Predictive Covert Communication Against Multi-UAV Surveillance Using Graph Koopman Autoencoder
This addresses the challenge of covert communication for mobile networks under UAV surveillance, representing a domain-specific incremental advance.
The paper tackles the problem of enabling low probability of detection communication in terrestrial ad-hoc networks under surveillance by multiple UAVs with unknown nonlinear dynamics, achieving at least 63%-75% lower detection probability compared to state-of-the-art baselines.
Low Probability of Detection (LPD) communication aims to obscure the presence of radio frequency (RF) signals to evade surveillance. In the context of mobile surveillance utilizing unmanned aerial vehicles (UAVs), achieving LPD communication presents significant challenges due to the UAVs' rapid and continuous movements, which are characterized by unknown nonlinear dynamics. Therefore, accurately predicting future locations of UAVs is essential for enabling real-time LPD communication. In this paper, we introduce a novel framework termed predictive covert communication, aimed at minimizing detectability in terrestrial ad-hoc networks under multi-UAV surveillance. Our data-driven method synergistically integrates graph neural networks (GNN) with Koopman theory to model the complex interactions within a multi-UAV network and facilitating long-term predictions by linearizing the dynamics, even with limited historical data. Extensive simulation results substantiate that the predicted trajectories using our method result in at least 63%-75% lower probability of detection when compared to well-known state-of-the-art baseline approaches, showing promise in enabling low-latency covert operations in practical scenarios.