Swarm Intelligence Optimization of Multi-RIS Aided MmWave Beamspace MIMO

TL;DR

This work tackles the challenge of a multi-RIS aided mmWave beamspace MIMO system with blocked direct BS–UE links by employing maximum ratio transmission and a low-complexity particle swarm optimization strategy to jointly optimize beam selection, power allocation, and RIS phase profiles. Its key contributions are the introduction of a distributed multi-RIS architecture, a PSO-based joint optimization framework, and a detailed analysis of complexity–performance trade-offs, including the finding that increasing RIS unit cells can yield larger sum-rate gains than increasing the number of BS beams due to beamspace sparsity. The results demonstrate meaningful sum-rate improvements over random configurations and highlight the importance of RIS design choices for energy efficiency in beamspace mmWave systems. Overall, the approach provides practical insights for deploying RIS-aided beamspace MIMO in scenarios with blocked links, suggesting a stronger emphasis on expanding RIS capabilities rather than simply widening the BS beam footprint.

Abstract

We investigate the performance of a multiple reconfigurable intelligence surface (RIS)-aided millimeter wave (mmWave) beamspace multiple-input multiple-output (MIMO) system with multiple users (UEs). We focus on a challenging scenario in which the direct links between the base station (BS) and all UEs are blocked, and communication is facilitated only via RISs. The maximum ratio transmission (MRT) is utilized for data precoding, while a low-complexity algorithm based on particle swarm optimization (PSO) is designed to jointly perform beam selection, power allocation, and RIS profile configuration. The proposed optimization approach demonstrates positive trade-offs between the complexity (in terms of running time) and the achievable sum rate. In addition, our results demonstrate that due to the sparsity of beamspace channels, increasing the number of unit cells (UCs) at RISs can lead to higher achievable rates than activating a larger number of beams at the MIMO BS.

Figures (3)

• Figure 1: Illustration of the adopted system model with $J = 4$ and $K = 7$.
• Figure 2: Achievable sum-rate vs. number of UEs for different number of selected beams when $M = 128$.
• Figure 3: Convergence of the proposed PSO scheme, $K = N_s = 8$.