Trajectory design for energy efficiency maximization in UAV based on reinforcement learning

Qianqian Cheng; Yu Su; Yuhe Qiu; Jian Zhou; Shuijie Wang

doi:10.1117/12.2639899

19 October 2022 Trajectory design for energy efficiency maximization in UAV based on reinforcement learning

Qianqian Cheng, Yu Su, Yuhe Qiu, Jian Zhou, Shuijie Wang

Proceedings Volume 12294, 7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering; 122943G (2022) https://doi.org/10.1117/12.2639899
Event: 7th International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 2022), 2022, Xishuangbanna, China

Abstract

UAV has become a promising development direction in 5G era because of its flexible deployment and economic efficiency. UAV with communication function can serve many scenes, such as traffic congestion, limited base station conditions, emergency rescue and so on. However, UAV has limited airborne energy, throughput and energy efficiency are the main bottlenecks of UAV as an air base station. Based on the consideration of various factors such as channel, user, UAV speed and transmission power, this paper constructs a reinforcement learning model for UAV energy efficiency, and puts forward the description of environment matrix to quantify the environmental parameters and participate in the action value evaluation. Firstly, based on the existing conditions, a constrained model is established to maximize the information throughput per unit energy consumption by combining historical empirical data with the exploration of a certain degree of freedom. In addition, we establish strong constraints on UAV energy to avoid unnecessary consumption as much as possible. The experimental results show that the algorithm proposed in this paper shows good performance in the simulation stage and excellent stability in the open environment.

Citation Download Citation

Qianqian Cheng, Yu Su, Yuhe Qiu, Jian Zhou, and Shuijie Wang "Trajectory design for energy efficiency maximization in UAV based on reinforcement learning", Proc. SPIE 12294, 7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering, 122943G (19 October 2022); https://doi.org/10.1117/12.2639899

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