Circular RIS-Enabled Channel Estimation and Localization for Multi-user ISAC Systems

Integrated sensing and communication (ISAC) is emerging as a key enabler to address the increasing demands of spectrum and throughput for ubiquitous sensing and communication. Hereafter, we consider the channel estimation and localization for multi-user ISAC systems assisted by the reconfigurable intelligent surface (RIS) technology. In order to acquire precise environmental information, we propose a novel circular RIS architecture with circularly arranged reflecting unit cells. By modeling the training signal as a low-rank third-order canonical polyadic tensor, we transform the channel estimation problem into a tensor deconstruction task. By leveraging the phase mode excitation principle, we develop a customized RIS training pattern, and retrieve the equivalent channel parameters by subspace estimation algorithms. By exploring the characteristics of RIS array manifolds and free-space propagation, we implement a unique decoupling of channel parameters for user localization, which cannot be supported by traditional linear RIS topologies. Moreover, the design degrees of freedom in the spatial and frequency dimensions are also exploited to further enhance the proposed algorithms. Simulation results indicate that the circular RIS-enabled channel estimation schemes can recover the propagation information with remarkable accuracy, thereby offering a high-level resolution of localization.