Secrecy Energy Efficiency Maximization in Space-Air-Ground Networks with an Aerial Eavesdropper

Space-air-ground networks have received a lot of attention due to its extensive coverage characteristics. However, it also faces many unprecedented security challenges. This paper considers a space-air-ground network with an airship acting as an eavesdropper. To counter the eavesdropping threat, the unmanned aerial vehicle transmits artificial noise to jam the eavesdropper. We first consider the scenario without the no-fly zone, to maximize the system secrecy energy efficiency. Specifically, we formulate it into a fractional programming optimization problem, which jointly optimizes the UAV trajectory design and the jamming power, and then decompose it into two subproblems, i.e., trajectory-related and power-related subproblems. To derive the solution, we employ Dinkelbach algorithm, successive convex approximation algorithm, and the method of introducing slack variables to convert the non-convex subproblems into convex forms. The convergence of the developed algorithm and the complexity of our proposed scheme are analytically evaluated. Moreover, we consider the scenario with a no-fly zone and derive the solution. The simulation results demonstrate that our proposed scheme can effectively improve the system secrecy energy efficiency.