Energy Consumption Minimization for UAV-Assisted Network in Hotspot Area

Abstract

Unmanned aerial vehicles (UAVs) play a crucial role in enhancing network coverage and capacity, especially in areas with high user density or limited infrastructure. This paper proposes an effective UAV-assisted offloading framework to minimize the energy consumption of both users and UAVs in an air-to-ground (A2G) network. First, UAVs are deployed by jointly considering the user distribution and guaranteeing the quality of service (QoS) of users. Further, user association, power control, and bandwidth allocation are jointly optimized, aiming to minimize the power consumption of users. Considering user mobility, the positions of UAVs are continuously refined using the double deep Q-network (DDQN) algorithm to reduce the weighted energy consumption of users and UAVs. Simulation results show that the proposed algorithm has better performance in reducing the total energy consumption compared with benchmark schemes.