Cellular Traffic Prediction using Recurrent Neural Networks

2023

Jaffry, Shan

Hasan, Syed Faraz

Conference Publication

en

Publication

Institute of Electrical and Electronics Engineers

United States of America

10.1109/ISTT50966.2020.9279373

une:1959.11/63407

Autonomous network traffic prediction will be a key feature in beyond 5G networks. In the past, researchers have used statistical methods such as Auto Regressive Integrated Moving Average (ARIMA) to provide traffic prediction. However ARIMA based models fail to provide accurate predictions in highly dynamic cellular environment. Hence, researchers are exploring deep learning techniques such as Recurrent Neural Networks (RNN) and Long-Short-Term-Memory (LSTM) to develop autonomous cellular traffic prediction models. This paper proposes a LSTM based cellular traffic prediction model using real world call data record. We have compared the LSTM based prediction with ARIMA model and vanilla Feed-Forward Neural Network (FFNN). The results show that LSTM and FFNN can accurately predict cellular traffic. However, it has been found that LSTM models converged more quickly in terms of training the model for prediction.

link

Proceedings of the 5th International Symposium on Telecommunication Technologies, p. 94-98

9781728181615

9781728181622

94

98