In this paper, we study joint beamforming and resource allocation in downlink multi-cell orthogonal frequency division multiple access (OFDMA) systems. We design a multi-agent deep Q-network (MADQN) algorithm to solve this problem. Furthermore, in order to improve the adaptability of neural networks for different wireless environment, we propose a transfer learning framework based on MADQN called TL-MADQN to dynamically output optimal beamforming and resource allocation policy. Finally, we adjust the allocation policy to maximize the sum-rate of all users by updating the weights of each neural network. Simulation results illustrate that the proposed TL-MADQN algorithm has higher sum-rate and faster convergence speed compared with the baseline algorithms.


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    Titel :

    Beamforming and Resource Allocation in Multi-cell OFDMA Systems based on Deep Transfer Reinforcement Learning


    Beteiligte:
    Sun, Gaoxiang (Autor:in) / Wang, Xiaoming (Autor:in) / Jiang, Rui (Autor:in) / Xu, Youyun (Autor:in)


    Erscheinungsdatum :

    01.06.2022


    Format / Umfang :

    613153 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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