SIM-assisted Secure Mobile Communications via Enhanced Proximal Policy Optimization Algorithm

TL;DR

This work investigates an SIM-assisted secure communication system for MUs under the threat of an eavesdropper, addressing practical challenges such as channel uncertainty in mobile environments, multiple MU interference, and residual hardware impairments, and formulate a joint power and phase shift optimization problem (JPPSOP), aiming at maximizing the achievable secrecy rate (ASR) of all MUs.

Abstract

With the development of sixth-generation (6G) wireless communication networks, the security challenges are becoming increasingly prominent, especially for mobile users (MUs). As a promising solution, physical layer security (PLS) technology leverages the inherent characteristics of wireless channels to provide security assurance. Particularly, stacked intelligent metasurface (SIM) directly manipulates electromagnetic waves through their multilayer structures, offering significant potential for enhancing PLS performance in an energy efficient manner. Thus, in this work, we investigate an SIM-assisted secure communication system for MUs under the threat of an eavesdropper, addressing practical challenges such as channel uncertainty in mobile environments, multiple MU interference, and residual hardware impairments. Consequently, we formulate a joint power and phase shift optimization problem (JPPSOP), aiming at maximizing the achievable secrecy rate (ASR) of all MUs. Given the non-convexity and dynamic nature of this optimization problem, we propose an enhanced proximal policy optimization algorithm with a bidirectional long short-term memory mechanism, an off-policy data utilization mechanism, and a policy feedback mechanism (PPO-BOP). Through these mechanisms, the proposed algorithm can effectively capture short-term channel fading and long-term MU mobility, improve sample utilization efficiency, and enhance exploration capabilities. Extensive simulation results demonstrate that PPO-BOP significantly outperforms benchmark strategies and other deep reinforcement learning algorithms in terms of ASR.10.1109/TWC.2026.3658332

Figures (9)

• Figure 1: An SIM-assisted secure communication system for MUs.
• Figure 2: The overall architecture of the proposed PPO-BOP to solve the JPPSOP.
• Figure 3: ASR when adopting different strategies ($M=4$, $N=36$, $P_{\max}=30$ dBm, $R^{\min}=0.5$ bit/s/Hz, $\kappa=0.10$, $lr=0.001$, $b=128$, and $l=3$, where $lr$, $b$, and $l$ represent the learning rate, the batch size, and the number of Bi-LSTM layers of PPO-BOP, respectively).
• Figure 4: Comparison of convergence curves between the proposed PPO-BOP and baseline algorithms. (a) ASR over training episodes. (b) Average reward per episode during training. ($M=4$, $N=36$, $P_{\max}=30$ dBm, $R^{\min}=0.5$ bit/s/Hz, $\kappa=0.10$, $lr=0.001$, $b=128$, and $l=3$).
• Figure 5: Performance comparison of different algorithms in terms of ASR with varying maximum transmit power ($M=4$, $N=36$, $R^{\min}=0.5$ bit/s/Hz, $\kappa=0.10$, $lr=0.001$, $b=128$, and $l=3$).