AI 中文总结
本文针对捏天线系统（PASS）的异构安全多用户传输问题，建立含三类安全模式的统一传输框架，开发HSPPO与MRHA-DPO两种学习控制器，实现兼顾效率与能力的物理层安全传输。
AI 中文摘要
本文研究捏天线系统（PASS）中的异构安全多用户传输，该系统中动态可调的捏天线可重塑导波和自由空间传播，以提升通信与保密性能。与传统仅通过权重或阈值表征不同安全需求的物理层安全设计不同，异构服务可能会针对每个信息流改变每个接收端的逻辑角色。为解决该问题，本文建立了统一的角色依赖型PASS传输框架，包含低、中、高安全模式：低安全模式最大化合法用户最小速率；中安全模式保护机密信息流免受外部窃听者攻击；高安全模式进一步防止非目标合法用户拦截未授权信息。信息波束成形、人工噪声与捏天线位置的联合优化被建模为长时连续控制问题，随后开发了两种基于学习的控制器以提供互补的复杂度-性能权衡：其一为异构感知安全的近端策略优化（HSPPO），其将模式特定的速率与保密违规直接转化为嵌入近端策略优化优势函数的归一化平滑反馈，实现轻量且对违规敏感的控制；其二为多关系层次感知的扩散策略优化（MRHA-DPO），其结合PASS感知的多关系图编码器、图条件层次速度网络及精确逆DPO训练，实现拓扑感知与表达性控制。所提框架使通用PASS平台可灵活支持依赖服务的保密需求，同时平衡在线效率与控制能力。
英文摘要
This paper investigates heterogeneous secure multi-user transmission in pinching-antenna systems (PASS), where dynamically adjustable pinching antennas reshape both guided-wave and free-space propagation to improve communication and confidentiality performance. Unlike conventional physical-layer security designs that represent different security requirements merely through weights or thresholds, heterogeneous services may change the logical role of each receiver for each information stream. To address this issue, we establish a unified role-dependent PASS transmission framework comprising low-, medium-, and high-security modes. These modes respectively maximize the minimum legitimate-user rate, protect confidential streams against external eavesdroppers, and further prevent non-target legitimate users from intercepting unauthorized information. The resulting joint optimization of information beamforming, artificial noise, and pinching-antenna positions is formulated as a long-horizon continuous-control problem. Two learning-based controllers are then developed to provide complementary complexity-performance tradeoffs. First, heterogeneous security-aware proximal policy optimization (HSPPO) directly transforms mode-specific rate and secrecy violations into normalized smooth feedback embedded in the proximal-policy-optimization advantage, enabling lightweight and violation-sensitive control. Second, multi-relational hierarchy-aware diffusion policy optimization (MRHA-DPO) combines a PASS-aware multi-relational graph encoder, a graph-conditioned hierarchical velocity network, and exact-inversion DPO training to achieve topology-aware and expressive control. The proposed framework enables a common PASS platform to flexibly support service-dependent confidentiality requirements while balancing online efficiency and control capability.
Comments13 pages, 7 figures
