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.

在空天地一体化网络中，利用多层接入网络间的频谱共享所带来的同频干扰，应用于异构传输中以增强物理层安全性，并通过基于深度学习的Q网络逼近方法选择最优的访问策略来提高秘密速率。利用无监督学习和Q网络逼近训练两个神经网络，无标签方法证明了我们提出的功率优化方法和访问策略的高效性，从而提升了安全传输性能。

基于无标签深度学习的空中地一体化网络中的安全访问选择