Prediction of Data Traffic in Telecom Networks based on Deep Neural Networks

Abstract

Accurate prediction of data traffic in telecom network is a challenging task for a better network management. It advances dynamic resource allocation and power management. This study employs deep neural networks including Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) techniques to one-hour-ahead forecast the volume of expected traffic and compares this approach to other methods including Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN) and Group Method of Data Handling (GMDH). The deep neural network implementation in this study analyses, evaluates and generates predictions based on the data of telecommunications activity every one hour, continuously in one year, released by Viettel Telecom in Vietnam. The performance indexes, including RMSE, MAPE, MAE, R and Theil’s U are used to make comparison of the developed models. The obtained results show that both LSTM and GRU model outperformed the ANFIS, ANN and GMDH models. The research findings are expected to provide an assistance and forecasting tool for telecom network operators. The experimental results also indicate that the proposed model is efficient and suitable for real-world network traffic prediction.