Predicting LTE Throughput Using Traffic Time Series

[Abstract] Throughput prediction is essential for congestion control and LTE network management. In this paper, the autoregressive integrated moving average (ARIMA) model and exponential smoothing model are used to predict the throughput in a single cell and whole region in an LTE network. The experimental results show that these two models perform differently in both scenarios. The ARIMA model is better than the exponential smoothing model for predicting throughput on weekdays in a whole region. The exponential smoothing model is better than the ARIMA model for predicting throughput on weekends in a whole region. The exponential smoothing model is better than the ARIMA model for predicting throughput in a single cell. In these two LTE network scenarios, throughput prediction based on traffic time series leads to more efficient resource management and better QoS.

[Keywords] ARIMA; exponential smoothing method; throughput prediction