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.


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    Titel :

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


    Beteiligte:
    Cheng, Qianqian (Autor:in) / Su, Yu (Autor:in) / Qiu, Yuhe (Autor:in) / Zhou, Jian (Autor:in) / Wang, Shuijie (Autor:in)

    Kongress:

    7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering ; 2022 ; Xishuangbanna,China


    Erschienen in:

    Proc. SPIE ; 12294 ; 122943G


    Erscheinungsdatum :

    19.10.2022





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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