{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T15:55:43Z","timestamp":1783526143480,"version":"3.55.0"},"reference-count":18,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2023,8,9]],"date-time":"2023-08-09T00:00:00Z","timestamp":1691539200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of China","award":["92067101"],"award-info":[{"award-number":["92067101"]}]},{"name":"Natural Science Foundation of China","award":["BE2021013-3"],"award-info":[{"award-number":["BE2021013-3"]}]},{"name":"Key R&amp;D plan of Jiangsu Province","award":["92067101"],"award-info":[{"award-number":["92067101"]}]},{"name":"Key R&amp;D plan of Jiangsu Province","award":["BE2021013-3"],"award-info":[{"award-number":["BE2021013-3"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The efficient and accurate diagnosis of faults in cellular networks is crucial for ensuring smooth and uninterrupted communication services. In this paper, we propose an improved 4G\/5G network fault diagnosis with a few effective labeled samples. Our solution is a heterogeneous wireless network fault diagnosis algorithm based on Graph Convolutional Neural Network (GCN). First, the common failure types of 4G\/5G networks are analyzed, and then the graph structure is constructed with the data in the network parameter, given data sets as nodes and similarities as edges. GCN is used to extract features from the graph data, complete the classification task for nodes, and finally predict the fault types of cells. A large number of experiments are carried out based on the real data set, which is achieved by driving tests. The results show that, compared with a variety of traditional algorithms, the proposed method can effectively improve the performance of network fault diagnosis with a small number of labeled samples.<\/jats:p>","DOI":"10.3390\/s23167042","type":"journal-article","created":{"date-parts":[[2023,8,9]],"date-time":"2023-08-09T10:30:48Z","timestamp":1691577048000},"page":"7042","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Cellular Network Fault Diagnosis Method Based on a Graph Convolutional Neural Network"],"prefix":"10.3390","volume":"23","author":[{"given":"Ebenezer Ackah","family":"Amuah","sequence":"first","affiliation":[{"name":"Jiangsu Key Laboratory of Wireless Communications, Nanjing University of Posts and Telecommunications, Nanjing 210003, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingxiao","family":"Wu","sequence":"additional","affiliation":[{"name":"Jiangsu Key Laboratory of Wireless Communications, Nanjing University of Posts and Telecommunications, Nanjing 210003, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaorong","family":"Zhu","sequence":"additional","affiliation":[{"name":"Jiangsu Key Laboratory of Wireless Communications, Nanjing University of Posts and Telecommunications, Nanjing 210003, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Mfula, H., and Nurminen, J.K. (2017, January 17\u201321). Adaptive Root Cause Analysis for Self-Healing in 5G Networks. Proceedings of the 2017 International Conference on High Performance Computing & Simulation (HPCS), Genoa, Italy.","DOI":"10.1109\/HPCS.2017.31"},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"2811","DOI":"10.1109\/TVT.2016.2586143","article-title":"Root Cause Analysis Based on Temporal Analysis of Metrics Toward Self-Organizing 5G Networks","volume":"66","author":"Khatib","year":"2017","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1109\/TNSM.2012.031912.110155","article-title":"An Automatic Detection and Diagnosis Framework for Mobile Communication Systems","volume":"9","author":"Szilagyi","year":"2012","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2369","DOI":"10.1109\/TVT.2015.2431742","article-title":"Automatic Root Cause Analysis for LTE Networks Based on Unsupervised Techniques","volume":"65","author":"Serrano","year":"2016","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1587","DOI":"10.1109\/TMC.2016.2601919","article-title":"Data Analytics for Diagnosing the RF Condition in Self-Organizing Networks","volume":"16","author":"Barco","year":"2017","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"7152","DOI":"10.1109\/TCOMM.2019.2926715","article-title":"A Framework for Automated Cellular Network Tuning With Reinforcement Learning","volume":"67","author":"Mismar","year":"2019","journal-title":"IEEE Trans. Commun."},{"key":"ref_8","first-page":"10081","article-title":"Machine Learning Based Automatic Diagnosis in Mobile Communication Networks","volume":"68","author":"Chen","year":"2019","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhang, K. (2014, January 29\u201330). The railway turnout fault diagnosis algorithm based on BP neural network. Proceedings of the 2014 IEEE International Conference on Control Science and Systems Engineering, Yantai, China.","DOI":"10.1109\/CCSSE.2014.7224524"},{"key":"ref_10","unstructured":"Jin, C., Li, L.E., Bu, T., and Sanders, S.W. (2013). System and Method for Root Cause Analysis of Mobile Network Performance Problems. (Application 13\/436,212), U.S. Patent."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"230706","DOI":"10.1098\/rsos.230706","article-title":"Fault diagnosis for wind turbines with graph neural network model based on one-shot learning","volume":"10","author":"Yang","year":"2023","journal-title":"R. Soc. Open Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"5286","DOI":"10.1038\/s41598-023-32369-y","article-title":"A graph neural network-based bearing fault detection method","volume":"13","author":"Xiao","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_13","first-page":"9157","article-title":"Graph Convolutional Network-Based Method for Fault Diagnosis Using a Hybrid of Measurement and Prior Knowledge","volume":"512","author":"Chen","year":"2021","journal-title":"IEEE Trans. Cybern."},{"key":"ref_14","first-page":"755","article-title":"A Survey of Graph Convolutional Neural Networks","volume":"43","author":"Bingbing","year":"2020","journal-title":"J. Comput."},{"key":"ref_15","unstructured":"Michal, D., Bresson, X., and Vandergheynst, P. (2016, January 5\u201310). Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering. Proceedings of the Advances in Neural Information Processing Systems 29 (NIPS 2016), Lausanne, Switzerland."},{"key":"ref_16","unstructured":"Thomas, N.K., and Welling, M. (2016). Semi-supervised classification with graph convolutional networks. arXiv."},{"key":"ref_17","first-page":"241","article-title":"Fault diagnosis of power transformers using graph convolutional network","volume":"7","author":"Liao","year":"2021","journal-title":"CSEE J. Power Energy Syst."},{"key":"ref_18","unstructured":"Qimai, L., Han, Z., and Wu, X.M. (2018, January 2\u20133). Deeper insights into graph convolutional networks for semi-supervised learning. Proceedings of the AAAI Conference on Artificial Intelligence, New Orleans, LA, USA."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/16\/7042\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:28:43Z","timestamp":1760128123000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/16\/7042"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,9]]},"references-count":18,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["s23167042"],"URL":"https:\/\/doi.org\/10.3390\/s23167042","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,9]]}}}