Cellular network average user throughput-downlink prediction by machine learning

Abstract

Communication service providers (CSPs) face enormous pressure to cope up with the massive demand for data connectivity due to the rapid spreading of smart devices and the rapid growth of data-intensive applications. CSPs are committed to provide their subscribers with a high level of customer experience. In order to achieve this commitment, CSPs need to expand their network capacity to provide a better throughput (the amount of bits a user can receive for download) to their subscribers. This work is a feasibility study to build a simple unified global model using data collected from different CSPs to predict the average user throughput in Downlink (DL), that can be provided by a CSP to any subscriber. Three state-of-the-art machine learning (ML) algorithms [Random Forest (RF), Gradient Boosting Machines (GBM), and Artificial Neural Networks (ANN)] have been experimented to predict the average user throughput in DL using LTE E-UTRAN measurements and selected configuration parameters. The result has indicated that, the performance of the ensemble methods (RF and GBM) outperforms the performance of the ANN. The proposed model is an ensemble model that combines both RF and GBM and reports their average as the final predicted average user throughput.