Bandwidth Prediction for 5G Cellular Networks

Abstract

Effective bandwidth prediction in the fifth-generation (5G) cellular networks is essential for bandwidth-consuming applications, such as virtual reality and holographic video streaming. However, accurate bandwidth prediction in 5G networks remains a challenging task due to the short-distance coverage and frequent handover properties of 5G base stations. In this paper, we propose HYPER, a HYbrid bandwidth PrEdiction appRoach using commercial smartphones. Hyper uses an AutoRegressive Moving Average (ARMA) time series predictive model for intra-cell bandwidth prediction and a Random Forest (RF) regression model for cross-cell bandwidth prediction. Our ARMA model takes prior bandwidth usage as its input, while the RF model further uses related network and physical features to predict future bandwidth. We conduct a measurement study in commercial 5G networks to analyze the relationship between these features and bandwidth. Moreover, we also propose a handover window adaptation algorithm to automatically adjust the handover window size and determine which model to use during handover. We use commercial 5G smartphones for data collection and conduct extensive experiments in diverse urban environments. Experimental results based on one TB of cellular data show that HYPER can reduce the bandwidth prediction error by more than 13% compared to state-of-the-art bandwidth prediction approaches.