Covert Communication Toward an Aerial Warden in NOMA-Based UAV-MEC Systems

Non-orthogonal multiple access (NOMA) enables multiple terminal devices to simultaneously share wireless resources, providing efficient computing offloading services for wireless devices in networks that integrate unmanned aerial vehicles (UAVs) with mobile edge computing (MEC). However, the broadcast characteristics of UAV line-of-sight (LoS) communication introduce serious security issues for NOMA-based UAV-MEC systems, especially when facing an aerial warden. To address this issue, we propose a covert communication scheme for NOMA-based UAV-MEC systems against an aerial warden, where the aerial warden monitors the task offloading behavior of terminal devices. In the proposed scheme, the average computing capacity is maximized by jointly optimizing the UAV trajectory and system resources while ensuring the covert performance requirements. Firstly, considering the terminal devices have a fixed number of computing tasks, a block coordinate descent (BCD)-based algorithm is proposed, which decomposes the non-convex original problem into several subproblems and solves them iteratively. Secondly, considering the case of dynamic tasks arrival at terminal devices, we propose a double-deep Q-learning (DDQN)-based algorithm, where the optimal strategy for trajectory planning and resource allocation is obtained. Simulation results demonstrate that the proposed scheme using two algorithms outperform their respective baselines.