{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T08:17:11Z","timestamp":1780474631147,"version":"3.54.1"},"reference-count":38,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,11,19]],"date-time":"2021-11-19T00:00:00Z","timestamp":1637280000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/sym13112207","type":"journal-article","created":{"date-parts":[[2021,11,19]],"date-time":"2021-11-19T08:29:17Z","timestamp":1637310557000},"page":"2207","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Performance Estimation in V2X Networks Using Deep Learning-Based M-Estimator Loss Functions in the Presence of Outliers"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8410-738X","authenticated-orcid":false,"given":"Ali R.","family":"Abdellah","sequence":"first","affiliation":[{"name":"Electronics and Communications Engineering, Electrical Engineering Department, Faculty of Engineering, Al-Azhar University, Qena 83513, Egypt"},{"name":"Department of Communication Networks and Data Transmission, The Bonch-Bruevich Saint-Petersburg State University of Telecommunications, 193232 St. Petersburg, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdullah","family":"Alshahrani","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Artificial Intelligence, College of Computer Science and Engineering, University of Jeddah, Jeddah 21493, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0213-8145","authenticated-orcid":false,"given":"Ammar","family":"Muthanna","sequence":"additional","affiliation":[{"name":"Department of Communication Networks and Data Transmission, The Bonch-Bruevich Saint-Petersburg State University of Telecommunications, 193232 St. Petersburg, Russia"},{"name":"Applied Probability and Informatics, Peoples\u2019 Friendship University of Russia (RUDN University), 117198 Moscow, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrey","family":"Koucheryavy","sequence":"additional","affiliation":[{"name":"Department of Communication Networks and Data Transmission, The Bonch-Bruevich Saint-Petersburg State University of Telecommunications, 193232 St. Petersburg, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"137184","DOI":"10.1109\/ACCESS.2019.2942390","article-title":"Machine Learning for 5G\/B5G Mobile and Wireless Communications: Potential, Limitations, and Future Directions","volume":"7","author":"Haeyoung","year":"2019","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.iatssr.2018.05.005","article-title":"Smarter and more connected: Future intelligent transportation system","volume":"42","author":"Sumalee","year":"2018","journal-title":"IATSS Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1007\/s42979-021-00592-x","article-title":"Machine Learning: Algorithms, Real-World Applications and Research Directions","volume":"2","author":"Sarker","year":"2021","journal-title":"SN Comput. 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