Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

Early-Exiting DNN MIMO Detector Design with Fast Signal Estimation Process

Full metadata record
DC Field Value Language
dc.contributor.authorHua, D.-T.-
dc.contributor.authorLee, D.-
dc.contributor.authorTran, A.T.-
dc.contributor.authorLakew, D.S.-
dc.contributor.authorDo, Q.T.-
dc.contributor.authorCho, S.-
dc.date.accessioned2023-09-14T09:42:32Z-
dc.date.available2023-09-14T09:42:32Z-
dc.date.issued2023-
dc.identifier.issn2831-6991-
dc.identifier.urihttps://scholarworks.bwise.kr/cau/handle/2019.sw.cau/67583-
dc.description.abstractMulti-input multi-output (MIMO) receiver design has been continuously investigated in order to satisfy the massive requirements of six-generation (6G) wireless networks regarding the accuracy-centric signal estimation under the dynamic channel state information. In addtion, the architectures MIMO detector is a pioneering work for applying artificial intelligence techniques. Eventhough the DL-based MIMO detector can be able to provide accurate signal estimation, huge execution time and energy consumption of immensely large deep neural network (DNN) models in size has not been studied. To this end, we introduce a concept of early-exiting DNN MIMO detector and fast signal estimation, which outputting the estimated transmitted signal without a full forward and back propagation computation of the conventional DNN model. The early-exiting model and the fast signal estimation concept is believed to be suitable for scenarios of practical time sensitive and resouce limited applications. © 2023 IEEE.-
dc.format.extent3-
dc.language영어-
dc.language.isoENG-
dc.publisherInstitute of Electrical and Electronics Engineers Inc.-
dc.titleEarly-Exiting DNN MIMO Detector Design with Fast Signal Estimation Process-
dc.typeArticle-
dc.identifier.doi10.1109/ICAIIC57133.2023.10067114-
dc.identifier.bibliographicCitation5th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2023, pp 505 - 507-
dc.description.isOpenAccessN-
dc.identifier.wosid001012997600094-
dc.identifier.scopusid2-s2.0-85152052077-
dc.citation.endPage507-
dc.citation.startPage505-
dc.citation.title5th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2023-
dc.type.docTypeProceedings Paper-
dc.subject.keywordAuthordeep convolutional neural network-
dc.subject.keywordAuthordeep neural network-
dc.subject.keywordAuthorearly-exiting model-
dc.subject.keywordAuthorMultiple input multiple output-
dc.subject.keywordAuthorsignal detector-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalResearchAreaEngineering-
dc.relation.journalWebOfScienceCategoryComputer Science, Artificial Intelligence-
dc.relation.journalWebOfScienceCategoryComputer Science, Interdisciplinary Applications-
dc.relation.journalWebOfScienceCategoryEngineering, Electrical & Electronic-
dc.description.journalRegisteredClassscopus-
Files in This Item
There are no files associated with this item.
Appears in
Collections
College of Software > School of Computer Science and Engineering > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher Cho, Sung Rae photo

Cho, Sung Rae
소프트웨어대학 (소프트웨어학부)
Read more

Altmetrics

Total Views & Downloads

BROWSE