Robustness Analysis of Hybrid Machine Learning Model for Anomaly Forecasting in Radio Access Networks

Abstract

Quality of Service in mobile networks is a vigorous necessity that depends on the traffic demand growth and the complex emergence of several new services and technologies. It can be improved by reducing the network failures and avoiding the congestion. As a result, a hybrid model can be used for proactive traffic congestion avoidance to alert the operator thus enhancing the end user perceived QoS. This model consists of a co-clustering algorithm to group cells that have similar behaviour based on key performance indicators and a logistic regression model to predict congestion. The hybrid model is compared to most known deep learning models presented in the literature. We consider a Long Short-Term Memory based on recurrent neural network approach and a Temporal Convolutional Network approach for comparison. The different models are compared using real field data from operational Long Term Evolution networks.