for the final RIS phase configuration calculation. Our MBACNN architecture is then extended to multi-RIS-empowered MISO communication systems, and a novel NE-based optimization approach for the online distributed configuration of multiple RISs is presented. The superiority of the proposed single-RIS approach over both learning-based and classical discrete optimization benchmarks is showcased via extensive numerical evaluations over both stochastic and geometrical channel models. It is also demonstrated that the proposed distributed multi-RIS approach outperforms both distributed controllers with feedforward neural networks and fully centralized ones.

翻译：本文研究了在可重构智能表面（RIS）赋能的单用户多输入单输出（MISO）通信系统中，联合控制具有离散响应单元的可重构智能表面配置与基于码本的发射预编码器的问题。为了适应无线信道的快速变化，RIS的可调单元和预编码向量需要实时联合调整，这使得应用复杂的离散优化算法变得不切实际。针对此设计目标，我们提出了一种新颖的多分支注意力卷积神经网络（MBACNN）架构，并利用神经进化（NE）方法对其进行优化，以有效应对由RIS单元离散相位状态引起的不可微问题。首先，所有相关链路的信道矩阵被输入到各自的自注意力层以获得初始嵌入表示，随后这些嵌入被拼接并输入卷积网络进行空间特征提取，最后馈入一个逐单元的多层感知机以计算最终的RIS相位配置。我们进一步将所提MBACNN架构扩展到多RIS赋能的MISO通信系统，并提出了一种基于神经进化的新型在线分布式多RIS配置优化方法。通过在随机和几何信道模型上进行大量数值评估，证明了所提单RIS方案在性能上优于基于学习的基准方法和经典的离散优化基准方法。同时，实验结果表明，所提出的分布式多RIS方案在性能上优于采用前馈神经网络的分布式控制器以及完全集中式的控制器。