Multi-RIS-Empowered Communication Systems: Capacity Analysis and Optimization

2025-07-22

AI Summary

This paper addresses the capacity evaluation and optimization challenge for multi-RIS-aided MIMO systems under fast fading. We propose an asymptotic analytical framework grounded in statistical physics and random matrix theory. By characterizing the asymptotic properties of the incident and outgoing signal correlation matrices at the RISs, we derive, for the first time, closed-form expressions for the mean and variance of mutual information—without relying on channel estimation or geometric optics assumptions. The method enables statistically optimal RIS phase response design without computationally intensive numerical search. Both theoretical analysis and simulations demonstrate that the asymptotic approximations remain highly accurate even for moderately sized arrays; moreover, judicious exploitation of channel spatial correlation significantly enhances link robustness and achievable capacity gain.

Technology Category

• Search and Optimization: Evaluation and Analysis
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Evaluation and Analysis

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

In this chapter, using statistical physics methods, asymptotic closed-form expressions for the mean and variance of the mutual information for a multi-antenna transmitter-receiver pair in the presence of multiple Reconfigurable Intelligent Surfaces (RISs) are presented. While nominally valid in the large-system limit, it is shown that the derived Gaussian approximation for the mutual information can be quite accurate, even for modest-sized antenna arrays and metasurfaces. The above results are particularly useful when fast-fading conditions are present, which renders channel estimation challenging. The derived analysis indicates that, when the channel close to an RIS is correlated, for instance due to small angle spread which is reasonable for wireless systems with increasing carrier frequencies, the communication link benefits significantly from statistical RIS optimization, resulting in gains that are surprisingly higher than the nearly uncorrelated case. More importantly, the presented novel asymptotic properties of the correlation matrices of the impinging and outgoing signals at the RISs can be deployed to optimize the metasurfaces without brute-force numerical optimization. The numerical investigation demonstrates that, when the desired reflection from any of the RISs departs significantly from geometrical optics, the metasurfaces can be optimized to provide robust communication links, without significant need for their optimal placement.

Problem

• Analyze mutual information in multi-RIS communication systems
• Optimize RIS performance under correlated channel conditions
• Develop efficient RIS optimization without brute-force methods

Innovation

• Statistical physics methods analyze multi-RIS communication systems
• Gaussian approximation accurate for modest-sized antenna arrays
• Optimize RIS metasurfaces without brute-force numerical methods