UAV-Assisted Multi-User Downlink Covert Communication Based on Lightweight Agents

The covert communication scenario with multiple users involved needs to take into account multiple conflicting optimization goals, such as maximizing the number of served users, minimizing service energy consumption, and balancing resource allocation. And the UAV-assisted multi-user communication is a typical knapsack traveling salesman problem, which is difficult to solve because of its large solution space and NP-hard nature. Therefore, to address this, a novel UAV-assisted multi-user downlink covert communication scenario is considered. To find an optimal balance of the maximum transmission rate and the minimum energy consumption, a low-complexity solution is proposed. First, a closed-form solution for the hovering position and transmission power of the UAV is deduced. Then, the user selection and trajectory planning results from the heuristic algorithm are used to initialize the deep reinforcement learning (DRL) agent, providing a superior starting point for the subsequent policy search. This strategy combines the fast convergence of heuristics with the dynamic adaptability of DRL, resulting in a final design that is more efficient and lightweight than conventional, pure DRL-based methods. The simulation results demonstrate that the proposed scheme improves energy efficiency by at least 40% compared with the benchmark scheme.