@inproceedings{fa73b6b217b84c3a8aa97613558d90c7,
title = "Cell Coverage Degradation Detection Using Deep Learning Techniques",
abstract = "We apply deep learning techniques to the sleeping cell problem, in order to achieve greater detection sensitivity than previously reported. We use a deep recurrent Neural Network (rNN) to process simulated RSRP reports in order to detect degradations of cell radio performance as well as complete outages. Using such a configuration we are able to achieve improved sensivity compared with a traditional Support Vector Machine (SVM) approach, while eliminating the need for a separate dimensionality reduction stage at the front end. We study multiple rNN configurations with up to three hidden layers and conclude that in this scenario we can achieve the target sensitivity with a single hidden layer, leading to highly efficient run time performance.",
keywords = "Cellular networks, cell degradation, cell outage, deep learning, fault detection, neural networks, self healing",
author = "David Mulvey and Foh, \{Chuan Heng\} and \{Ali Imran\}, Muhammad and Rahim Tafazolli",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 9th International Conference on Information and Communication Technology Convergence, ICTC 2018 ; Conference date: 17-10-2018 Through 19-10-2018",
year = "2018",
month = nov,
day = "16",
doi = "10.1109/ICTC.2018.8539449",
language = "English",
series = "9th International Conference on Information and Communication Technology Convergence: ICT Convergence Powered by Smart Intelligence, ICTC 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "446--447",
booktitle = "9th International Conference on Information and Communication Technology Convergence",
address = "United States",
}