In this study, a deep reinforcement learning (DRL) method was employed to solve the joint optimization problem for user association, resource allocation, and power allocation in heterogeneous networks (HetNets), which is an NP-hard problem. Existing studies have taken various optimization objectives into account. The heterogeneous network-deep-Q- network frame-work (HetDQN) is proposed to solve this type of optimization problem in HetNets. Based on maximum spectral efficiency, we designed a 6- layer deep neural network. The state space, objective function, and reward function are presented. In comparison with the existing solution, HetDQN can achieve a higher spectral efficiency. The simulation results revealed that HetDQN has better performance in term of convergence.


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    Titel :

    Deep Reinforcement Learning Framework for Joint Resource Allocation in Heterogeneous Networks


    Beteiligte:
    Zhang, Yong (Autor:in) / Kang, Canping (Autor:in) / Teng, YingLei (Autor:in) / Li, Sisi (Autor:in) / Zheng, WeiJun (Autor:in) / Fang, JingHui (Autor:in)


    Erscheinungsdatum :

    01.09.2019


    Format / Umfang :

    619735 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch