Multi-UAV Trajectory Design for Fair and Secure Communication

TL;DR

This paper proposes a multi-UAV system with a novel optimization approach to enhance secure and fair wireless communication by suppressing eavesdropping and optimizing UAV trajectories under multiple constraints.

Contribution

It introduces a two-step optimization method combining clustering and deep reinforcement learning to improve secrecy and fairness in multi-UAV communication networks.

Findings

The proposed algorithm significantly improves secrecy throughput.

Simulation results confirm the effectiveness of the approach.

The method balances fairness, security, and energy constraints.

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.