A Deep Learning Approach to User Allocation in a 5th Generation Network

In the current 5G cellular telecommunication networks, resource allocation is a main optimization target to meet the demands of the network and its users.In this work, we focus on optimizing resource allocation in 5G Multiple Input Multiple Output (MIMO) networks with the assistance of Deep Learning Artificial Neural Networks.To meet the complex and dynamic demands of the network and users the models are trained in a realistic scenario using a large variety of user circumstances.A large contingent of Machine Learning model architectures is tried and cataloged comparing their performance and accuracy.

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Publication Details

Published
2024-12-13
DOI
https://doi.org/10.1145/3716554.3716626
Citations
1
Primary Topic
IoT and Edge/Fog Computing
Type
article
Field-Weighted Citation Impact
0.35

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article

A Deep Learning Approach to User Allocation in a 5th Generation Network

Kyriakos Sgarbas, Iliana Loi, Απόστολος Γκάμας, Ioannis Konstantoulas
1 citations
IoT and Edge/Fog Computing
0.35
article

A Deep Learning Approach to User Allocation in a 5th Generation Network

Kyriakos Sgarbas, Iliana Loi, Απόστολος Γκάμας, Ioannis Konstantoulas
article en
1 citations

Abstract

In the current 5G cellular telecommunication networks, resource allocation is a main optimization target to meet the demands of the network and its users.In this work, we focus on optimizing resource allocation in 5G Multiple Input Multiple Output (MIMO) networks with the assistance of Deep Learning Artificial Neural Networks.To meet the complex and dynamic demands of the network and users the models are trained in a realistic scenario using a large variety of user circumstances.A large contingent of Machine Learning model architectures is tried and cataloged comparing their performance and accuracy.

University of Patras (GR), University of Ioannina (GR)
Hellenic Foundation for Research and Innovation
Openalex Percentile: Top 33%
IoT and Edge/Fog Computing
0.35
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