Energy and Service-priority aware Trajectory Design for UAV-BSs using Double Q-Learning

Abstract

Next generation mobile networks have proposed the integration of Unmanned Aerial Vehicles (UAVs) as aerial base stations (UAV-BS) to serve ground nodes. Despite having advantages of using UAV-BSs, their dependence on the on-board, limited-capacity battery hinders their service continuity. Shorter trajectories can save flying energy, however UAV-BSs must also serve nodes based on their service priority since nodes' service requirements are not always the same. In this paper, we present an energy-efficient trajectory optimization for a UAV assisted IoT system in which the UAV-BS considers the IoT nodes' service priorities in making its movement decisions. We solve the trajectory optimization problem using Double Q-Learning algorithm. Simulation results reveal that the Q-Learning based optimized trajectory outperforms a benchmark algorithm, namely Greedilyserved algorithm, in terms of reducing the average energy consumption of the UAV-BS as well as the service delay for high priority nodes.

Green internet of things using UAVs in B5G networks: A review of applications and strategies

“…The authors in [41] studied a UAVs-assisted wireless system and focused on maximizing the vehicle's rate on the ground to join the UAV's trajectory while maintaining UAV's energy constraints. Furthermore, the optimal trajectory is introduced for balancing QoS and energy efficiency intelligently using Q-learning [53][54][55][56], RF band allocation [57], and wireless power transfer to recharge the UAVs [58].…”