Localization and Channel Reconstruction for Extra Large RIS-Assisted Massive MIMO Systems

Abstract

Reconfigurable intelligent surface (RIS) is a promising material that can passively manipulate electromagnetic waves and improve the quality of mobile communication services at a low cost. It can be made large to extend the service region and acquire the ability for localization enhancement. However, the lack of a signal processing module at the RIS makes channel estimation a difficult problem. When employing an extra large RIS, users are in the near field of the RIS and the channel shows spatial nonstationarity, making the problem more complicated. In this paper, these challenges are addressed by a low-overhead joint localization and channel reconstruction scheme proposed for extra large RIS-assisted massive multi-input multi-output systems. This scheme can accurately identify the visibility region (VR) of each user, find the user positions by exploiting the near-field characteristics, and reconstruct the channels by jointly utilizing the pilots of multiple users. Numerical results demonstrate that the identified VR covers more than 97% of the real VR, and the user localization accuracy reaches centimeter level at the millimeter-wave frequency band. A more accurate channel reconstruction result than that of existing works can also be obtained. This work verifies the great potential of RIS and is a solid step toward the integration of communication and sensing.