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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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 |
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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. |
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57215054855 Samidi F.S. Mohamed Radzi N.A. Mohd Azmi K.H. Mohd Aripin N. Azhar N.A. |
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Samidi F.S. Mohamed Radzi N.A. Mohd Azmi K.H. Mohd Aripin N. Azhar N.A. |
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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 |
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MDPI |
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2023 |
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1806426026413129728 |