Beamforming optimization and system level assessment in RIS-aided MIMO systems comprising hybrid precoding architectures

Abstract

The terahertz (THz) band is a candidate technology for future sixth-generation (6G) wireless networks that could support increasingly demanding requirements, such as high wireless traffic volumes and transmission rates. However, the limited range and high propagation losses at these frequencies present several challenges that must be overcome. One emerging solution is the use of reconfigurable intelligent surfaces (RIS), which optimize communication network performance in combination with ultra-massive multiple-input multiple-output (UM-MIMO) antennas. UM-MIMO’s large number of antennas provides highly directional beams, enabling reliable data propagation from the transmitter to the receiver at THz frequencies. However, it can substantially increase implementation complexity. This paper proposes a joint hybrid precoder and RIS optimization algorithm to overcome these challenges. The algorithm is designed to maximize the achievable rate of THz UM-MIMO communications, by segregating digital and analog precoder computations and adopting hybrid architectures: fully connected (FC), array-of-subarrays (AoSA), and dynamic array-of-subarrays (DAoSA). The proposed algorithm supports multicarrier transmission and the use of multiple, parallel RIS panels deployed throughout the propagation path. Numerical simulations demonstrate the efficiency and versatility of the algorithm, particularly in contexts where THz systems operate under severe constraints. System-level simulations in a 300 GHz office environment reveal that distributing multiple parallel RIS panels throughout the environment yields the maximum achievable throughput. RIS deployment offers the greatest coverage and throughput gains in low-density scenarios but provides diminishing returns as density increases.