Throughput Prediction using Machine Learning in LTE and 5G Networks

The emergence of novel cellular network technologies, within 5G, are envisioned as key enablers of a new set of use-cases, including industrial automation, intelligent transportation, and tactile internet. The critical nature of the traffic requirements ranges from ultra-reliable communications, massive connectivity, and enhanced mobile broadband. Thus, the growing research on cellular network monitoring and prediction aims for ensuring a satisfied user-base and fulfillment of service level agreements. The scope of this study is to develop an approach for predicting the cellular link throughput of end-users, with a goal to benchmark the performance of network slices. First, we report and analyze a measurement study involving real-life cases, such as driving in urban, sub-urban, and rural areas, as well as tests in large crowded areas. Second, we develop machine learning models using lower-layer metrics, describing the radio environment, to predict the available throughput. The models are initially validated on the LTE network and then applied to a non-standalone 5G network. Finally, we suggest scaling the proposed model into the future standalone 5G network. We have achieved 93% and 84% R^2 accuracy, with 0.06 and 0.17 mean squared error, in predicting the end-user's throughput in LTE and non-standalone 5G network, respectively.