Multi-Group Multicasting Using Reconfigurable Intelligent Surfaces: A Deep Learning Approach

Thanks to the ability to customize the propagation of wireless signals, reconfigurable intelligent surfaces (RISs) have great potential in enhancing the performance of future wireless communication systems. While the majority of papers in the literature considers single-RIS scenarios, the potential deployment of multiple RISs, that offer ubiquitous connectivity for diverse user demands, calls for further investigation. This paper considers a downlink multi-group multicast system underpinned by multiple RISs and aims to maximize the sum spectral efficiency subject to an overall transmit power constraint. This optimization problem is highly challenging due to the non-convex, non-smooth, and non-differentiable properties of the objective function, as well as the non-convex unit modulus constraint. To address this complex problem, we propose a model-driven deep learning (DL) approach. This involves first solving the joint active and passive beamforming design through an alternating projected gradient (APG) algorithm with an approximate objective function. The APG algorithm is then unfolded into an iterative procedure using multiple layers with trainable parameters. A network training method is proposed to ensure that the performance improves with the number of iterations. Remarkably, our model is also nicely generalizable to the imperfect channel state information (CSI) scenario, without any change to the network architecture, by simply combining the recursive approximation method and adding some long/short-term trainable parameters to accommodate the two-timescale transmission protocol. Our simulation results demonstrate the superiority of our proposed DL method over existing algorithms in terms of both complexity and performance. Specifically, the proposed model-driven DL method reduces the runtime by approximately 80% compared to the APG algorithm and 99.97% compared to the majorization–minimization algorithm, while it also achieves comparable performance. Furthermore, our proposed method for imperfect CSI scenarios reduces the performance loss by 5%-10% compared to the proposed method without considering the influence of imperfect CSI.