With the rapid development of wireless communications, the industries and academia acknowledge that the millimeter-wave (mm-wave) frequency band is rich in spectral resources. This paper considers the mm-wave train-ground communication system with multiple mobile relays (MRs) in a high-speed rail (HSR) scenario. We use the deep reinforcement learning (DRL) method to solve the beam management problem to maximize the system throughput. First, the inter-beam interference in the MRs scene is modeled due to the effect of the inter-beam angle on the system performance. Second, the maximization problem of system throughput is constructed for the beam management at both the transmitter and receiver. And we proposed the dynamic joint beam management scheme for the base station and the MRs located on the train’s roof based on DRL. Finally, the performance evaluation shows that the proposed scheme displays low computational complexity in the online phase. Additionally, the system throughput performance is close to the ideal optimal beam tracking algorithm, which combines high performance and low complexity, proving the feasibility of using DRL theory for beam management in train-ground communication.


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    Titel :

    Deep Reinforcement Learning-Based Train-Ground Beamforming Management for Multi-MRs Mm-wave Communication


    Beteiligte:
    Qiao, Yuanyuan (Autor:in) / Niu, Yong (Autor:in) / Zhang, Xiangfei (Autor:in) / Wang, Ning (Autor:in) / Zhong, Zhangdui (Autor:in) / Ai, Bo (Autor:in)


    Erscheinungsdatum :

    10.10.2023


    Format / Umfang :

    1298844 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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