{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:12:35Z","timestamp":1760231555122,"version":"build-2065373602"},"reference-count":24,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2022,9,24]],"date-time":"2022-09-24T00:00:00Z","timestamp":1663977600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Beijing Natural Science Foundation Project","award":["JQ21036","61821001"],"award-info":[{"award-number":["JQ21036","61821001"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["JQ21036","61821001"],"award-info":[{"award-number":["JQ21036","61821001"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Beijing Key Laboratory of Work Safety Intelligent Monitoring","award":["JQ21036","61821001"],"award-info":[{"award-number":["JQ21036","61821001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The digital optical fiber repeater (DOFR) is an important infrastructure in the LTE networks, which solve the problem of poor regional signal quality. Various types of conventional measurement data from the LTE network cannot indicate whether a working DOFR is present in the cell. Currently, the detection of DOFRs relies solely on maintenance engineers for field detection. Manual detection methods are not timely or efficient, because of the large number and wide geographical distribution of DOFRs. Implementing automatic detection of DOFR can reduce the maintenance cost for mobile network operators. We treat the DOFR detection problem as a classification problem and employ a deep convolutional neural network (DCNN) to tackle it. The measurement report (MR) we used in this paper are tabular data, which is not an ideal input for DCNN. We propose a novel MR representation method that takes the overall MR data of a cell as a sample rather than a single record in the table, and represents the MR data as a pseudo-image matrix (PIM). The PIM will be used as the input for training DCNN, and the trained DCNN will be used to perform DOFR detection tasks. We conducted a series of experiments on real MR data, and the classification accuracy can achieve 93%. The proposed AI-based method can effectively detect the DOFR in a cell.<\/jats:p>","DOI":"10.3390\/s22197257","type":"journal-article","created":{"date-parts":[[2022,9,26]],"date-time":"2022-09-26T03:34:17Z","timestamp":1664163257000},"page":"7257","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Application of Deep Convolutional Neural Network for Automatic Detection of Digital Optical Fiber Repeater"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1083-9614","authenticated-orcid":false,"given":"Xingkang","family":"Tian","sequence":"first","affiliation":[{"name":"School of Electronic Engineering, Be\u0133ing University of Posts and Telecommunications, Beijing 100088, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1286-7141","authenticated-orcid":false,"given":"Fan","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Be\u0133ing University of Posts and Telecommunications, Beijing 100088, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6387-503X","authenticated-orcid":false,"given":"Cong","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Be\u0133ing University of Posts and Telecommunications, Beijing 100088, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5288-8708","authenticated-orcid":false,"given":"Wenhao","family":"Fan","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Be\u0133ing University of Posts and Telecommunications, Beijing 100088, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8076-1904","authenticated-orcid":false,"given":"Yuanan","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Be\u0133ing University of Posts and Telecommunications, Beijing 100088, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Yazti, D.Z., and Krishnaswamy, S. (2014, January 14\u201318). Mobile big data analytics: Research, practice, and opportunities. Proceedings of the 2014 IEEE 15th International Conference on Mobile Data Management, Brisbane, Australia.","DOI":"10.1109\/MDM.2014.73"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1489","DOI":"10.1109\/JIOT.2017.2714189","article-title":"Mobile big data: The fuel for data-driven wireless","volume":"4","author":"Cheng","year":"2017","journal-title":"IEEE Internet Things J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1109\/MNET.2017.1500295NM","article-title":"Exploiting mobile big data: Sources, features, and applications","volume":"31","author":"Cheng","year":"2017","journal-title":"IEEE Netw."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1109\/MCOM.2018.1700294","article-title":"Recent advances and challenges in mobile big data","volume":"56","author":"Ahmed","year":"2018","journal-title":"IEEE Commun. Mag."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2224","DOI":"10.1109\/COMST.2019.2904897","article-title":"Deep learning in mobile and wireless networking: A survey","volume":"21","author":"Zhang","year":"2019","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_6","first-page":"1","article-title":"Quest for margins: Operational cost strategies for mobile operators in Europe","volume":"42","author":"Buvat","year":"2009","journal-title":"Capgemini"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1985","DOI":"10.1109\/ACCESS.2016.2540520","article-title":"Big data analytics in mobile cellular networks","volume":"4","author":"He","year":"2016","journal-title":"IEEE Access"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1109\/JCN.2015.000102","article-title":"Big data meets telcos: A proactive caching perspective","volume":"17","author":"Bennis","year":"2015","journal-title":"J. Commun. Netw."