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\%.

翻译：堆叠智能超表面（SIM）由多层可重构透射超表面构成，正作为变革性技术在未来无线通信安全领域崭露头角。本文研究将SIM集成到多用户多输入多输出（MIMO）系统中以增强物理层安全性。我们提出了一种新颖的系统架构：每个基站（BS）天线传输专用的单用户数据流，而多层SIM在电磁域执行基于波束成形的波前调控，从而避免复杂的基带数字预编码需求，显著降低硬件开销。为最大化加权和保密速率（WSSR），我们构建了关于基站功率分配与SIM相移的联合预编码优化问题，该问题因目标函数复杂性及优化变量间的耦合而具有高维非凸特性。为此，我们提出一种流形增强的异构多智能体持续学习（MHACL）框架，该框架融合梯度表征与双尺度策略优化，能在安全通信需求较高的动态环境中实现鲁棒性能。进一步，我们开发了SIM-MHACL（SIMHACL）——一种低复杂度学习模板，它将相位协调嵌入乘积流形结构，将指数级搜索空间降至线性复杂度，同时保持物理可实现性。仿真结果表明，所提框架在SIM辅助系统中可实现毫秒级单次迭代训练，显著优于多种基线方案，其中SIMHACL在取得与MHACL相当WSSR的同时，将计算时间降低了30\%。