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.
| Original language | English |
|---|---|
| Title of host publication | ICTC 2022 - 13th International Conference on Information and Communication Technology Convergence |
| Subtitle of host publication | Accelerating Digital Transformation with ICT Innovation |
| Publisher | IEEE Computer Society |
| Pages | 786-788 |
| Number of pages | 3 |
| ISBN (Electronic) | 9781665499392 |
| DOIs | |
| Publication status | Published - 2022 |
| Event | 13th International Conference on Information and Communication Technology Convergence, ICTC 2022 - Jeju Island, Korea, Republic of Duration: 2022 Oct 19 → 2022 Oct 21 |
Publication series
| Name | International Conference on ICT Convergence |
|---|---|
| Volume | 2022-October |
| ISSN (Print) | 2162-1233 |
| ISSN (Electronic) | 2162-1241 |
Conference
| Conference | 13th International Conference on Information and Communication Technology Convergence, ICTC 2022 |
|---|---|
| Country/Territory | Korea, Republic of |
| City | Jeju Island |
| Period | 22/10/19 → 22/10/21 |
Bibliographical 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:
© 2022 IEEE.
Keywords
- 5G/6G networks
- traffic prediction
- transfer learning
ASJC Scopus subject areas
- Information Systems
- Computer Networks and Communications