Performance Estimation in V2X Networks Using Deep Learning-Based M-Estimator Loss Functions in the Presence of Outliers

Recently, 5G networks have emerged as a new technology that can control the advancement of telecommunication networks and transportation systems. Furthermore, 5G networks provide better network performance while reducing network traffic and complexity compared to current networks. Machine-learning t...

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Autores principales:	Ali R. Abdellah, Abdullah Alshahrani, Ammar Muthanna, Andrey Koucheryavy
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	5G networks V2X deep learning M-estimators outliers Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/9bc9ce0d04d240379e3a076cabfa5ceb
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spelling	oai:doaj.org-article:9bc9ce0d04d240379e3a076cabfa5ceb2021-11-25T19:07:35ZPerformance Estimation in V2X Networks Using Deep Learning-Based M-Estimator Loss Functions in the Presence of Outliers10.3390/sym131122072073-8994https://doaj.org/article/9bc9ce0d04d240379e3a076cabfa5ceb2021-11-01T00:00:00Zhttps://www.mdpi.com/2073-8994/13/11/2207https://doaj.org/toc/2073-8994Recently, 5G networks have emerged as a new technology that can control the advancement of telecommunication networks and transportation systems. Furthermore, 5G networks provide better network performance while reducing network traffic and complexity compared to current networks. Machine-learning techniques (ML) will help symmetric IoT applications become a significant new data source in the future. Symmetry is a widely studied pattern in various research areas, especially in wireless network traffic. The study of symmetric and asymmetric faults and outliers (anomalies) in network traffic is an important topic. Nowadays, deep learning (DL) is an advanced approach in challenging wireless networks such as network management and optimization, anomaly detection, predictive analysis, lifetime value prediction, etc. However, its performance depends on the efficiency of training samples. DL is designed to work with large datasets and uses complex algorithms to train the model. The occurrence of outliers in the raw data reduces the reliability of the training models. In this paper, the performance of Vehicle-to-Everything (V2X) traffic was estimated using the DL algorithm. A set of robust statistical estimators, called M-estimators, have been proposed as robust loss functions as an alternative to the traditional MSE loss function, to improve the training process and robustize DL in the presence of outliers. We demonstrate their robustness in the presence of outliers on V2X traffic datasets.Ali R. AbdellahAbdullah AlshahraniAmmar MuthannaAndrey KoucheryavyMDPI AGarticle5G networksV2Xdeep learningM-estimatorsoutliersMathematicsQA1-939ENSymmetry, Vol 13, Iss 2207, p 2207 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	5G networks V2X deep learning M-estimators outliers Mathematics QA1-939
spellingShingle	5G networks V2X deep learning M-estimators outliers Mathematics QA1-939 Ali R. Abdellah Abdullah Alshahrani Ammar Muthanna Andrey Koucheryavy Performance Estimation in V2X Networks Using Deep Learning-Based M-Estimator Loss Functions in the Presence of Outliers
description	Recently, 5G networks have emerged as a new technology that can control the advancement of telecommunication networks and transportation systems. Furthermore, 5G networks provide better network performance while reducing network traffic and complexity compared to current networks. Machine-learning techniques (ML) will help symmetric IoT applications become a significant new data source in the future. Symmetry is a widely studied pattern in various research areas, especially in wireless network traffic. The study of symmetric and asymmetric faults and outliers (anomalies) in network traffic is an important topic. Nowadays, deep learning (DL) is an advanced approach in challenging wireless networks such as network management and optimization, anomaly detection, predictive analysis, lifetime value prediction, etc. However, its performance depends on the efficiency of training samples. DL is designed to work with large datasets and uses complex algorithms to train the model. The occurrence of outliers in the raw data reduces the reliability of the training models. In this paper, the performance of Vehicle-to-Everything (V2X) traffic was estimated using the DL algorithm. A set of robust statistical estimators, called M-estimators, have been proposed as robust loss functions as an alternative to the traditional MSE loss function, to improve the training process and robustize DL in the presence of outliers. We demonstrate their robustness in the presence of outliers on V2X traffic datasets.
format	article
author	Ali R. Abdellah Abdullah Alshahrani Ammar Muthanna Andrey Koucheryavy
author_facet	Ali R. Abdellah Abdullah Alshahrani Ammar Muthanna Andrey Koucheryavy
author_sort	Ali R. Abdellah
title	Performance Estimation in V2X Networks Using Deep Learning-Based M-Estimator Loss Functions in the Presence of Outliers
title_short	Performance Estimation in V2X Networks Using Deep Learning-Based M-Estimator Loss Functions in the Presence of Outliers
title_full	Performance Estimation in V2X Networks Using Deep Learning-Based M-Estimator Loss Functions in the Presence of Outliers
title_fullStr	Performance Estimation in V2X Networks Using Deep Learning-Based M-Estimator Loss Functions in the Presence of Outliers
title_full_unstemmed	Performance Estimation in V2X Networks Using Deep Learning-Based M-Estimator Loss Functions in the Presence of Outliers
title_sort	performance estimation in v2x networks using deep learning-based m-estimator loss functions in the presence of outliers
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/9bc9ce0d04d240379e3a076cabfa5ceb
work_keys_str_mv	AT alirabdellah performanceestimationinv2xnetworksusingdeeplearningbasedmestimatorlossfunctionsinthepresenceofoutliers AT abdullahalshahrani performanceestimationinv2xnetworksusingdeeplearningbasedmestimatorlossfunctionsinthepresenceofoutliers AT ammarmuthanna performanceestimationinv2xnetworksusingdeeplearningbasedmestimatorlossfunctionsinthepresenceofoutliers AT andreykoucheryavy performanceestimationinv2xnetworksusingdeeplearningbasedmestimatorlossfunctionsinthepresenceofoutliers
_version_	1718410293267136512

Performance Estimation in V2X Networks Using Deep Learning-Based M-Estimator Loss Functions in the Presence of Outliers

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