Hao Luo, Daniel Lu, Ahmed Alkhateeb, and Georgios C. Trichopoulos


We present the progress on localization in mmWave wireless communication systems using reconfigurable intelligent surfaces (RISs) and non-line-of-sight (NLoS) imaging methods. Reconfigurable intelligent surfaces, with their large number of antennas, offer an interesting opportunity for high spatial-resolution imaging. We propose a novel RIS-aided integrated imaging and communication system that can reduce the RIS beam training overhead for communication by leveraging the imaging of the surrounding environment. In particular, using the RIS as a wireless imaging device, our system constructs the scene depth map of the environment, including the mobile user. Then, we develop a user detection algorithm that subtracts the background and extracts the mobile user attributes from the depth map. These attributes are then utilized to design the RIS interaction vector and the beam selection strategy with low overhead. Simulation results show that the proposed approach can achieve comparable beamforming gain to the optimal/exhaustive beam selection solution while requiring 1000 times less beam training overhead. Additionally, we leverage our prior work on mmWave NLoS imaging and propose a method for future wireless communication systems to enable localization by taking advantage of the large spatio-temporal resolution.