Using LSTM Network for Predicting RadioMobile Networks’ Behaviors

Abstract

Breakthroughs in artificial intelligence, machine learning and deep learning with great recent achievements have been strongly impacting the mobile communication industry. The traditional method of mobile network management and operation has been shifting toward automation and proactive monitoring. Forecasting radio mobile networks’ behaviors in coexisting multi-layer technology mobile networks for improving resource allocation efficiency and optimizing network’s key performance indicators becomes a critical task. The use of a Long-Short Term Memory (LSTM) network for predicting radio networks’ behaviors based on real statistical data from operational support systems is suggested in this research. The promising results show the potential of our approach. The prediction results can help mobile network operators leverage networks’ operating performance