In multicell cellular networks, coordinated multiuser scheduling (CMUS) based on block diagonalization precoding, which jointly selects the scheduled users among multiple base stations (BSs), is an efficient method to eliminate inter-user interference. However, most existing CMUS algorithms require global channel state information and many iterations, which are impractical in dynamic wireless networks due to its heavy information overhead and computational complexity. In this paper, we propose a distributed adaptive CMUS algorithm based on multi-agent reinforcement learning (MARL) to maximize the sum rate with less information overhead and computational complexity. In the proposed scheme, an adaptive multiple Deep Q-Network (DQN) architecture is deployed at each BS, aiming to reduce information overhead and computation complexity by identifying suitable CMUS policies with fewer DQNs. Each BS trains its own multi-DQNs and performs appropriate CMUS actions based on local information. Simulation results show that, by executing a subset of the DQNs, the proposed adaptive multi- DQN architecture reduces at least 50% of the information overhead and the computational complexity of the scheme without adaptive multi-DQN architecture. Additionally, compared to the centralized iterative approach, the proposed scheme costs 9.44% of the information overhead and 3.33%0 of the running time of the centralized iterative approach while achieving marginally superior performance.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Distributed Adaptive Multiuser Scheduling via Multi-Agent Reinforcement Learning in Multicell MIMO Cellular Networks


    Beteiligte:
    Bai, Shaozhuang (Autor:in) / Gao, Zhenzhen (Autor:in) / Liao, Xuewen (Autor:in) / Sun, Xiaodong (Autor:in)


    Erscheinungsdatum :

    24.06.2024


    Format / Umfang :

    694587 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Downlink Performance Analysis of Multicell Multiuser 3D MIMO System

    Yin, Wenran / Li, Lihua / Li, Xingwang et al. | IEEE | 2015


    Simultaneous Channel Estimation in Multicell Multiuser MIMO OFDM Systems

    Tseng, Yen-Chia / Chen, Po-Jen / Lu, Shih-Hao et al. | IEEE | 2024


    Multiuser Scheduling with Enhanced Greedy Techniques for Multicell and Cell-Free Massive MIMO Systems

    Mashdour, Saeed / De Lamare, Rodrigo C. / Lima, Joao P. S. H. | IEEE | 2022


    Exploiting Multiuser Diversity for MIMO Cellular Systems using Packet Scheduling and the VBLAST Receiver

    Floros, C. / McLaughlin, S. / Thompson, J. et al. | British Library Conference Proceedings | 2005


    Distributed charging station scheduling method and device based on multi-agent reinforcement learning

    LEI TIAN / WANG ZHENGZHUO / CHEN XIAOWEI et al. | Europäisches Patentamt | 2025

    Freier Zugriff