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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    Title :

    Deep Reinforcement Learning Framework for Joint Resource Allocation in Heterogeneous Networks


    Contributors:
    Zhang, Yong (author) / Kang, Canping (author) / Teng, YingLei (author) / Li, Sisi (author) / Zheng, WeiJun (author) / Fang, JingHui (author)


    Publication date :

    2019-09-01


    Size :

    619735 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English