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User Allocation in 5G Networks Using Machine Learning Methods for Clustering

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Advanced Information Networking and Applications (AINA 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 225))

Abstract

The rapid increase in the volume of devices connected to mobile networks poses unprecedented demands on existing networking infrastructures. Machine Learning is a promising technique, already applied in various sectors of our everyday lives. It enables decision making not with the use of traditional programming but rather by using data to train models to cope with various problems without explicit programming on how to do so. The integration of Machine Learning techniques is deemed necessary in as many processes as possible to help the network face congestion and enable efficient real time decision making. In this paper we present two Machine Learning based mechanisms for improving real time user allocation on the network as well as predicting the best positioning scheme for Smallcell Base Stations to provide effective utilization of the network’s resources.

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Authors and Affiliations

  1. Computer Engineering and Informatics Department, Patras, Greece

    Christos Bouras & Rafail Kalogeropoulos

Authors
  1. Christos Bouras
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  2. Rafail Kalogeropoulos
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Corresponding author

Correspondence to Christos Bouras .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. Department of Computer Science, Ryerson University, Toronto, ON, Canada

    Isaac Woungang

  3. Faculty of Business Administration, Rissho University, Tokyo, Japan

    Tomoya Enokido

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Bouras, C., Kalogeropoulos, R. (2021). User Allocation in 5G Networks Using Machine Learning Methods for Clustering. In: Barolli, L., Woungang, I., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2021. Lecture Notes in Networks and Systems, vol 225. Springer, Cham. https://doi.org/10.1007/978-3-030-75100-5_2

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