Optimizing Reconfigurable Intelligent Surface-Assisted Distributed Wireless Sensing

Wireless sensing has recently attracted significant interest due to its potential to support a wide range of immersive human-machine interactive applications without requiring any extra devices to be carried out by human users. However, previous studies have shown that wireless sensing performance can be significantly degraded if the relative locations of the transmitters, human users, and receivers are non-ideal and/or the distances between the user and receivers are large. These constraints hinder the wide applications of wireless sensing in many practical scenarios. A promising approach for wireless sensing is through the use of reconfigurable intelligent surfaces (RISs) that can control the propagation environment to create a customizable wireless environment for wireless sensing. To this end, in this paper, the use of RIS-assisted wireless sensing for enhanced human gesture recognition is investigated. A novel RIS-assisted distributed wireless sensing framework that utilizes federated learning (FL) is proposed to enable collaborative model training among decentralized receivers. Then, a novel metric, called human-influencing signal-to-interference ratio (HSIR), is introduced to characterize the quality of locally recorded data as well as its impact on the performance of wireless sensing. To alleviate the model draft problem of FL-assisted wireless sensing, caused by spatial heterogeneity of the quality of wireless sensing data at different receivers, the optimal amplitudes and phases of the RIS are derived so as to improve the HSIR of a set of low-performance receivers located at non-ideal locations. Simulation results show that the proposed RIS-assisted system can significantly improve wireless sensing accuracy by up to 20.1% compared to traditional distributed system.