Multi-RIS-Assisted 3D Localization and Synchronization via Deep Learning

Abstract

Reconfigurable intelligent surfaces (RISs) have received considerable attention in applications related to localization. However, operation in multi-path scenarios is challenging from both complexity and performance perspectives. This study presents a two-stage low complexity method for joint three-dimensional (3D) localization and synchronization using multiple RISs. Firstly, the received signals are preprocessed, and an efficient deep learning architecture is proposed to initially estimate the angles of departure (AODs) of the virtual line of sight paths from the RISs to the user. Then, a hybrid asynchronous AOD time-of-arrival-based approach is proposed in the first stage to estimate an initial guess of the position of the user equipment (UE). Finally, in the second stage, an optimization problem is formulated to refine the position of the UE by effectively utilizing the estimated delays and the clock offset. Our comparative study reveals that the proposed method outperforms the existing methods in terms of accuracy and complexity. Notably, the proposed method showcases enhanced robustness against multipath effects when compared to the state-of-the-art approaches.

Keywords

• 3D localization
• deep learning
• mmWave
• reconfigurable intelligent surface
• synchronization