sibsutisВестник СибГУТИThe Herald of the Siberian State University of Telecommunications and Information Science1998-6920СибГУТИ10.55648/1998-6920-2022-16-2-55-62sibsutis-133Research ArticleСтатьиСбор данных о работе оборудования в сети мобильного оператора связиData collection concerning equipment operation in the network of a mobile operatorЖанаеваСауле БактыкереевнаZhanayevaSaule BaktykereevnaЖанаева Сауле Бактыкереевна, аспирант, кафедра прикладной математики и кибернетики 

630102, Новосибирск, ул. Кирова, 86

Saule B. Zhanayeva, Postgraduate student

Novosibirsk

szhanayeva@gmail.comСибГУТИРоссияSiberian State University of Telecommunications and Information ScienceRussian Federation20222207202202Вестник СибГУТИ5562Copyright © Жанаева С.Б., 20222022Жанаева С.Б.Zhanayeva S.B.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://vestnik.sibsutis.ru/jour/article/view/133

Операторы мобильной сети передачи данных с ростом количества оборудования сталки-ваются с увеличивающейся сложностью эксплуатации и растущими затратами на обслу-живание. При увеличении количества базовых станций растет и количество сбоев. Со-временные технологические решения, основанные на алгоритмах нейронных сетей, спо-собны заблаговременно с определенной вероятностью предсказать возникновение сбоев вработе оборудования. Для обучения модели нейронной сети требуются данные о работе исбоях на оборудовании мобильной сети передачи данных. В данной статье рассказывает-ся о выполненном сборе данных в сети мобильного оператора 4G+, об особенностях иограничениях, которые в дальнейшем могут повлиять на обучение модели

Mobile data network operators face increasing operational complexity and rising maintenance costs as thenumber of equipment increases. As the number of base stations increases, so does the number of failures.Modern technological solutions based on neural network algorithms are able to predict in advance with acertain probability the occurrence of equipment failures. Data of operation and failures on the mobile net-work equipment is required to train a neural network model. The article considers the performed data collec-tion on the 4G mobile operator network, features and limitations that may further affect the model training