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1109\/MCOM.001.1900664","article-title":"When machine learning meets wireless cellular networks: Deployment, challenges, and applications","volume":"58","author":"Challita","year":"2020","journal-title":"IEEE Commun. Mag."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"32328","DOI":"10.1109\/ACCESS.2018.2837692","article-title":"Big data analytics, machine learning, and artificial intelligence in next-generation wireless networks","volume":"6","author":"Kibria","year":"2018","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1682","DOI":"10.1109\/COMST.2018.2825786","article-title":"Self-Healing in emerging cellular networks: Review, challenges, and research directions","volume":"20","author":"Asghar","year":"2018","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zhang, X., and Sun, Y. (2020, January 29\u201331). Unsupervised Fault Diagnosis Platform Implementation for Self-Healing in Cellular Networks. Proceedings of the 2020 Information Communication Technologies Conference (ICTC), Nanjing, China.","DOI":"10.1109\/ICTC49638.2020.9123269"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Bothe, S., Masood, U., Farooq, H., and Imran, A. (2020, January 26\u201329). Neuromorphic AI Empowered Root Cause Analysis of Faults in Emerging Networks. Proceedings of the 2020 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom), Odessa, Ukraine.","DOI":"10.1109\/BlackSeaCom48709.2020.9235002"},{"key":"ref_14","unstructured":"Barco, R., Nielsen, L., Guerrero, R., Hylander, G., and Patel, S. (2002, January 9\u201311). Automated troubleshooting of a mobile communication network using bayesian networks. Proceedings of the 4th International Workshop on Mobile and Wireless Communications Network, Stockholm, Sweden."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"7549","DOI":"10.1016\/j.eswa.2015.05.031","article-title":"Data mining for fuzzy diagnosis systems in LTE networks","volume":"42","author":"Khatib","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2451","DOI":"10.1109\/TVT.2007.912610","article-title":"Automated diagnosis for UMTS networks using Bayesian network approach","volume":"57","author":"Khanafer","year":"2008","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1109\/MCI.2017.2773824","article-title":"Machine learning for performance prediction in mobile cellular networks","volume":"13","author":"Riihijarvi","year":"2018","journal-title":"IEEE Comput. Intell. Mag."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Falkenberg, R., Sliwa, B., Piatkowski, N., and Wietfeld, C. (2018, January 27\u201330). Machine learning based uplink transmission power prediction for LTE and upcoming 5G networks using passive downlink indicators. Proceedings of the 2018 IEEE 88th Vehicular Technology Conference (VTC-Fall), Chicago, IL, USA.","DOI":"10.1109\/VTCFall.2018.8690629"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Gao, J., Cheng, X., Xu, L., and Ye, H. (2016, January 26\u201328). An interference management algorithm using big data analytics in LTE cellular networks. Proceedings of the 2016 16th International Symposium on Communications and Information Technologies (ISCIT), Qingdao, China.","DOI":"10.1109\/ISCIT.2016.7751630"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wang, X., and Zhou, Q. (2020, January 8\u201311). LTE Network Quality Analysis Method Based on MR Data and XGBoost Algorithm. Proceedings of the 2020 5th IEEE International Conference on Big Data Analytics (ICBDA), Xiamen, China.","DOI":"10.1109\/ICBDA49040.2020.9101302"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1093\/nsr\/nwx106","article-title":"A brief introduction to weakly supervised learning","volume":"5","author":"Zhou","year":"2018","journal-title":"Natl. Sci. Rev."},{"key":"ref_22","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Vydana, H.K., and Vuppala, A.K. (September, January 28). Residual neural networks for speech recognition. Proceedings of the 2017 25th European Signal Processing Conference (EUSIPCO), Kos, Greece.","DOI":"10.23919\/EUSIPCO.2017.8081266"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"6111","DOI":"10.1007\/s00521-019-04097-w","article-title":"A transfer convolutional neural network for fault diagnosis based on ResNet-50","volume":"32","author":"Wen","year":"2019","journal-title":"Neural Comput. Appl."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/19\/7257\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:38:59Z","timestamp":1760143139000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/19\/7257"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,24]]},"references-count":24,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["s22197257"],"URL":"https:\/\/doi.org\/10.3390\/s22197257","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2022,9,24]]}}}