Intelligent UAV Caching and Energy Management in 6G Networks
Abdelkader Ilyes Ameur, Omar Sami Oubbati, Abderrezak Rachedi, Ali Arishi, Mohammed Atiquzzaman
Abstract
In the landscape of Sixth-Generation (6 G) networks, achieving efficient content delivery and caching in Unmanned Aerial Vehicle (UAV)-assisted terrestrial networks remains a critical challenge due to dynamic user demand, energy constraints, and redundant storage limitations. This paper proposes a novel framework integrating UAVs, Unmanned Ground Vehicles (UGVs), Reconfigurable Intelligent Surfaces (RISs), and Wireless Power Transfer (WPT) to eliminate the need for UAVs to return to the Base Station (BS) for energy replenishment and content reloading. A Multi-Agent Deep Reinforcement Learning (MADRL) approach is employed to optimize UAV and UGV movements, ensuring adaptive trajectory adjustments for uninterrupted connectivity and service continuity. Specifically, UAVs receive energy and data directly from the BS when a Line-of-Sight (LoS) link is available or, in the case of a Non-Line-of-Sight (NLoS) scenario, via UGV-mounted RISs that reflect signals to/from the BS, maximizing WPT efficiency and enhancing data relaying. A Softmax-based probabilistic caching strategy optimizes content placement based on request popularity while preventing redundant storage across UAVs, enabling them to autonomously update their caches and reposition according to expected demand patterns. When a requested content file is unavailable in a UAV's cache, the request is forwarded to the BS via RIS-assisted reflection or direct communication, ensuring low-latency content retrieval without requiring UAV trajectory deviations. The proposed framework significantly enhances cache efficiency and UAV mobility, enabling continuous operation. Comprehensive simulations confirm the efficacy of this method, showcasing significant gains in network sustainability, energy efficiency, and content availability over traditional UAV-assisted caching techniques.