сбор данныхмобильные сетисбои в работе оборудованияцентр управления сетьюdata collectionmobile networksequipment failuresnetwork operational centerReferencesTihvinskiy V. O., Terentev S. V., Koval V. A. Seti mobilnoy cvyazi 5G: tehnologii, arhitektura i uslugiTihvinskiy V. O., Terentev S. V., Koval V. A. Seti mobilnoy cvyazi 5G: tehnologii, arhitektura i uslugi [Mobile Networks 5G: technologies, architecture and services]. Moscow, Media Publishers, 2021, 375 p.[Mobile Networks 5G: technologies, architecture and services]. Moscow, Media Publishers, 2021, 375Brink H., Richards J. W., Fetherolf M. Real world Machine Learning. US, Manning Publications Co., 2017, 266 p.p.Yin-Hsin Liu, Yao-Chung Tu, Chang-Yu Hsu, Hsin-Chieh Chao. Predicting malfunction of mobile network base station using machine learning approach. Proceedings of the 20th Asia-Pacific Network Operations and Management Symposium (APNOMS), 18-20 September, Matsue, Japan, 2019, pp. 1-4. DOI: 10.23919/APNOMS.2019.8892894.Brink H., Richards J. W., Fetherolf M. Real world Machine Learning. US, Manning Publications Co.,Kumar Y., Farooq H., Imran A. Fault Prediction and Reliability Analysis in a Real Cellular Network. Proceedings of 13th International Wireless Communications and Mobile Computing Conference (IWCMC), 26-30 June, pp. 1090-1095, Valencia, Spain, 2017. DOI: 10.1109/IWCMC.2017.7986437., 266 p.Corazza A., Isgro F., Longobardo L., Prevete R. A machine learning approach for predictive maintenance for mobile phones service providers. Advances on P2P, Parallel, Grid, Cloud and Internet Computing. Lecture Notes on Data Engineering and Communications Technologies, Springer International Publishing, 2017. DOI: 10.1007/978-3-319-49109-7_69.Yin-Hsin Liu, Yao-Chung Tu, Chang-Yu Hsu, Hsin-Chieh Chao. Predicting malfunction of mobileOtani T., Toube H., Kimura T., Furutani M. Application of AI to mobile network operation. ITU Journal: ICT Discoveries, 2018, Special Issue, no. 1, pp. 43-48.network base station using machine learning approach. Proceedings of the 20th Asia-Pacific NetworkHu C., Youn B. D., Kim T. Semi-supervised learning with co-training for data-driven prognostics. Proceedings of IEEE Int. Conf. on Prognostics and Health Management: Enhancing Safety, Efficiency, Availability, and Effectiveness of Systems Through PHM Technology and Application, 18-21 June, Denver, CO, USA, 2012, pp. 1297-1306. DOI: 10.1109/ICPHM.2012.6299526.Operations and Management Symposium (APNOMS), 18-20 September, Matsue, Japan, 2019, pp. 1-4.Yousefia N., Tsianikasa S., Zhoua J., Coit D. W. Inspection plan prediction for multi-repairable component systems using neural network. Proceedings of the 2020 IISE Annual Conference, Institute of Industrial and Systems Engineers, IISE, United States, 1-3 Nov, 2020.DOI: 10.23919/APNOMS.2019.8892894.Schmidt B., Wang L. Cloud-enhanced predictive maintenance. Int J Adv Manuf Technol, 2018, no. 99, pp. 5–13. DOI 10.1007/s00170-016-8983-8.Kumar Y., Farooq H., Imran A. Fault Prediction and Reliability Analysis in a Real Cellular Network.Wei Wu, Feng Zhang, Min Liu, Weiming Shen. A Multi-agent Based Failure Prediction Method Using Neural Network Algorithm. Proceedings of 2014 IEEE International Conference on Systems, Man, and Cybernetics, San Diego, CA., 2014, pp. 2268-2272, DOI: 10.1109/SMC.2014.6974263.Proceedings of 13th International Wireless Communications and Mobile Computing ConferenceKarballaeezadeh N., Zaremotekhases F., Shamshirband S., Mosavi A., Nabipour N., Csiba P., Várkonyi-Kóczy A. Intelligent Road Inspection with Advanced Machine Learning; Hybrid Prediction Models for Smart Mobility and Transportation Maintenance Systems. Energies, 2020, no. 13, pp. 1718. DOI: 10.3390/en13071718.(IWCMC), 26-30 June, pp. 1090-1095, Valencia, Spain, 2017. DOI: 10.1109/IWCMC.2017.7986437.(IWCMC), 26-30 June, pp. 1090-1095, Valencia, Spain, 2017. DOI: 10.1109/IWCMC.2017.7986437.Corazza A., Isgro F., Longobardo L., Prevete R. A machine learning approach for predictive mainte-Corazza A., Isgro F., Longobardo L., Prevete R. A machine learning approach for predictive mainte-nance for mobile phones service providers. Advances on P2P, Parallel, Grid, Cloud and Internet Com-nance for mobile phones service providers. Advances on P2P, Parallel, Grid, Cloud and Internet Com-puting. Lecture Notes on Data Engineering and Communications Technologies, Springer Internationalputing. Lecture Notes on Data Engineering and Communications Technologies, Springer InternationalPublishing, 2017. DOI: 10.1007/978-3-319-49109-7_69.Publishing, 2017. DOI: 10.1007/978-3-319-49109-7_69.Otani T., Toube H., Kimura T., Furutani M. Application of AI to mobile network operation. ITU Jour-Otani T., Toube H., Kimura T., Furutani M. Application of AI to mobile network operation. ITU Jour-nal: ICT Discoveries, 2018, Special Issue, no. 1, pp. 43-48.nal: ICT Discoveries, 2018, Special Issue, no. 1, pp. 43-48.Hu C., Youn B. D., Kim T. Semi-supervised learning with co-training for data-driven prognostics.Hu C., Youn B. D., Kim T. Semi-supervised learning with co-training for data-driven prognostics.Proceedings of IEEE Int. Conf. on Prognostics and Health Management: Enhancing Safety, Efficiency,Proceedings of IEEE Int. Conf. on Prognostics and Health Management: Enhancing Safety, Efficiency,Availability, and Effectiveness of Systems Through PHM Technology and Application, 18-21 June, Den-Availability, and Effectiveness of Systems Through PHM Technology and Application, 18-21 June, Den-ver, CO, USA, 2012, pp. 1297-1306. DOI: 10.1109/ICPHM.2012.6299526.ver, CO, USA, 2012, pp. 1297-1306. DOI: 10.1109/ICPHM.2012.6299526.Yousefia N., Tsianikasa S., Zhoua J., Coit D. W. Inspection plan prediction for multi-repairable compo-Yousefia N., Tsianikasa S., Zhoua J., Coit D. W. Inspection plan prediction for multi-repairable compo-nent systems using neural network. Proceedings of the 2020 IISE Annual Conference, Institute of Indus-nent systems using neural network. Proceedings of the 2020 IISE Annual Conference, Institute of Indus-trial and Systems Engineers, IISE, United States, 1-3 Nov, 2020.trial and Systems Engineers, IISE, United States, 1-3 Nov, 2020.Schmidt B., Wang L. Cloud-enhanced predictive maintenance. Int J Adv Manuf Technol, 2018, no. 99,Schmidt B., Wang L. Cloud-enhanced predictive maintenance. Int J Adv Manuf Technol, 2018, no. 99,pp. 5–13. DOI 10.1007/s00170-016-8983-8.pp. 5–13. DOI 10.1007/s00170-016-8983-8.Wei Wu, Feng Zhang, Min Liu, Weiming Shen. A Multi-agent Based Failure Prediction Method UsingWei Wu, Feng Zhang, Min Liu, Weiming Shen. A Multi-agent Based Failure Prediction Method UsingNeural Network Algorithm. Proceedings of 2014 IEEE International Conference on Systems, Man, andNeural Network Algorithm. Proceedings of 2014 IEEE International Conference on Systems, Man, andCybernetics, San Diego, CA., 2014, pp. 2268-2272, DOI: 10.1109/SMC.2014.6974263.Cybernetics, San Diego, CA., 2014, pp. 2268-2272, DOI: 10.1109/SMC.2014.6974263.Karballaeezadeh N., Zaremotekhases F., Shamshirband S., Mosavi A., Nabipour N., Csiba P.,Karballaeezadeh N., Zaremotekhases F., Shamshirband S., Mosavi A., Nabipour N., Csiba P.,Várkonyi-Kóczy A. Intelligent Road Inspection with Advanced Machine Learning; Hybrid PredictionVárkonyi-Kóczy A. Intelligent Road Inspection with Advanced Machine Learning; Hybrid PredictionModels for Smart Mobility and Transportation Maintenance Systems. Energies, 2020, no. 13, pp. 1718.Models for Smart Mobility and Transportation Maintenance Systems. Energies, 2020, no. 13, pp. 1718.DOI: 10.3390/en13071718.DOI: 10.3390/en13071718.

The authors declare that there are no conflicts of interest present.