Machine-learning based traffic forecasting for resource management in C-RAN

Abstract

The assumption of a fixed computational capacityat the Baseband Unit (BBU) pools in a Cloud Radio Access Network (C-RAN) deployment results in underutilized resourcesor unsatisfied users depending on traffic requirements. In thispaper a new strategy to predict the required resources based on Machine Learning techniques is proposed and analysed. SupportVector Machine (SVM), Time-Delay Neural Network (TDNN),and Long Short-Term Memory (LSTM) have been tested andcompared to select the best predicting approach. Instead of usinga regular synthetic scenario a realistic dense cell deployment overVienna city is used to validate the results. Authors show that theproposed solution reduces the unused resources average by 96 %