UAV-Assisted Physical Layer Security for Space–Air–Ground Integrated Networks (SAGIN) With Multiple Eavesdroppers

This paper investigates a drone (aka UAV)-assisted physical layer security framework for space–air–ground integrated networks (SAGINs) in the presence of multiple eavesdroppers. A single full-duplex UAV is deployed to support satellite-to-ground communications by simultaneously relaying desired signals to legitimate users and transmitting artificial noise to degrade the reception quality of eavesdroppers. To enhance secure connectivity, we formulate a max–min secrecy rate optimization problem that jointly considers sub-channel allocation and power distribution. The sub-channel allocation is optimized using a constrained genetic algorithm, which efficiently handles the combinatorial nature of the problem. Additionally, power allocation is optimized through a nested-loop approach, in which the outer loop employs Bayesian optimization to address complex objective functions, while the inner loop makes the allocation tractable using variable substitutions and approximation methods to overcome non-convexity. The simulation results demonstrate that the proposed method outperforms the benchmark schemes in terms of secrecy performance, particularly under stringent resource and security constraints in SAGINs.