In Space-air-ground integrated networks (SAGIN), the inherent openness and extensive broadcast coverage expose these networks to significant eavesdropping threats. Considering the inherent co-channel interference due to spectrum sharing among multi-tier access networks in SAGIN, it can be leveraged to assist the physical layer security among heterogeneous transmissions. However, it is challenging to conduct a secrecy-oriented access strategy due to both heterogeneous resources and different eavesdropping models. In this paper, we explore secure access selection for a scenario involving multi-mode users capable of accessing satellites, unmanned aerial vehicles, or base stations in the presence of eavesdroppers. Particularly, we propose a Q-network approximation based deep learning approach for selecting the optimal access strategy for maximizing the sum secrecy rate. Meanwhile, the power optimization is also carried out by an unsupervised learning approach to improve the secrecy performance. Remarkably, two neural networks are trained by unsupervised learning and Q-network approximation which are both label-free methods without knowing the optimal solution as labels. Numerical results verify the efficiency of our proposed power optimization approach and access strategy, leading to enhanced secure transmission performance.

翻译：在空天地一体化网络（SAGIN）中，固有的开放性和广泛的广播覆盖使这些网络面临严重的窃听威胁。考虑到SAGIN中多层接入网络间因频谱共享而产生的同频干扰，这一干扰可被利用来辅助异构传输间的物理层安全。然而，由于异构资源及不同的窃听模型，执行面向保密的接入策略具有挑战性。本文针对多模式用户（能够接入卫星、无人机或基站）在窃听者存在下的场景，探索安全接入选择问题。具体而言，我们提出一种基于Q网络近似的深度学习方法，用于选择最优接入策略以最大化总保密速率。同时，采用无监督学习方法进行功率优化以提升保密性能。值得注意的是，两个神经网络分别通过无监督学习和Q网络近似进行训练，两者均为无标签方法，无需已知最优解作为标签。数值结果验证了所提功率优化方法和接入策略的有效性，从而实现了增强的安全传输性能。