@inproceedings{e456fb24c5634ad5979a1b643dde096c,
title = "TransTraffic: Predicting Network Traffic using Low Resource Data",
abstract = "In private 5G/6G networks, an adequate and accurate resource management is essential. In this paper, we propose a traffic prediction model, TransTraffic, that utilizes transfer learning for low resource data. Our evaluation demonstrates that leveraging prior knowledge from a similar traffic domain helps predict network traffic for a new domain or service.",
keywords = "5G/6G networks, traffic prediction, transfer learning",
author = "Chaewon Kang and Jeewoo Yoon and Daejin Choi and Eunil Park and Sangheon Pack and Jinyoung Han",
note = "Funding Information: ACKNOWLEDGMENT This research was supported by National Research Foundation (NRF) of Korea Grant funded by the Korean Government (MSIT) (No. 2021R1A4A3022102), and the MSIT (Ministry of Science and ICT), Korea, under the ICAN (ICT Challenge and Advanced Network of HRD) program (IITP-2022-2020-0-01816) supervised by the IITP (Institute of Information & Communications Technology Planning & Evaluation). Publisher Copyright: {\textcopyright} 2022 IEEE.; 13th International Conference on Information and Communication Technology Convergence, ICTC 2022 ; Conference date: 19-10-2022 Through 21-10-2022",
year = "2022",
doi = "10.1109/ICTC55196.2022.9952575",
language = "English",
series = "International Conference on ICT Convergence",
publisher = "IEEE Computer Society",
pages = "786--788",
booktitle = "ICTC 2022 - 13th International Conference on Information and Communication Technology Convergence",
}