Clustering-Based Charger Deployment and Trajectory Optimization for UAV-Assisted Wireless Networks

Unmanned aerial vehicles (UAVs) are increasingly pivotal in large-scale wireless relay and inspection systems. However, energy constraints and infrastructure costs hinder large-scale deployment. This letter introduces a two-stage optimization combining clustering-based charger allocation and UAV trajectory optimization. The key strategy is the fusion of DBSCAN clustering with convex optimization techniques. By adaptively grouping communication nodes based on density distributions, charger deployment is minimized while maintaining coverage efficiency. We employ convex relaxation to jointly optimize UAV trajectories and charger locations, prioritizing proximity to critical nodes for enhancing communication quality. Simulations demonstrate a 36.5% reduction in charger count and a 61.4% improvement in minimum total throughput compared to baseline methods. The results validate a cost-effective and high-performance solution for UAV-assisted wireless networks.