-Ongoing-6GD2DMECML/AIMobilityRRMUAVCooperation with the International Research Centre in Area of Digital Communication Systems

Machine learning for mobile network optimization and for image processing

The project supports collaboration with EURECOM and its members and partners in area of machine learning for optimization of mobile networks. From the scientific point of view, the objective is to develop algorithms and methods for network control and allocation of distributed computing resources in mobile networks with cloud-based architecture encompassing Multi-access Edge Computing and Cloud-RAN and exploit machine learning for image processing and compression.

Device-to-device, Edge computing, Mobility management, Radio resource management, UAVs/Flying base stations, Machine learning

Major results and publications

  • , , and
    Machine Learning-Based Beamforming for Unmanned Aerial Vehicles Equipped with Reconfigurable Intelligent Surfaces
    IEEE Wireless Communications, .

  • , and
    Q-Learning-based Setting of Cell Individual Offset for Handover of Flying Base Stations
    IEEE Vehicular Technology Conference (IEEE VTC2022-Spring), .

  • , and
    Mitigation of Doppler Effect in High-speed Trains through Relaying
    IEEE Vehicular Technology Conference (IEEE VTC2022-Spring), .

  • , , and
    System and Methods for Device-to-Device Communication
    US patent with application No. 16/919,990, .

  • , and
    Reuse of Multiple Channels by Multiple D2D Pairs in Dedicated Mode: Game Theoretic Approach
    IEEE Transactions on Wireless Communications, volume 20, no. 7, .

    [BibTex] [pdf] [doi]

    03/2020 - 12/2024
    Project no.:
    LTT 20004
    Ministry of Education, Youth and Sports
    ~730k EUR
    Zdenek Becvar
    Team (6Gm):
    Pavel Mach, Mehyar Najla, Jan Plachy, Aida Madelkhanova, Saleh Nikooroo
    D2D, UAV, MEC, Mobility, RRM, ML

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