Deep Learning-Based Modulation Identification for OFDM Systems
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Song, Geonho | - |
dc.contributor.author | Jang, Mingyu | - |
dc.contributor.author | Yoon, Dongweon | - |
dc.date.accessioned | 2023-08-22T02:57:20Z | - |
dc.date.available | 2023-08-22T02:57:20Z | - |
dc.date.created | 2023-08-17 | - |
dc.date.issued | 2023-06 | - |
dc.identifier.issn | 2157-8672 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/189400 | - |
dc.description.abstract | This paper deals with a deep learning (DL)-based automatic modulation classification (AMC) method for orthogonal frequency division multiplexing (OFDM) systems. Among DL methods for AMC, convolution neural network (CNN) has been widely studied to classify the modulation scheme used in the OFDM systems. Although conventional CNN has performed well in previous studies, its classification performance can be degraded when an additional modulation scheme is considered. In this paper, we investigate the CNN-based AMC for the OFDM systems to improve the classification performance by using a deeper CNN model with a residual connection. Through computer simulations, we show that the proposed model outperforms the conventional CNN model for various ranges of training signal-to-noise ratios in terms of classification accuracy. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | IEEE Computer Society | - |
dc.title | Deep Learning-Based Modulation Identification for OFDM Systems | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Yoon, Dongweon | - |
dc.identifier.doi | 10.1109/IWSSIP58668.2023.10180258 | - |
dc.identifier.scopusid | 2-s2.0-85166356000 | - |
dc.identifier.bibliographicCitation | International Conference on Systems, Signals, and Image Processing, v.2023-June, pp.1 - 4 | - |
dc.relation.isPartOf | International Conference on Systems, Signals, and Image Processing | - |
dc.citation.title | International Conference on Systems, Signals, and Image Processing | - |
dc.citation.volume | 2023-June | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 4 | - |
dc.type.rims | ART | - |
dc.type.docType | Conference paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | Convolution | - |
dc.subject.keywordPlus | Convolutional neural networks | - |
dc.subject.keywordPlus | Deep learning | - |
dc.subject.keywordPlus | Learning systems | - |
dc.subject.keywordPlus | Orthogonal frequency division multiplexing | - |
dc.subject.keywordPlus | Signal to noise ratio | - |
dc.subject.keywordPlus | Neural network models | - |
dc.subject.keywordPlus | Automatic modulation | - |
dc.subject.keywordPlus | Automatic modulation classification | - |
dc.subject.keywordPlus | Classification performance | - |
dc.subject.keywordPlus | Convolution neural network | - |
dc.subject.keywordPlus | Convolutional neural network | - |
dc.subject.keywordPlus | Modulation classification | - |
dc.subject.keywordPlus | Modulation schemes | - |
dc.subject.keywordPlus | Neural network model | - |
dc.subject.keywordPlus | Orthogonal frequency division multiplexing systems | - |
dc.subject.keywordPlus | Orthogonal frequency-division multiplexing | - |
dc.subject.keywordAuthor | automatic modulation classification | - |
dc.subject.keywordAuthor | convolutional neural network | - |
dc.subject.keywordAuthor | orthogonal frequency division multiplexing | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/10180258 | - |
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