LSTM Recurrent Neural Network (RNN) for Anomaly Detection in Cellular Mobile Networks

• Published in: Machine Learning for Networking Vol. 11407; pp. 222 - 237
• Main Authors: Al Mamun, S. M. Abdullah, Beyaz, Mehmet
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2019; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Anomaly detection; Cell diagnostics; Cell performance degradation; Deep Neural Network; Recurrent Neural Network (RNN)
• ISBN: 3030199444; 9783030199449
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-19945-6_15

Anomaly detection can show significant behavior changes in the cellular mobile network. It can explain much important missing information and which can be monitored using advanced AI (Artificial Intelligent) applications/tools. In this paper, we have proposed LSTM (Long Short-Term Memory) based RNN (Recurrent Neural Network) which can model a time series profile for LTE network based on cell KPI values. We have shown in this paper that the dynamic behavior of a single cell can be simplified using a combination of a set for neighbor cells. We can predict the profile and anomalous behavior using this method. According to the best of our knowledge this approach is applied here for the first time for cell level performance profile generation and anomaly detection. In a related work, they have proposed ensemble method to compare different KPIs and cell performance using machine learning algorithm. We have applied DNN (Deep Neural Network) to generate a profile on KPI features from historical data. It gave us deeper insight into how the cell is performing over time and can connect with the root causes or hidden fault of a major failure in the cellular network.