Modeling and prediction of session throughput of constant bit rate streams in wireless data networks

Abstract

This paper presents an approach to modeling and prediction of session throughput of constant bit rate streams in wireless data networks. A stable traffic generator is used to generate smooth data streams that are transmitted across various types of wireless connections in real-world wireless data networks, including wireless LANs and wireless cellular WANs. The throughput values of the data streaming sessions are recorded. Based on the analysis of statistical properties of the collected data, linear time series analysis is used to models and predict the session throughput. Autoregressive (AR) models are selected from a number of linear time series models since they can be fit to data in deterministic amount of time. The performance of AR models for prediction is compared to simpler models for prediction is compared to simpler models such as MEAN and window mean (WM) models, and our study shows that successful models, such as AR and WM models, have similar performance in predicting the session throughput of wireless data networks. The main contribution of our research is that by statistical study, it shows that session throughputs in wireless data networks can be modeled and predicted to a useful degree from past values by using linear time series analysis such as AR and WM models.