LSTM-based throughput prediction for LTE networks
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Na, Hyeonjun | - |
dc.contributor.author | Shin, Yongjoo | - |
dc.contributor.author | Lee, Dongwon | - |
dc.contributor.author | Lee, Joohyun | - |
dc.date.accessioned | 2023-07-27T12:08:40Z | - |
dc.date.available | 2023-07-27T12:08:40Z | - |
dc.date.created | 2023-06-09 | - |
dc.date.issued | 2023-04 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/188307 | - |
dc.description.abstract | Throughput prediction is crucial for reducing latency in time-critical services. We study the attention-based LSTM model for predicting future throughput. First, we collected the TCP logs and throughputs in LTE networks and transformed them using CUBIC and BBR trace log data. Then, we use the sliding window method to create input data for the prediction model. Finally, we trained the LSTM model with an attention mechanism. In the experiment, the proposed method shows lower normalized RMSEs than the other method.(c) 2021 The Author(s). Published by Elsevier B.V. on behalf of The Korean Institute of Communications and Information Sciences. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | 한국통신학회 | - |
dc.title | LSTM-based throughput prediction for LTE networks | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Lee, Joohyun | - |
dc.identifier.doi | 10.1016/j.icte.2021.12.001 | - |
dc.identifier.scopusid | 2-s2.0-85121360573 | - |
dc.identifier.wosid | 000988896800001 | - |
dc.identifier.bibliographicCitation | ICT Express, v.9, no.2, pp.247 - 252 | - |
dc.relation.isPartOf | ICT Express | - |
dc.citation.title | ICT Express | - |
dc.citation.volume | 9 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 247 | - |
dc.citation.endPage | 252 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.subject.keywordPlus | REGRESSION | - |
dc.subject.keywordAuthor | Machine learning | - |
dc.subject.keywordAuthor | Deep learning | - |
dc.subject.keywordAuthor | Throughput prediction | - |
dc.subject.keywordAuthor | LSTM | - |
dc.subject.keywordAuthor | Attention method | - |
dc.identifier.url | https://www.sciencedirect.com/science/article/pii/S2405959521001661 | - |
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