Name
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICUFN49451.2021.9528756 | 12TH INTERNATIONAL CONFERENCE ON UBIQUITOUS AND FUTURE NETWORKS (ICUFN 2021) |
Keywords | DocType | ISSN |
machine learning, deep learning, throughput prediction | Conference | 2165-8528 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dongwon Lee | 1 | 0 | 0.34 |
| Joohyun Lee | 2 | 4 | 2.79 |