A Non-Stationary Channel Model for RIS-Mounted UAV-Assisted ISAC Systems

This paper introduces a novel unmanned aerial vehicle (UAV)-mounted reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication (ISAC) framework that synergistically amalgamates data transmission with environmental awareness to enhance both spectral efficiency and system performance. However, the practical deployment of this system encounters challenges such as severe path loss and frequent line-of-sight (LoS) obstructions, especially in dynamic environments. To address these issues, we employ an RIS-equipped UAV that dynamically adapts its reflection coefficients, optimizing both communication and sensing in a multiple-input multiple-output (MIMO)-based wireless network, where a base station (BS) serves a user and simultaneously detects a target. This framework is further extended to a multi-user downlink scenario, where the BS serves multiple users via the RIS-mounted UAV while jointly performing target sensing. By leveraging the UAV’s mobility and considering multiple scatterers, the system optimally reconfigures its RIS phase shifts to improve sensing accuracy and ensure communication reliability.We derive analytical expressions for both communication and sensing rates to evaluate system performance and examine the impact of RIS reflection phase configurations on the statistical properties of the channel, providing valuable design insights for effective deployments. To accommodate the non-stationary effects in UAV-assisted RIS channels, our model incorporates real-time UAV velocity variations and their influence on beamforming. Furthermore, we characterize key propagation metrics, including spatial cross-correlation, temporal autocorrelation, and frequency correlation functions, along with channel capacity analysis. Theoretical and simulation results validate the proposed channel model, demonstrating improvements in system performance.