Multi-UAV-Enabled Energy-Efficient Data Delivery for Low-Altitude Economy: Joint Coded Caching, User Grouping, and UAV Deployment
Qing Wei, Ruoguang Li, Wenle Bai, Zhu Han
Abstract
Non-terrestrial network (NTN) enabled low-altitude economy (LAE) has emerged as a promising economic paradigm that leverages advanced air mobility (AAM) vehicles to revolutionize connectivity in the six-generation (6G) era. By deploying unmanned aerial vehicles (UAVs) as flying edge nodes, wireless caching can significantly alleviate network congestion and reduce latency, enabling the efficient handling of massive terrestrial user requests in LAE applications. However, the limited energy and storage capacity of UAVs pose significant challenges to provide persistent and diverse content delivery services. To address such limitations, this paper proposes a multi-UAV-enabled coded caching scheme for energy-efficient data delivery, in which both the communication coverage and cache hit are satisfied. Taking into account the dynamics of user mobility and user preferences, we design an energy minimization problem with the joint optimization of coding vectors, caching variables, user grouping, and updated UAV locations. We initially deploy UAVs using a constrained K-means clustering algorithm based on user locations, and evaluate the clustering effectiveness with the silhouette coefficient. Then, we solve this problem by proposing a multi-UAV enabled coded caching optimization (MUCCO) scheme, embedded with a novel projected distance-based user grouping method, semidefinite programming (SDP), and matching theory. The simulation results demonstrate that the proposed MUCCO scheme can achieve low energy consumption compared to other schemes, with scalable user density and file library size.