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

2025-08-26

AI Summary

This work addresses the joint deployment and trajectory optimization of multi-UAV swarms for 6G aerial access networks, aiming to minimize both system energy consumption (including UAVs and ground users) and user communication latency. Method: A hierarchical swarm architecture is proposed, where lead UAVs serve as airborne base stations and trailing UAVs act as relays; dynamic spatial partitioning is achieved via K-means clustering and Voronoi tessellation, while Fermat point modeling captures geometric relay connectivity constraints. To solve the resulting multi-objective mixed-integer nonlinear programming problem, an improved Non-dominated Sorting Whale Optimization Algorithm (NSWOA) is developed, enhancing both convergence and solution diversity. Results: Experiments demonstrate that the proposed approach reduces computational complexity by approximately 50% compared to baseline methods, while significantly improving energy efficiency and end-to-end response performance in large-scale, remote-area coverage scenarios.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

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.

Problem

• Optimizing UAV swarm deployment and trajectory for 6G networks
• Minimizing energy consumption and delay for ground users
• Solving NP-hard multi-objective optimization problem efficiently

Innovation

• Hierarchical UAV swarms structure
• K-means Voronoi area division
• Improved whale optimization algorithm