Reconfigurable intelligent surfaces, with their large number of antennas, offer an interesting opportunity for high spatial-resolution imaging. In this paper, we propose a novel RIS-aided integrated imaging and communication system that can reduce the RIS beam training overhead for communication by leveraging the imaging of the surrounding environment. In particular, using the RIS as a wireless imaging device, our system constructs the scene depth map of the environment, including the mobile user. Then, we develop a user detection algorithm that subtracts the background and extracts the mobile user attributes from the depth map. These attributes are then utilized to design the RIS interaction vector and the beam selection strategy with low overhead. Simulation results show that the proposed approach can achieve comparable beamforming gain to the optimal/exhaustive beam selection solution while requiring 1000 times less beam training overhead.

翻译：可重构智能表面凭借其大量天线，为高空间分辨率成像提供了有趣机遇。本文提出一种新型RIS辅助的集成成像与通信系统，该系统可通过利用周围环境成像来降低RIS通信波束训练开销。具体而言，本系统将RIS作为无线成像设备，构建包含移动用户在内的环境场景深度图。随后我们开发了一种用户检测算法，通过背景减除从深度图中提取移动用户属性。这些属性被进一步用于设计低开销的RIS交互向量和波束选择策略。仿真结果表明，所提方法在实现与最优/穷举波束选择方案相近的波束赋形增益的同时，所需波束训练开销降低1000倍。