5G Technology: ML Hyperparameter Tuning Analysis for Subcarrier Spacing Prediction Model

Resource optimisation is critical because 5G is intended to be a major enabler and a leading infrastructure provider in the information and communication technology sector by supporting a wide range of upcoming services with varying requirements. Therefore, system improvisation techniques, such as m...

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Main Authors: Samidi F.S., Mohamed Radzi N.A., Mohd Azmi K.H., Mohd Aripin N., Azhar N.A.
Other Authors: 57215054855
Format: Article
Published: MDPI 2023
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Institution: Universiti Tenaga Nasional
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spelling my.uniten.dspace-268022023-05-29T17:36:49Z 5G Technology: ML Hyperparameter Tuning Analysis for Subcarrier Spacing Prediction Model Samidi F.S. Mohamed Radzi N.A. Mohd Azmi K.H. Mohd Aripin N. Azhar N.A. 57215054855 57218936786 57982272200 57858108600 57219033091 Resource optimisation is critical because 5G is intended to be a major enabler and a leading infrastructure provider in the information and communication technology sector by supporting a wide range of upcoming services with varying requirements. Therefore, system improvisation techniques, such as machine learning (ML) and deep learning, must be applied to make the model customisable. Moreover, improvisation allows the prediction system to generate the most accurate outcomes and valuable insights from data whilst enabling effective decisions. In this study, we first provide a literature study on the applications of ML and a summary of the hyperparameters influencing the prediction capabilities of the ML models for the communication system. We demonstrate the behaviour of four ML models: k nearest neighbour, classification and regression trees, random forest and support vector machine. Then, we observe and elaborate on the suitable hyperparameter values for each model based on the accuracy in prediction performance. Based on our observation, the optimal hyperparameter setting for ML models is essential because it directly impacts the model�s performance. Therefore, understanding how the ML models are expected to respond to the system utilised is critical. � 2022 by the authors. Final 2023-05-29T09:36:49Z 2023-05-29T09:36:49Z 2022 Article 10.3390/app12168271 2-s2.0-85136560848 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136560848&doi=10.3390%2fapp12168271&partnerID=40&md5=0923fb4c8bd99dd7f49822879e8b6600 https://irepository.uniten.edu.my/handle/123456789/26802 12 16 8271 All Open Access, Gold MDPI Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Resource optimisation is critical because 5G is intended to be a major enabler and a leading infrastructure provider in the information and communication technology sector by supporting a wide range of upcoming services with varying requirements. Therefore, system improvisation techniques, such as machine learning (ML) and deep learning, must be applied to make the model customisable. Moreover, improvisation allows the prediction system to generate the most accurate outcomes and valuable insights from data whilst enabling effective decisions. In this study, we first provide a literature study on the applications of ML and a summary of the hyperparameters influencing the prediction capabilities of the ML models for the communication system. We demonstrate the behaviour of four ML models: k nearest neighbour, classification and regression trees, random forest and support vector machine. Then, we observe and elaborate on the suitable hyperparameter values for each model based on the accuracy in prediction performance. Based on our observation, the optimal hyperparameter setting for ML models is essential because it directly impacts the model�s performance. Therefore, understanding how the ML models are expected to respond to the system utilised is critical. � 2022 by the authors.
author2 57215054855
author_facet 57215054855
Samidi F.S.
Mohamed Radzi N.A.
Mohd Azmi K.H.
Mohd Aripin N.
Azhar N.A.
format Article
author Samidi F.S.
Mohamed Radzi N.A.
Mohd Azmi K.H.
Mohd Aripin N.
Azhar N.A.
spellingShingle Samidi F.S.
Mohamed Radzi N.A.
Mohd Azmi K.H.
Mohd Aripin N.
Azhar N.A.
5G Technology: ML Hyperparameter Tuning Analysis for Subcarrier Spacing Prediction Model
author_sort Samidi F.S.
title 5G Technology: ML Hyperparameter Tuning Analysis for Subcarrier Spacing Prediction Model
title_short 5G Technology: ML Hyperparameter Tuning Analysis for Subcarrier Spacing Prediction Model
title_full 5G Technology: ML Hyperparameter Tuning Analysis for Subcarrier Spacing Prediction Model
title_fullStr 5G Technology: ML Hyperparameter Tuning Analysis for Subcarrier Spacing Prediction Model
title_full_unstemmed 5G Technology: ML Hyperparameter Tuning Analysis for Subcarrier Spacing Prediction Model
title_sort 5g technology: ml hyperparameter tuning analysis for subcarrier spacing prediction model
publisher MDPI
publishDate 2023
_version_ 1806426026413129728