Energy-efficient clustering and path planning for UAV-assisted D2D cellular networks

Abstract

The integration of Device-to-Device (D2D) communication and Unmanned Aerial Vehicles (UAVs) into advanced cellular networks is essential for effectively addressing the growing data demands. However, long-range communication in cellular and D2D networks typically requires higher transmission power, leading to increased energy consumption and reduced energy efficiency. To address this, we propose an innovative technique that combines hypergraph-based clustering with UAV path planning to minimize energy consumption in UAV-assisted D2D cellular networks. Our technique utilizes hypergraph theory to group UEs into clusters based on proximity and communication needs. The Particle Swarm Optimization (PSO) algorithm is employed to select a central User Equipment (UE) in each cluster, considering factors such as distance, residual energy, and degree centrality. Once the central UEs are chosen, the UAV’s path is optimized using the Ant Colony System (ACS) algorithm, addressing the Generalized Traveling Salesman Problem (GTSP) to minimize travel distance and energy consumption. We also analyze the computational complexity of the proposed technique, demonstrating its efficiency over existing techniques. Simulation results show significant improvements in system throughput, energy consumption, energy efficiency, and UAV path length.