Dynamic Trajectory Optimization and Power Control for Hierarchical UAV Swarms in 6G Aerial Access Network

TL;DR

This paper introduces a hierarchical UAV swarm framework for 6G networks, optimizing their deployment and trajectories to enhance connectivity, reduce energy consumption, and lower latency through advanced algorithms.

Contribution

It proposes a novel hierarchical UAV swarm structure and an improved optimization algorithm for joint deployment and trajectory planning in large-scale 6G aerial access networks.

Findings

50% reduction in algorithm complexity

Enhanced energy efficiency of UAV swarms

Improved latency performance in simulations

Abstract

Unmanned aerial vehicles (UAVs) can serve as aerial base stations (BSs) to extend the ubiquitous connectivity for ground users (GUs) in the sixth-generation (6G) era. However, it is challenging to cooperatively deploy multiple UAV swarms in large-scale remote areas. Hence, in this paper, we propose a hierarchical UAV swarms structure for 6G aerial access networks, where the head UAVs serve as aerial BSs, and tail UAVs (T-UAVs) are responsible for relay. In detail, we jointly optimize the dynamic deployment and trajectory of UAV swarms, which is formulated as a multi-objective optimization problem (MOP) to concurrently minimize the energy consumption of UAV swarms and GUs, as well as the delay of GUs. However, the proposed MOP is a mixed integer nonlinear programming and NP-hard to solve. Therefore, we develop a K-means and Voronoi diagram based area division method, and construct Fermat points to establish connections between GUs and T-UAVs. Then, an improved non-dominated sorting whale optimization algorithm is proposed to seek Pareto optimal solutions for the transformed MOP. Finally, extensive simulations are conducted to verify the performance of proposed algorithms by comparing with baseline mechanisms, resulting in a 50% complexity reduction.