Label-free Deep Learning Driven Secure Access Selection in
  Space-Air-Ground Integrated Networks
Zhaowei Wang Zhisheng Yin Xiucheng Wang Nan Cheng Yuan Zhang Tom H. Luan
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.