Due to the characteristics of line-of-sight (LoS) communication in unmanned aerial vehicle (UAV) networks, these systems are highly susceptible to eavesdropping and surveillance. To effectively address the security concerns in UAV communication, covert communication methods have been adopted. This paper explores the joint optimization problem of trajectory and transmission power in a multi-hop UAV relay covert communication system. Considering the communication covertness, power constraints, and trajectory limitations, an algorithm based on multi-agent proximal policy optimization (MAPPO), named covert-MAPPO (C-MAPPO), is proposed. The proposed method leverages the strengths of both optimization algorithms and reinforcement learning to analyze and make joint decisions on the transmission power and flight trajectory strategies for UAVs to achieve cooperation. Simulation results demonstrate that the proposed method can maximize the system throughput while satisfying covertness constraints, and it outperforms benchmark algorithms in terms of system throughput and reward convergence speed.

High-Frequency (HF) communication is widely used for long-distance transmission in remote and disaster areas. However, the dynamic nature of the ionosphere and multipath propagation in the HF channel pose significant challenges to designing efficient and robust communication systems. In this paper, we propose a Fixed Station (FS) and frequency matching method, as well as a power allocation method, to improve the sum-rate of air-to-ground HF communication networks. We derive optimal power allocation among users that share the same frequency, based on which a modified water-filling algorithm is used to solve the power allocation problem in multi-user scenarios, while a low-complexity algorithm is proposed to solve the integer optimization problem of frequency-FS matching. Simulation results demonstrate that the proposed algorithm outperforms the naive algorithm, indicating its effectiveness.