Energy-Efficient Cluster Formation and Central User Selection in UAV-Assisted Cellular Networks

Incorporating Device-to-Device (D2D) communication and Unmanned Aerial Vehicles (UAVs) into next-generation cellular networks is crucial for addressing the growing demand for high-data-rate applications. However, increasing communication distances in both cellular and D2D environments raises transmission power requirements, leading to higher energy consumption and reduced network efficiency. Additionally, in the conventional technique, UAVs communicate directly with each UE individually, further exacerbating the energy demands of UAVs. To overcome these challenges, this paper presents an energy-efficient clustering technique designed to minimize power consumption for both UEs and UAVs. Hypergraph-based clustering enables adaptive grouping by leveraging received signal strength, while the Whale Optimization Algorithm (WOA) selects central users based on UE distance, residual energy, and connectivity metrics. This structured clustering strategy enhances system throughput and optimizes energy efficiency. Extensive simulations validate the proposed model, demonstrating significant improvements in energy efficiency, reduced computational complexity, and superior network performance compared to existing methods.