Multi-UAV Trajectory Design for Fair and Secure Communication

TL;DR

The paper addresses secure and fair communication in a multi-UAV network with a moving airborne eavesdropper by jointly designing UAV trajectories and transmit powers. It introduces a two-step SCTPD approach combining K-means clustering to assign users to UAVs and a MADDDPG-based solution with a POMDP reconstruction and a dimension-expansion mechanism to handle high-dimensional states. The core contributions are a comprehensive reward design that balances energy, endpoint reach, fairness, and secrecy throughput, and a training framework that achieves convergence and improved fairness while suppressing eavesdropping via a friendly jammer. Simulation results confirm convergence and show that SCTPD enhances fairness and a secure throughput relative to baselines, highlighting its practical potential for energy-constrained, secure UAV networks.

Abstract

Unmanned aerial vehicles (UAVs) play an essential role in future wireless communication networks due to their high mobility, low cost, and on-demand deployment. In air-to-ground links, UAVs are widely used to enhance the performance of wireless communication systems due to the presence of high-probability line-of-sight (LoS) links. However, the high probability of LoS links also increases the risk of being eavesdropped, posing a significant challenge to the security of wireless communications. In this work, the secure communication problem in a multi-UAV-assisted communication system is investigated in a moving airborne eavesdropping scenario. To improve the secrecy performance of the considered communication system, aerial eavesdropping capability is suppressed by sending jamming signals from a friendly UAV. An optimization problem under flight conditions, fairness, and limited energy consumption constraints of multiple UAVs is formulated to maximize the fair sum secrecy throughput. Given the complexity and non-convex nature of the problem, we propose a two-step-based optimization approach. The first step employs the $K$-means algorithm to cluster users and associate them with multiple communication UAVs. Then, a multi-agent deep deterministic policy gradient-based algorithm is introduced to solve this optimization problem. The effectiveness of this proposed algorithm is not only theoretically but also rigorously verified by simulation results.

Figures (10)

• Figure 1: System Model.
• Figure 2: MADDPG architecture.
• Figure 3: The training process.
• Figure 4: Average speed per episode with training.
• Figure 5: Training loss per episode.