Swarm Intelligence Optimization of Multi-RIS Aided MmWave Beamspace MIMO

2025-05-20

AI Summary

This paper addresses the performance optimization of multi-reconfigurable intelligent surface (RIS)-assisted beamspace MIMO systems in millimeter-wave multi-user communications under complete blockage of the direct link. Method: We propose a low-complexity framework jointly optimizing beam selection, power allocation, and RIS phase shifts. To this end, particle swarm optimization (PSO) is introduced for the first time into multi-RIS–beamspace co-design, integrated with maximum-ratio transmission (MRT) precoding and sparse beamspace channel modeling. Results: Theoretical analysis and simulations demonstrate that, under sparse channel conditions, increasing the number of RIS elements yields significantly greater sum-rate gains than expanding the number of activated base station beams. Compared to benchmark schemes, the proposed method achieves substantial sum-rate improvement while reducing computational runtime by an order of magnitude. These results reveal that RIS scale expansion constitutes the dominant performance enhancement mechanism in non-line-of-sight scenarios.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Security and Privacy: Large-scale security measurements

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.

Problem

• Optimizing multi-RIS aided mmWave MIMO with blocked direct links
• Joint beam selection, power allocation, and RIS configuration via PSO
• Enhancing sum rate by increasing RIS unit cells over beams

Innovation

• Particle swarm optimization for RIS configuration
• Maximum ratio transmission for data precoding
• Beamspace channel sparsity utilization