Maximum-Likelihood User Localization in Active-RIS Empowered mmWave Wireless Networks

This paper considers the user localization problem in a single-cell system operating at millimeter wave (mmWave), wherein the serving base station (BS) is aided by an active reconfigurable intelligent surface (RIS). For the considered scenario, the maximum likelihood (ML) estimator for the user position and orientation is presented, along with two sub-optimal lower-complexity estimation strategies. Numerical results are provided to show the effectiveness of the proposed solutions.

I. Introduction

Reconfigurable Intelligent Surfaces (RISs) are attracting a huge interest both in industry and in academia for their ability to alter the propagation environment [1]. Besides improving the coverage of wireless networks, it has been recently shown that they also play a key role in user localization. Precisely, RISs are able to provide coverage to previously shadowed areas and serve as virtual anchor points [2]; moreover, they can be useful for target tracking [3], simultaneous localization and mapping (SLAM) procedures [4], and radar based localization [5] and detection [6], [7]. Controlling the RIS in such applications is not trivial. Adaptive RIS configurations have been explored, investigating the trade off between performance and training overhead [5], [8].