Joint Trajectory Design and User Scheduling of Aerial Cognitive Radio Networks

TL;DR

This paper proposes an iterative optimization algorithm for joint trajectory design and user scheduling in aerial cognitive radio networks to maximize secrecy rates against eavesdroppers.

Contribution

It introduces a novel joint optimization framework for UAV trajectory, user scheduling, and power control in aerial CRNs considering eavesdropper uncertainty.

Findings

The proposed algorithm effectively enhances secrecy performance.

Optimized UAV trajectories improve communication security.

Numerical results confirm the scheme's superiority over baseline methods.

Abstract

Unmanned aerial vehicles (UAVs) have been widely employed to enhance the end-to-end performance of wireless communications since the links between UAVs and terrestrial nodes are line-of-sight (LoS) with high probability. However, the broadcast characteristics of signal propagation in LoS links make it vulnerable to being wiretapped by malicious eavesdroppers, which poses a considerable challenge to the security of wireless communications. This paper investigates the security of aerial cognitive radio networks (CRNs). An airborne base station transmits confidential messages to secondary users utilizing the same spectrum as the primary network. An aerial base station transmits jamming signals to suppress the eavesdropper to enhance secrecy performance. The uncertainty of eavesdropping node locations is considered, and the average secrecy rate of the cognitive user is maximized by optimizing multiple users' scheduling, the UAVs' trajectory, and transmit power. To solve the non-convex optimization problem with mixed multiple integers variable problem, we propose an iterative algorithm based on block coordinate descent and successive convex approximation. Numerical results verify the effectiveness of our proposed algorithm and demonstrate that our scheme is beneficial to improving the secrecy performance of aerial CRNs.