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Mean opinion score estimation for mobile broadband networks using Bayesian Networks

Mobile broadband (MBB) networks are expanding rapidly to deliver higher data speeds. The fifth-generation cellular network promises enhanced-MBB with high-speed data rates, low power connectivity, and ultra-low latency video streaming. However, existing cellular networks are unable to perform well d...

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Main Authors: El-Saleh, Ayman A., Alhammadi, Abdulraqeb, Shayea, Ibraheem, Azizan, Azizul, Hassan, Wan Haslina
Format: Article
Language:English
Published: Tech Science Press 2022
Subjects:
T Technology (General)
Online Access:http://eprints.utm.my/103263/1/AzizulAzizan2022_MeanOpinionScoreEstimationforMobile.pdf
http://eprints.utm.my/103263/
http://dx.doi.org/10.32604/cmc.2022.024642
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spelling my.utm.1032632023-10-24T10:06:39Z http://eprints.utm.my/103263/ Mean opinion score estimation for mobile broadband networks using Bayesian Networks El-Saleh, Ayman A. Alhammadi, Abdulraqeb Shayea, Ibraheem Azizan, Azizul Hassan, Wan Haslina T Technology (General) Mobile broadband (MBB) networks are expanding rapidly to deliver higher data speeds. The fifth-generation cellular network promises enhanced-MBB with high-speed data rates, low power connectivity, and ultra-low latency video streaming. However, existing cellular networks are unable to perform well due to high latency and low bandwidth, which degrades the performance of various applications. As a result, monitoring and evaluation of the performance of these network-supported services is critical. Mobile network providers optimize and monitor their network performance to ensure the highest quality of service to their end-users. This paper proposes a Bayesian model to estimate the minimum opinion score (MOS) of video streaming services for any particular cellular network. The MOS is the most commonly used metric to assess the quality of experience. The proposed Bayesian model consists of several input data, namely, round-trip time, stalling load, and bite rates. It was examined and evaluated using several test data sizes with various performance metrics. Simulation results show the proposed Bayesian network achieved higher accuracy overall test data sizes than a neural network. The proposed Bayesian network obtained a remarkable overall accuracy of 90.36% and outperformed the neural network. Tech Science Press 2022 Article PeerReviewed application/pdf en http://eprints.utm.my/103263/1/AzizulAzizan2022_MeanOpinionScoreEstimationforMobile.pdf El-Saleh, Ayman A. and Alhammadi, Abdulraqeb and Shayea, Ibraheem and Azizan, Azizul and Hassan, Wan Haslina (2022) Mean opinion score estimation for mobile broadband networks using Bayesian Networks. Computers, Materials and Continua, 72 (3). pp. 4571-4587. ISSN 1546-2218 http://dx.doi.org/10.32604/cmc.2022.024642 DOI : 10.32604/cmc.2022.024642
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
El-Saleh, Ayman A.
Alhammadi, Abdulraqeb
Shayea, Ibraheem
Azizan, Azizul
Hassan, Wan Haslina
Mean opinion score estimation for mobile broadband networks using Bayesian Networks
description Mobile broadband (MBB) networks are expanding rapidly to deliver higher data speeds. The fifth-generation cellular network promises enhanced-MBB with high-speed data rates, low power connectivity, and ultra-low latency video streaming. However, existing cellular networks are unable to perform well due to high latency and low bandwidth, which degrades the performance of various applications. As a result, monitoring and evaluation of the performance of these network-supported services is critical. Mobile network providers optimize and monitor their network performance to ensure the highest quality of service to their end-users. This paper proposes a Bayesian model to estimate the minimum opinion score (MOS) of video streaming services for any particular cellular network. The MOS is the most commonly used metric to assess the quality of experience. The proposed Bayesian model consists of several input data, namely, round-trip time, stalling load, and bite rates. It was examined and evaluated using several test data sizes with various performance metrics. Simulation results show the proposed Bayesian network achieved higher accuracy overall test data sizes than a neural network. The proposed Bayesian network obtained a remarkable overall accuracy of 90.36% and outperformed the neural network.
format Article
author El-Saleh, Ayman A.
Alhammadi, Abdulraqeb
Shayea, Ibraheem
Azizan, Azizul
Hassan, Wan Haslina
author_facet El-Saleh, Ayman A.
Alhammadi, Abdulraqeb
Shayea, Ibraheem
Azizan, Azizul
Hassan, Wan Haslina
author_sort El-Saleh, Ayman A.
title Mean opinion score estimation for mobile broadband networks using Bayesian Networks
title_short Mean opinion score estimation for mobile broadband networks using Bayesian Networks
title_full Mean opinion score estimation for mobile broadband networks using Bayesian Networks
title_fullStr Mean opinion score estimation for mobile broadband networks using Bayesian Networks
title_full_unstemmed Mean opinion score estimation for mobile broadband networks using Bayesian Networks
title_sort mean opinion score estimation for mobile broadband networks using bayesian networks
publisher Tech Science Press
publishDate 2022
url http://eprints.utm.my/103263/1/AzizulAzizan2022_MeanOpinionScoreEstimationforMobile.pdf
http://eprints.utm.my/103263/
http://dx.doi.org/10.32604/cmc.2022.024642
_version_ 1781777670414008320
score 13.211869

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