TY - GEN
T1 - Cellular Traffic Prediction using Recurrent Neural Networks
AU - Jaffry, Shan
AU - Hasan, Syed Faraz
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/11/9
Y1 - 2020/11/9
N2 - 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.
AB - 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.
KW - Beyond 5G
KW - Cellular Traffic Prediction
KW - LSTM
KW - Recurrent Neural Network
KW - call data record
UR - http://www.scopus.com/inward/record.url?scp=85098866431&partnerID=8YFLogxK
U2 - 10.1109/ISTT50966.2020.9279373
DO - 10.1109/ISTT50966.2020.9279373
M3 - Conference Proceeding
AN - SCOPUS:85098866431
T3 - 2020 IEEE 5th International Symposium on Telecommunication Technologies, ISTT 2020 - Proceedings
SP - 94
EP - 98
BT - 2020 IEEE 5th International Symposium on Telecommunication Technologies, ISTT 2020 - Proceedings
A2 - Razak, Nur Idora Abdul
A2 - Bin Mansor, Mohd Fais
A2 - Naim, Nani Fazlina
A2 - Muhamad, Wan Norsyafizan W.
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th IEEE International Symposium on Telecommunication Technologies, ISTT 2020
Y2 - 9 November 2020 through 11 November 2020
ER -