基于图强化学习的非地面网络分布式物理层认证与协作RSMA

中文摘要 AI 辅助

现有的非地面网络（NTN）物理层认证（PLA）方案通常依赖单锚点验证，缺乏认证与传输的联合设计，并且忽视了窃听下的标签隐私泄露问题。本文考虑被动、位置感知的静态窃听者，其无法获取合法信道状态信息（CSI）。在此威胁模型下，我们提出面向多区域NTN系统的安全自适应联邦认证（SAFA-MZ），在确保认证可靠性、功率限制和覆盖约束的同时最大化保密频谱效率（SSE）。其核心思想是将组级认证标签嵌入协作式多层速率分割多址接入（RSMA）传输结构中。私有信号和公共信号联合波束成形，利用人工噪声（AN）降低信息泄露，并通过组差分隐私（GDP）保护标签信息免受推断攻击。此外，用户按语义优先级分组，根据信息重要性分配SSE。我们在认证可靠性和概率保密约束下构建联合SSE最大化问题，优化高空平台站（HAPS）部署、用户关联和RSMA功率分配。该问题通过基于修复的交叉熵方法（RCEM）和图感知优势演员-评论家算法（GA2C）求解。RCEM的计算复杂度随用户数呈二次增长，而GA2C呈线性增长，可实现可扩展的低时延推理。在合谋和非合谋窃听者下的仿真结果表明，所提方法相比单连接传输平均SSE提升高达135%，相比无AN方案提升21%。这些结果证实SAFA-MZ为动态NTN环境提供了可扩展且安全的解决方案。

英文摘要

Existing physical-layer authentication (PLA) schemes for non-terrestrial networks (NTNs) often rely on single-anchor verification, lack joint authentication-transmission design, and ignore tag privacy leakage under eavesdropping. In this paper, we consider passive, location-aware, static eavesdroppers without access to legitimate channel state information (CSI). Under this threat model, we propose secure adaptive federated authentication for multi-zone NTN systems (SAFA-MZ) that maximizes secrecy spectral efficiency (SSE) while ensuring authentication reliability, power limits, and coverage constraints. The main idea is to embed group-level authentication tags into a collaborative multi-layer rate-splitting multiple access (RSMA) transmission structure. Private and common signals are jointly beamformed, artificial noise (AN) is used to reduce information leakage, and group differential privacy (GDP) protects tag information against inference attacks. In addition, users are grouped by semantic priority to allocate SSE based on information importance. We formulate a joint SSE maximization problem under authentication reliability and probabilistic secrecy constraints, optimizing high-altitude platform station (HAPS) placement, user association, and RSMA power allocation. The resulting problem is solved using a repair-based cross-entropy method (RCEM) and a graph-aware advantage actor-critic algorithm (GA2C). RCEM scales quadratically with the number of users, while GA2C scales linearly and achieves scalable, low-latency inference. Simulation results under both colluding and non-colluding eavesdroppers show that the proposed method improves average SSE by up to 135% over single-connect transmission and 21% over the scheme without AN. These results confirm SAFA-MZ offers a scalable and secure solution for dynamic NTN environments.

发表机构

• Tarbiat Modares University（塔比亚特·莫达雷斯大学）

机构由 AI 辅助整理，请以论文原文为准。