Energy efficient deployment of aerial base stations for mobile users in multi-hop UAV networks

Abstract

Unmanned aerial vehicles (UAVs) are popularly considered as aerial base stations in a Low-Altitude Platform (LAP) to provide wireless connections to ground users in disaster and digital divide situation. Most of previous studies have investigated energy efficient UAV deployment only for the stationary ground users. Contrarily, we propose an on-line deployment algorithm for ground mobile users (GMUs) who are partially observable in the multi-UAV system. In the framework of Partially Observable Markov Decision Process (POMDP) with large state and action space, we use the Monte Carlo Tree Search (MCTS) algorithm enhanced by the Double Progressive Widening (DPW) and Deep Neural Network (DNN)-based guidance. For online learning of the DNN, simulated samples are used with Proximal Policy Optimization (PPO) that prevents excessive training for a particular belief. From experiment in urban environment, the proposed scheme shows better learning performance than the MCTS with rollout policy and faster convergence than training with environment samples. In addition, it optimizes dual objectives proportionally in a trade-off between energy saving and throughput including prediction error on GMU location.