Machine Learning-Based Traffic Prediction in 4G LTE Networks. Case Study of a Mobile Operator in Cameroon

Abstract

Mobile subscribers are increasingly demanding the availability of broadband services while the radio resources allowing them to be connected are limited. Understanding mobile Internet consumption trends and subscriber traffic demands is essential to enable the management of existing radio resources. However, it can be difficult to understand and describe the data usage patterns of mobile users because of the complexity of mobile networks. In this study, we study and characterize the data usage patterns and user behavior in mobile networks to perform traffic demand prediction. We exploit a dataset collected via a mobile network measurement and billing platform of the Historical Telecommunications Operator (HTO) network called U2020/MAE. We elucidate different network factors and study how they affect data usage patterns by taking mobile users of the HTO as a use case. Then, we compare mobile users' data usage patterns, considering total data consumption, network access, number of sessions created per user, throughput, and user satisfaction level with the services. Finally, we propose an application that employs a machine-learning model to predict traffic demand using the HTO data.