6GML/AIMobilityRRMUAVCommunication in Self-optimizing Mobile Networks with Drones

Mobile networks with UAVs mounted on drones and acting as flying base stations

The project aims the investigation of fundamental principles and the development of algorithms enabling user’s communication in mobile networks with highly flexible and self-optimizing radio access network encompassing drones carrying small base stations (denoted as flying base stations). We will develop algorithms for the optimal association of mobile terminals to either conventional static or flying base stations, for positioning of the flying base station with respect to users’ position and requirements, and for allocation of radio resources.

UAVs, Machine learning, Self-optimization, Radio resource management, Energy efficiency

Major results and publications

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    Optimal Positioning of Flying Base Stations and Transmission Power Allocation in NOMA Networks
    IEEE Transactions on Wireless Communications, .

    [BibTex] [pdf] [doi]
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    Soft Frequency Reuse with Allocation of Resource Plans based on Machine Learning in the Networks with Flying Base Stations
    IEEE Access, volume 9, .

    [BibTex] [pdf] [doi]
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    Selection of Relays in Multi-Pair D2D Communication with Multicast
    European Wireless (EW 2021), .

    [BibTex]
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    Positioning and Association Rules for Transparent Flying Relay Stations
    IEEE Wireless Communications Letters, volume 10, no. 6, .

    [BibTex] [pdf]
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    Power Allocation, Channel Reuse, and Positioning of Flying Base Stations with Realistic Backhaul
    IEEE Internet of Things Journal, .

    [BibTex]
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    Optimization of Cell Individual Offset for Handover of Flying Base Station
    IEEE Vehicular Technology Conference (IEEE VTC-Spring 2021), .

    [BibTex] [pdf] [doi]
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    6G in the Sky: On-Demand Intelligence at the Edge of 3D Networks
    ETRI Journal, volume 42, no. 5, .

    [BibTex] [pdf] [doi]
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    Energy Efficient Positioning of Flying Base Stations via Coulomb's law
    IEEE Global Communications Conference (IEEE Globecom 2020) workshop on Space-Ground Integrated Networks, .

    [BibTex] [pdf]
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    Joint Association, Transmission Power Allocation and Positioning of Flying Base Stations Considering Limited Backhaul
    IEEE Vehicular Technology Conference (IEEE VTC-Fall 2020), .

    [BibTex]
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    Optimization of Transmission Power for NOMA in Networks with Flying Base Stations
    IEEE Vehicular Technology Conference (IEEE VTC-Fall 2020), .

    [BibTex] [pdf] [doi]
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    Integrating UAVs as Transparent Relays into Mobile Networks: A Deep Learning Approach
    IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (IEEE PIMRC 2020), .

    [BibTex] [pdf]
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    Flexible Soft Frequency Reuse for Interference Management in the Networks with Flying Base Stations
    IEEE Vehicular Technology Conference (IEEE VTC-Spring 2020), .

    [BibTex]
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    Reducing Energy Consumed by Repositioning of Flying Base Stations Serving Mobile Users
    IEEE Wireless Communications and Networking Conference (IEEE WCNC 2020), .

    [BibTex] [doi]
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    Optimizing Transmission and Propulsion Powers for Flying Base Stations
    IEEE Wireless Communications and Networking Conference (IEEE WCNC 2020), .

    [BibTex] [pdf] [doi]
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    Joint Positioning of UAV and Power Control for Flying Base Stations in Mobile Networks
    IEEE International Conference on Wireless and Mobile Computing, Networking and Communications (IEEE WiMOB 2019), .

    [BibTex] [pdf] [doi]
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    Positioning of Flying Base Stations to Optimize Throughput and Energy Consumption of Mobile Devices
    IEEE Vehicular Technology Conference (IEEE VTC-Spring 2019), .

    [BibTex]
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    Joint Positioning of Flying Base Stations and Association of Users: Evolutionary-Based Approach
    IEEE Access, volume 7, .

    [BibTex] [pdf] [doi]
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    Performance of Mobile Networks with UAVs: Can Flying Base Stations Substitute Ultra-Dense Small Cells?
    European Wireless (EW 2017), , BEST PAPER AWARD.

    [BibTex] [pdf]

    Date:
    01/2018 – 06/2021
    Project no.:
    P102/18/27023S
    Funding:
    Czech Science Foundation (GACR)
    Budget:
    ~175k EUR
    PI:
    Zdenek Becvar
    Team:
    Pavel Mach, Mehyar Najla, Saleh Nikooroo, Jan Plachy, Aida Madelkhanova, Miguel Fontanilla Perez de Tudela
    Partners:
    --