Temporal Cooperative Localization for RIS-Assisted mmWave Systems

Reconfigurable intelligent surface (RIS) is envisioned to be a promising new solution for high-precision user localization in non-line-of-sight (NLoS) scenarios. However, existing RIS-assisted localization systems may experience significant performance degradation in mobile environments with rapidly changing network topologies. To address this problem, this paper proposes a RIS-assisted millimeter wave (mmWave) localization system that leverages temporal cooperation for localization performance improvement. In particular, we exploit the statistical temporal correlation of user locations over multiple time slots to decompose the Fisher information matrix (FIM), and then derive the corresponding Bayesian Cramér-Rao bound (BCRB). An efficient passive beamforming (PBF) design algorithm based on manifold optimization is proposed to minimize the BCRB. Numerical results indicate that the proposed localization scheme significantly enhances the positioning accuracy, particularly in environments with limited LoS channels.