Title: Machine learning based cell association for mMTC 5G communication networks

Authors: Siddhant Ray; Budhaditya Bhattacharyya

Addresses: School of Electronics Engineering, Vellore Institute of Technology, Vellore, India ' School of Electronics Engineering, Vellore Institute of Technology, Vellore, India

Abstract: With the advent of 5G communication networks, the number of devices on the core 5G network significantly increases.A5Gnetwork is a cloud native, massively connected internet of things (IoT) platform with a huge number of devices hosted on the network now known as massive machine type communication (mMTC). As ultra-low latency is pivotal in developing 5G communication, a proper cell association scheme is now required to meet the load and traffic needs of the new network, opposed to older cell association schemes which were based only on the reference signal received power (RSRP). This paper proposes an unsupervised machine learning algorithm, namely hidden Markov model (HMM) learning on the network's telemetry data, which is used to learn network parameters and select the best eNodeB for cell association. The proposed model uses an HMM learning followed by decoding for selecting the optimal cell for association.

Keywords: MTC; machine type communication; load balancing; ultra-low latency; 5G networks; HMM learning; Baum-Welch algorithm; Viterbi decoding; channel availability; cell association; optimal selection.

DOI: 10.1504/IJMNDI.2020.112622

International Journal of Mobile Network Design and Innovation, 2020 Vol.10 No.1, pp.10 - 16

Received: 30 May 2020
Accepted: 09 Jun 2020

Published online: 25 Jan 2021 *

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