TY - GEN
T1 - Cellular traffic type recognition and prediction
AU - Nguyen, Tuan Anh
AU - Martins, Philippe
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/9/13
Y1 - 2021/9/13
N2 - 4G and 5G cellular traffic pattern recognition and prediction are key objectives for network optimization. They also are becoming of fundamental importance for the next-generation cellular network. Recognizing mobile traffic patterns and proactively knowing the user behaviors allow the operator to optimize the resource allocation. On the other hand, it is a complex problem due to the diverse set of applications behind the traffic. Most traffic prediction problems focus on capturing the dynamic of traffic and enhancing the performance. In this paper, we design a deep learning model for traffic pattern recognition and prediction of the type of arrival packet using Long Short-Term Memory (LSTM) neural networks. The mobile traffic information is collected from the Downlink Control Information (DCI) using the Amarisoft software. The learning phase of the model relies on a well-known traffic pattern simulated on Amarisoft 4G and 5G testbed.
AB - 4G and 5G cellular traffic pattern recognition and prediction are key objectives for network optimization. They also are becoming of fundamental importance for the next-generation cellular network. Recognizing mobile traffic patterns and proactively knowing the user behaviors allow the operator to optimize the resource allocation. On the other hand, it is a complex problem due to the diverse set of applications behind the traffic. Most traffic prediction problems focus on capturing the dynamic of traffic and enhancing the performance. In this paper, we design a deep learning model for traffic pattern recognition and prediction of the type of arrival packet using Long Short-Term Memory (LSTM) neural networks. The mobile traffic information is collected from the Downlink Control Information (DCI) using the Amarisoft software. The learning phase of the model relies on a well-known traffic pattern simulated on Amarisoft 4G and 5G testbed.
U2 - 10.1109/PIMRC50174.2021.9569524
DO - 10.1109/PIMRC50174.2021.9569524
M3 - Conference contribution
AN - SCOPUS:85118446876
T3 - IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC
SP - 1167
EP - 1172
BT - Proceedings - 2001 IEEE International Conference on Data Mining, ICDM 2001
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 32nd IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2021
Y2 - 13 September 2021 through 16 September 2021
ER -