Learning from Noisy Labels for MIMO Detection With One-Bit ADCs
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, Jinsung | - |
dc.contributor.author | Lee, Namyoon | - |
dc.contributor.author | Hong, Songnam | - |
dc.contributor.author | Jeon, Yo-Seb | - |
dc.date.accessioned | 2023-06-01T06:45:50Z | - |
dc.date.available | 2023-06-01T06:45:50Z | - |
dc.date.created | 2023-02-08 | - |
dc.date.issued | 2023-03 | - |
dc.identifier.issn | 2162-2337 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/185756 | - |
dc.description.abstract | This paper presents a data detection method for multiple-input multiple-output systems with one-bit analog-to-digital converters. The basic idea is to learn the likelihood function of the system from training samples. To this end, a training data generation strategy is first proposed, which labels a one-bit received signal with a symbol index determined by channel-based data detection. This strategy requires no extra training overhead beyond pilot symbols for channel estimation, but leads to noisy labels due to data detection errors. For accurate learning from the noisy labels, an expectation-maximization algorithm is also developed. This algorithm learns both the likelihood function and the transition probability from each noisy label to a true label. Numerical results demonstrate that the presented method performs similar to the optimal maximum likelihood detection. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Learning from Noisy Labels for MIMO Detection With One-Bit ADCs | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Hong, Songnam | - |
dc.identifier.doi | 10.1109/LWC.2022.3230403 | - |
dc.identifier.scopusid | 2-s2.0-85146224579 | - |
dc.identifier.wosid | 000965226700001 | - |
dc.identifier.bibliographicCitation | IEEE WIRELESS COMMUNICATIONS LETTERS, v.12, no.3, pp.456 - 460 | - |
dc.relation.isPartOf | IEEE WIRELESS COMMUNICATIONS LETTERS | - |
dc.citation.title | IEEE WIRELESS COMMUNICATIONS LETTERS | - |
dc.citation.volume | 12 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 456 | - |
dc.citation.endPage | 460 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.subject.keywordPlus | MAXIMUM-LIKELIHOOD | - |
dc.subject.keywordPlus | SYSTEMS | - |
dc.subject.keywordAuthor | Channel estimation | - |
dc.subject.keywordAuthor | MIMO communication | - |
dc.subject.keywordAuthor | Training | - |
dc.subject.keywordAuthor | Symbols | - |
dc.subject.keywordAuthor | Noise measurement | - |
dc.subject.keywordAuthor | Receivers | - |
dc.subject.keywordAuthor | Indexes | - |
dc.subject.keywordAuthor | MIMO systems | - |
dc.subject.keywordAuthor | data detection | - |
dc.subject.keywordAuthor | noisy labels | - |
dc.subject.keywordAuthor | expectation maximization | - |
dc.subject.keywordAuthor | training data generation | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/9993788 | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
222, Wangsimni-ro, Seongdong-gu, Seoul, 04763, Korea+82-2-2220-1365
COPYRIGHT © 2021 HANYANG UNIVERSITY.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.