Label-free Deep Learning Driven Secure Access Selection in Space-Air-Ground Integrated Networks

TL;DR

This paper introduces a label-free deep learning framework for secure access selection in SAGIN, leveraging spectrum sharing and neural networks to enhance physical layer security without prior labels.

Contribution

It proposes a novel Q-network based deep learning method for access strategy selection and an unsupervised learning approach for power optimization in SAGIN security.

Findings

Enhanced secrecy rate through deep learning-based access selection.

Effective power optimization improves secure transmission performance.

Label-free neural networks eliminate the need for optimal solution labels.

Abstract

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.