Radio Map-Based Trajectory Design for UAV-Assisted Wireless Energy Transmission Communication Network by Deep Reinforcement Learning
In this paper, we consider a wireless energy-carrying communication network of a UAV. In this communication network, the internet of things (IoT) devices maintain their work via the power supply of batteries. The energy of batteries is slowly consumed over time. The UAV adopts the full-duplex working mode and the flight hover protocol, that is, they can power the target device in the hover position and collect base station data at the same time. Unlike traditional methods, which seek to achieve wireless energy transmission, this paper adopts deep reinforcement learning. On the one hand, the deep reinforcement learning algorithm seeks to solve the dynamic programming problem without a model. The traditional method often requires a prior channel model, to form a formula about variables for convex optimization, while reinforcement learning only requires the interaction between agents and the environment. Then, the strategy is optimized according to the reward function feedback. On the other hand, traditional optimization methods generally solve static programming problems. Since IoT devices constantly collect information from the surrounding physical environment, and their requirements for power supply change dynamically, traditional methods are relatively complex and require huge computational overhead, while deep reinforcement learning performs well in complex problems. The purpose of our work is that with the assistance of the radio map, a UAV can find the best hover position, maximize the energy supplied by the UAV, maximize the data throughput collected, and minimize the energy consumption. The simulation results show that the proposed algorithm can well find the best hovering position of the UAVs and significantly improve the performance of the UAV-assisted wireless energy transmission communication network.