This paper explores Physical-Layer Security (PLS) in Flexible Duplex (FlexD) networks, considering scenarios involving eavesdroppers. Our investigation revolves around the intricacies of the sum secrecy rate maximization problem, particularly when faced with coordinated and distributed eavesdroppers employing a Minimum Mean Square Error (MMSE) receiver. Our contributions include an iterative classical optimization solution and an unsupervised learning strategy based on Graph Neural Networks (GNNs). To the best of our knowledge, this work marks the initial exploration of GNNs for PLS applications. Additionally, we extend the GNN approach to address the absence of eavesdroppers' channel knowledge. Extensive numerical simulations highlight FlexD's superiority over Half-Duplex (HD) communications and the GNN approach's superiority over the classical method in both performance and time complexity.

翻译：本文探讨了灵活双工（FlexD）网络中考虑窃听者场景下的物理层安全（PLS）问题。我们的研究围绕和保密速率最大化问题的复杂性展开，特别考虑了采用最小均方误差（MMSE）接收机的协作式与分布式窃听者。本文的贡献包括：一种迭代式经典优化方案，以及基于图神经网络（GNN）的无监督学习策略。据我们所知，本研究首次探索了GNN在PLS中的应用。此外，我们将GNN方法扩展至窃听者信道状态信息未知的情形。大量数值仿真表明，FlexD在性能上优于半双工（HD）通信，而GNN方法在性能和时间复杂度上均优于经典方法。