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TransTraffic: Predicting Network Traffic using Low Resource Data
- Issue Date
- Oct-2022
- Publisher
- IEEE Computer Society
- Keywords
- 5G/6G networks; traffic prediction; transfer learning
- Citation
- International Conference on ICT Convergence, v.2022-October, pp 786 - 788
- Pages
- 3
- Indexed
- SCOPUS
- Journal Title
- International Conference on ICT Convergence
- Volume
- 2022-October
- Start Page
- 786
- End Page
- 788
- ISSN
- 2162-1233
- 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. © 2022 IEEE.
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Collections - Computing and Informatics > Convergence > 1. Journal Articles
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Related Researcher
- HAN, JIN YOUNG
- Computing and Informatics (Convergence)