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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    Title :

    Deep Learning-based Multi-Connectivity Optimization in Cellular Networks


    Contributors:


    Publication date :

    2022-06-01


    Size :

    337586 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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