@inproceedings{68cdb2331e8f4a20b2a825a5eee10e41,
title = "A CNN-based Transportation Type Identification for 5G Mobile Networks Using Cellular Information",
abstract = "Mobility type identification is posed as the first step for 5G mobile networks to realize mobility management. The telecom operators can deploy customized network slices for different users with different mobility types. In this paper, we propose Cellular to Image Transportation Type Identification (CITTI) to identify transportation types by using cellular information only. CITTI takes a set of cellular data and transforms them into cellular-based images. The Convolutional Neural Network-based identifier is then used to identify the transportation type. With over 700 hours of two real-world datasets, several experiments are conducted to verify the accuracy of the proposed CITTI. The experimental results show that CITTI can achieve almost 95% accuracy and outperforms prior studies.",
keywords = "5G, Cellular Information, Deep learning, Transportation Type Identification",
author = "Tseng, {Yi Ting} and Lin, {Yi Hao} and Jyh-Cheng Chen and Lin, {Hsien Ting} and Ho, {Fong Man} and Lin, {Chia Hung}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Conference on Communications, ICC 2021 ; Conference date: 14-06-2021 Through 23-06-2021",
year = "2021",
month = jun,
day = "14",
doi = "10.1109/ICC42927.2021.9500753",
language = "English",
series = "IEEE International Conference on Communications",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "ICC 2021 - IEEE International Conference on Communications, Proceedings",
address = "美國",
}