Using RIS to Support/Prevent Passive Sensing: RIS-enabled Passive Sensing Performance and RIS Configuration Study

Passive sensing that extracts human occupancy and activity information from wireless signal propagation properties has drawn growing interest in recent years. With wireless signals pervasively accessible, passive sensing brings both opportunities and threats to privacy. To leverage the sensing benefits, improving passive sensing accuracy and granularity is pursued. However, in turn, this increases the difficulty of protecting information. In this study, we investigate the use of reconfigurable intelligent surface (RIS) to influence passive sensing for different needs, i.e., to support or to prevent sensing. This paper presents the first theoretical performance analysis of RIS-enabled passive sensing and thereof, addresses the optimal RIS configurations that can improve or degrade the sensing accuracy as desired. In specific, with knowledge of channel features in target sensing scenes of interest, we model the passive sensing performance by a signal receiver to identify the target scenes when a RIS is utilized in the environment. We derive closed-form probability metrics of correct sensing, i.e., the accuracy, and false sensing of the scenes, with which we optimize RIS configuration to maximize/minimize the sensing accuracy. A heuristic algorithm, termed RIS- PS, is developed, solving the RIS configurations in a low complexity. In simulations, we verify our theoretical results and the performance of RIS-PS. Results show RIS's ability in both light-of-sight and non-light-of-sight passive sensing scenarios.