Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 이주현 | - |
dc.date.accessioned | 2022-08-17T01:30:30Z | - |
dc.date.available | 2022-08-17T01:30:30Z | - |
dc.date.issued | 2021-08 | - |
dc.identifier.citation | 2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN) Ubiquitous and Future Networks (ICUFN), 2021 Twelfth International Conference on. :452-454 Aug, 2021 | en_US |
dc.identifier.isbn | 978-1-7281-6476-2 | - |
dc.identifier.isbn | 978-1-7281-6475-5 | - |
dc.identifier.issn | 2165-8536 | - |
dc.identifier.uri | https://ieeexplore.ieee.org/document/9528756?arnumber=9528756&SID=EBSCO:edseee | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/172461 | - |
dc.description.abstract | Wireless communication contains many fluctuations than wired networks. In this paper, we present several machine learning and deep learning models to predict future network throughput, which is crucial for reducing latency in online streaming services. This paper explains the main components of the throughput prediction system. The throughput prediction model includes data input, data training, and prediction computation parts. This model accepts network throughput for the training data of the model and forecasts future data. We also present the advantages and limitations of utilizing AI models for throughput prediction. Finally, we believe that this study highlights the impact of deep learning techniques for throughput prediction. | en_US |
dc.description.sponsorship | This work has supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1C1C1005126). This work was supported by Institute of Information & communications Technology Planning & evaluation(IITP) grant funded by the Korea government(MSIT) (No.2021-0-01673, Establishment of Medium and Long Term Network Strategy for New Digital Growth). | en_US |
dc.language.iso | en | en_US |
dc.publisher | IEEE | en_US |
dc.subject | machine learning | en_US |
dc.subject | deep learning | en_US |
dc.subject | throughput prediction | en_US |
dc.title | Machine Learning and Deep Learning for Throughput Prediction | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1109/ICUFN49451.2021.9528756 | - |
dc.relation.page | 453-454 | - |
dc.contributor.googleauthor | Lee, Dongwon | - |
dc.contributor.googleauthor | Lee, Joohyun | - |
dc.sector.campus | E | - |
dc.sector.daehak | COLLEGE OF ENGINEERING SCIENCES[E] | - |
dc.sector.department | SCHOOL OF ELECTRICAL ENGINEERING | - |
dc.identifier.pid | joohyunlee | - |
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