Machine Learning Enabled Multi-Radio Access Technology Selection in 5G Networks

In this paper, we present a machine learning algorithm for effective RAT selection in 5G networks by considering the geo-location (latitude and longitude) of the user as well as the received signal strength intensity (RSSI) from the base station as basic parameters, real live data from a 5G network base-station were collated, divided into training and testing data-sets, the training data-sets (input) were used to train models of supervised machine learning classification algorithm: Decision Tree (DT), Extra Tree (XTREE), Random Forest (RF), Gradient Boosting (GB), and eXtreme Gradient Boosting (XGBoost); these trained models are further tested with input test data-sets to predict/select the appropriate RAT (4G/5G) as labelled output. Evaluation of results showed a measure of accuracy of our chosen model of RAT selection; (XGBoost) at optimal level 93.86\%, which was further cross validated at 92.9\% when compared with other algorithms for its effectiveness on future data and mitigation ability on over-fitting and under-fitting issues, hence recommended for planning and optimization purposes in similar urban/dense-urban environment to assist in maintaining the rapidly increasing demand of network connections and devices.