Due to the continuous increase in data traffic, communication networks require increased densification and multi-layering. Different hotspot areas may access different networks. We aim to avoid interference from cell-free millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) sys-tems in hotspot areas. To this end, we consider a specific scenario involving the cell-free mmWave massive MIMO system and the mmWave Vehicle-to-Everything (V2X) network. To minimize interference on the mmWave V2X network while ensuring the sum spectral efficiency (SE) performance of the cell-free mmWave massive MIMO system, a novel joint problem of beam selection and user equipment (UE) association is formulated. We propose a deep reinforcement learning (DRL)-based distributed double deep Q-network (D-DDQN) algorithm to address this problem. Simulation results indicate that the algorithm we propose out-performs traditional algorithms.


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    Title :

    Interference Avoidance in Hotspot Areas for Cell-Free Millimeter-Wave Massive MIMO Systems


    Contributors:
    Zhu, Shihao (author) / Liu, Junhui (author) / Zhao, Ming (author) / Zhou, Wuyang (author)


    Publication date :

    2024-06-24


    Size :

    1683234 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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