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

Quang Hung Do, Thi Thanh Hang Doan, Thi Van Anh Nguyen, Nguyen Tung Duong, Vu Van Linh
2020 Journal of Computer Science  
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
more » ... od 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. -known traditional methods are Auto Regressive Integrated Moving Average (ARIMA), Seasonal ARIMA (SA), Markov Modulated Poisson Process models (MMPP) and Kalman filtering. A multiplicative seasonal ARIMA/GARCH model was proposed in describing and forecasting the mobile communication network traffic (Tran et al., 2015). The data traffic was collected from EVN Telecom mobile communication network. The results showed that shows a good estimation when dealing with volatility clustering in the data series. Gao et al. (2017) utilized multiplicative seasonal ARIMA and Holt-Winters models to model traffic predication. The two methods were used to analyze the trend of mobile network traffic per hour, build and validate models and then predict mobile network traffic within a given period of time. It
doi:10.3844/jcssp.2020.1268.1277 fatcat:inolodbyzjdunmkhr3oyrqumui