Ergodic Mutual Information and Outage Probability for SIM-Assisted Holographic MIMO Communications

2025-10-23

AI Summary

This work investigates the ergodic mutual information (EMI) and outage probability for a stacked intelligent metasurface (SIM)-assisted holographic MIMO system—a novel problem not previously addressed. Leveraging large random matrix theory, we derive the asymptotic distribution of EMI; using statistical channel state information (CSI), we establish a tractable closed-form outage probability model; and we propose a low-complexity gradient descent algorithm to jointly optimize SIM phase shifts and transmit covariance matrices. Unlike conventional alternating optimization, our algorithm significantly reduces computational complexity and accelerates convergence. Simulation results validate the theoretical analysis, demonstrating that the proposed scheme outperforms both conventional MIMO and single-layer metasurface systems in terms of both EMI and outage performance.

Technology Category

• Search and Optimization: Metareasoning and Metaheuristics
• Machine Learning: Optimization
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Security and Privacy: Large-scale security measurements

Abstract

Stacked intelligent metasurface (SIM) is a promising enabler for next-generation high-capacity networks that exhibit better performance compared to its single-layer counterpart by means of just wave propagation. However, the study of ergodic mutual information (EMI) and outage probability for SIM-assisted multiple-input-multiple-output (MIMO) systems is not available in the literature. To this end, we obtain the distribution of the MI by using large random matrix theory (RMT) tools. Next, we derive a tight closed-form expression for the outage probability based on statistical channel state information (CSI). Moreover, we apply the gradient descent method for the minimization of the outage probability. Simulation results verify the analytical results and provide fundamental insights such as the performance enhancements compared to conventional MIMO systems and the single-layer counterpart. Notably the proposed optimization algorithm is faster than the alternating optimization (AO) benchmark by saving significant overhead.

Problem

• Analyzing ergodic mutual information for SIM-assisted MIMO systems
• Deriving outage probability using statistical channel state information
• Optimizing system performance with efficient gradient descent methods

Innovation

• Using stacked intelligent metasurface for holographic MIMO communications
• Applying large random matrix theory to obtain mutual information distribution
• Employing gradient descent method to minimize outage probability