Multi-connectivity emerges as a useful feature to handle the traffic in heterogeneous cellular scenarios and fulfill the demanding requirements in terms of data rate and reliability. It allows a device to be simultaneously connected to multiple cells belonging to different radio access network nodes from a single or multiple radio access technologies. This paper addresses the problem of optimally splitting the traffic among cells when multi-connectivity is used. For this purpose, it proposes the use of deep learning to determine the optimum amount of traffic of a device that needs to be sent through one or another cell depending on the current traffic and radio conditions. Obtained results reveal a promising capability of the proposed Deep Q Network solution to select quasi optimum traffic splits in the considered scenario.


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    Titel :

    Deep Learning-based Multi-Connectivity Optimization in Cellular Networks


    Beteiligte:
    Hernandez-Carlon, J. J. (Autor:in) / Perez-Romero, J. (Autor:in) / Sallent, O. (Autor:in) / Vila, I. (Autor:in) / Casadevall, F. (Autor:in)


    Erscheinungsdatum :

    01.06.2022


    Format / Umfang :

    337586 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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