Forecasting Wireless Network Traffic and Channel Utilization Using Real Network/Physical layer Data

Prediction of wireless network parameters, such as traffic (TU) and channel utilization (CU) data, can help in proactive resource allocation to handle the increasing amount of devices in an enterprise network. In this work, we examined the medium-to-long-scale forecasting of TU and CU data collected from an enterprise network using classical methods, such as Holt-Winters, Seasonal ARIMA (SARIMA), and machine learning methods, such as long short-term memory (LSTM) and gated recurrent unit (GRU). We also improved the performance of conventional LSTM and GRU for time series forecasting by proposing features-like grid training data structure which uses older historical data as features. The wireless network time series pre-processing methods and the verification methods are presented as time series analysis steps. The model hyper-parameters selections process and the comparison of different forecasting models are also provided. This work has proven that physical layer data has more predictive power in time series forecasting aspect with all forecasting models.