With the advantages of Millimeter wave in wireless communication network, the coverage radius and inter-site distance can be further reduced, the ultra dense network (UDN) becomes the mainstream of future networks. The main challenge faced by UDN is the serious inter-site interference, which needs to be carefully addressed by joint user association and resource allocation methods. In this paper, we propose a multi-agent Q-learning based method to jointly optimize the user association and resource allocation in UDN. The deep Q-network is applied to guarantee the convergence of the proposed method. Simulation results reveal the effectiveness of the proposed method and different performances under different simulation parameters are evaluated.


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    Titel :

    Learning-Based Joint User-AP Association and Resource Allocation in Ultra Dense Network


    Beteiligte:
    Cheng, Zhipeng (Autor:in) / LiWang, Minghui (Autor:in) / Chen, Ning (Autor:in) / Lin, Hongyue (Autor:in) / Gao, Zhibin (Autor:in) / Huang, Lianfen (Autor:in)


    Erscheinungsdatum :

    01.05.2020


    Format / Umfang :

    178552 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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