Abstract:This study focuses on the optimization of a single-cell multi-user multiple-input multiple-output (MIMO) system with multiple large-size reconfigurable intelligent surfaces (RISs). The overall transmit power is minimized by optimizing the precoding coefficients and the RIS configuration, with constraints on users' signal-to-interference-plus-noise ratios (SINRs). The minimization problem is divided into two sub-problems and solved by means of an iterative alternating optimization (AO) approach. The first sub-problem focuses on finding the best precoder design. The second sub-problem optimizes the configuration of the RISs by partitioning them into smaller tiles. Each tile is then configured as a combination of pre-defined configurations. This allows the efficient optimization of RISs, especially in scenarios where the computational complexity would be prohibitive using traditional approaches. Simulation results show the good performance and limited complexity of the proposed method in comparison to benchmark schemes.
Abstract:In this paper we propose a novel framework that aims to jointly design the reflection coefficients of multiple reconfigurable intelligent surfaces (RISs) and the precoding strategy of a single base station (BS) to optimize the tracking of the position and the velocity of a single multi-antenna user equipment (UE). Differently from the literature, and to keep the overall complexity affordable, we assume that RIS optimization is performed less frequently than localization and precoding adaptation. The optimal RIS and precoder strategy is compared with the classical beam focusing strategy and that which maximizes the communication rate. It is shown that if the RISs are optimized for communication, the solution is suboptimal when used for tracking purposes. Numerical results show that it is possible to achieve the 6G positioning requirements in a typical indoor environment with only one BS and a few RISs operating at millimeter waves.