Low-Complexity Multi-Agent Continual Learning for Stacked Intelligent Metasurface-Assisted Secure Communications

2026-02-02

AI Summary

This work addresses the trade-off between physical-layer security and hardware overhead in multi-user MIMO systems by proposing a joint optimization framework that integrates base station power allocation with phase control of stacked intelligent metasurfaces (SIMs) to enhance secrecy performance through electromagnetic-domain beamforming. The core innovation lies in embedding a product manifold structure into a heterogeneous multi-agent continual learning framework, enabling linear-complexity cooperative phase search. Two algorithms—MHACL and SIMHACL—are developed, with SIMHACL achieving comparable weighted sum secrecy rates to MHACL while reducing computational time by 30%, reaching millisecond-level per-iteration latency and significantly outperforming existing approaches.

Technology Category

• Machine Learning: Hardware-aware ML
• Search and Optimization: Sampling/Simulation-based Search
• Multiagent Systems: Multiagent Learning

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

Abstract

Stacked intelligent metasurfaces (SIMs), composed of multiple layers of reconfigurable transmissive metasurfaces, are gaining prominence as a transformative technology for future wireless communication security. This paper investigates the integration of SIM into multi-user multiple-input multiple-output (MIMO) systems to enhance physical layer security. A novel system architecture is proposed, wherein each base station (BS) antenna transmits a dedicated single-user stream, while a multi-layer SIM executes wave-based beamforming in the electromagnetic domain, thereby avoiding the need for complex baseband digital precoding and significantly reducing hardware overhead. To maximize the weighted sum secrecy rate (WSSR), we formulate a joint precoding optimization problem over BS power allocation and SIM phase shifts, which is high-dimensional and non-convex due to the complexity of the objective function and the coupling among optimization variables. To address this, we propose a manifold-enhanced heterogeneous multi-agent continual learning (MHACL) framework that incorporates gradient representation and dual-scale policy optimization to achieve robust performance in dynamic environments with high demands for secure communication. Furthermore, we develop SIM-MHACL (SIMHACL), a low-complexity learning template that embeds phase coordination into a product manifold structure, reducing the exponential search space to linear complexity while maintaining physical feasibility. Simulation results validate that the proposed framework achieves millisecond-level per-iteratio ntraining in SIM-assisted systems, significantly outperforming various baseline schemes, with SIMHACL achieving comparable WSSR to MHACL while reducing computation time by 30\%.

Problem

• Stacked Intelligent Metasurfaces
• Physical Layer Security
• Weighted Sum Secrecy Rate
• Multi-Agent Continual Learning
• Non-convex Optimization

Innovation

• Stacked Intelligent Metasurfaces
• Continual Learning
• Manifold Optimization
• Physical Layer Security
• Low-Complexity Beamforming