Accurate Bandwidth and Delay Prediction for 5G Cellular Networks

The fifth-generation (5G) has empowerd various applications. Effective bandwidth and delay prediction in 5G cellular networks are essential for many applications, such as virtual reality and holographic video streaming. However, accurate bandwidth and delay 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 and delay prediction approach that uses an Auto Regressive Moving Average (ARMA) time series predictive model for intra-cell prediction and a Random Forest (RF) regression model for cross-cell prediction. Our ARMA model takes prior information as its input, while the RF model further uses related network and physical features to predict future performance. We conduct a measurement study in commercial 5G networks to analyze the relationship between these features and bandwidth/delay. Moreover, we also propose a handover window adaptation algorithm to automatically adjust the handover window size and determine which model to use during handover for accurate bandwidth and delay prediction. We use commercial 5G smartphones for data collection and conducted extensive experiments in diverse urban environments. Experimental results show that HYPER can reduce the prediction error by more than 13% compared to state-of-the-art prediction approaches.