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arXiv 2602.13265eess.SPcs.ITmath.IT
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通过增强型近端策略优化算法实现SIM辅助的安全移动通信

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

Dalian Maritime University(大连海事大学);Jilin University(吉林大学);Beijing Jiaotong University(北京交通大学);Nanyang Technological University(南洋理工大学)

Wenxuan Ma, Bin Lin, Hongyang Pan, Geng Sun, Enyu Shi, Jiancheng An, Chau Yuen

AI总结:

本文提出PPO-BOP算法,通过增强型近端策略优化解决SIM辅助安全移动通信中的信道不确定性、多用户干扰和硬件损伤问题,提升保密率性能。

摘要:

随着第六代(6G)无线通信网络的发展,安全挑战日益突出,尤其是对于移动用户(MUs)而言。作为一种有前途的解决方案,物理层安全(PLS)技术利用无线信道的固有特性提供安全保障。特别是,堆叠智能超材料(SIM)通过其多层结构直接操控电磁波,为以节能方式提高PLS性能提供了显著潜力。因此,在这项工作中,我们研究了在窃听威胁下为移动用户(MUs)设计的SIM辅助安全通信系统,解决了诸如移动环境中信道不确定性、多个MU干扰和残余硬件损伤等实际挑战。因此,我们提出了一个联合功率和相位移优化问题(JPPSOP),旨在最大化所有MUs的可实现保密率(ASR)。鉴于该优化问题的非凸性和动态性,我们提出了一种增强型近端策略优化算法,包含双向长短期记忆机制、非策略数据利用机制和策略反馈机制(PPO-BOP)。通过这些机制,所提出的算法可以有效捕捉短期信道衰落和长期MU移动性,提高样本利用效率,并增强探索能力。广泛的仿真结果表明,PPO-BOP在ASR方面显著优于基准策略和其他深度强化学习算法。

英文摘要:

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

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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