Machine Learning and Deep Learning for Throughput Prediction
- Title
- Machine Learning and Deep Learning for Throughput Prediction
- Author
- 이주현
- Keywords
- machine learning; deep learning; throughput prediction
- Issue Date
- 2021-08
- Publisher
- IEEE
- 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
- 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.
- URI
- https://ieeexplore.ieee.org/document/9528756?arnumber=9528756&SID=EBSCO:edseeehttps://repository.hanyang.ac.kr/handle/20.500.11754/172461
- ISBN
- 978-1-7281-6476-2; 978-1-7281-6475-5
- ISSN
- 2165-8536
- DOI
- 10.1109/ICUFN49451.2021.9528756
- Appears in Collections:
- COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > ELECTRICAL ENGINEERING(전자공학부) > Articles
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