Machine learning approach of multi-RAT selection for travelling users in 5G NSA networks

Abstract

The rapid increment of mobile device usage and the corresponding huge data volume generated afterwards, necessitated the utilisation of the 5G network spectrum. This is deployed today in terrestrial communication in a non-stand-alone (NSA) architectural mode; where 5G networks are supported by 4G LTE networks. Hence, the current 5G implementation with the gargantuan number of mobile subscribers, poses challenges to the choice of network Radio Access Technology (RAT) selection between 4G and 5G networks, among available multiple base-stations to mobile (travelling) users, with respect to their location, bandwidth requirement, and mobility style. Hence, to address the scenario presented above, the authors record live signal measurements of 4G and 5G networks by a travelling user, that transversed multiple 5G NSA base stations. RAT selection implementations were carried out with support vector machine (SVM), deep neural network (DNN), and eXtreme Gradient Boosting (XGBoost) algorithms to select an appropriate RAT between 4G and 5G RATs, for effective resource allocation for travelling users’ requirements. Evaluation of results with standard classification metrics shows XGBoost with overall outstanding accuracy performance at 99.64%.