Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 윤동원 | - |
dc.date.accessioned | 2019-01-17T02:32:53Z | - |
dc.date.available | 2019-01-17T02:32:53Z | - |
dc.date.issued | 2016-10 | - |
dc.identifier.citation | 2016 International Conference on Information and Communication Technology Convergence (ICTC), Page. 19-21 | en_US |
dc.identifier.isbn | 978-1-5090-1325-8 | - |
dc.identifier.uri | https://ieeexplore.ieee.org/document/7763537/?arnumber=7763537&SID=EBSCO:edseee | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/81342 | - |
dc.description.abstract | Deep neural network (DNN) has recently received much attention due to its superior performance in classifying data with complex structure. In this paper, we investigate application of DNN technique to automatic classification of modulation classes for digitally modulated signals. First, we select twenty one statistical features which exhibit good separation in empirical distributions for all modulation formats considered (i.e., BPSK, QPSK, 8PSK, 16QAM, and 64QAM). These features are extracted from the received signal samples and used as the input to the fully connected DNN with three hidden layer. The training data containing 25,000 feature vectors is generated by the computer simulation under both additive Gaussian white noise (AWGN) and Rician fading channels. Our test results show that the proposed method brings dramatic performance improvement over the existing classifier especially for high Doppler fading channels. | en_US |
dc.description.sponsorship | This work was supported by the research fund of Signal Intelligence Research Center supervised by Defense Acquisition Program Administration and Agency for Defense Development of Korea. | en_US |
dc.language.iso | en | en_US |
dc.publisher | IEEE | en_US |
dc.subject | Deep neural network | en_US |
dc.subject | automatic modulation classification | en_US |
dc.subject | digital modulations | en_US |
dc.subject | fading channels | en_US |
dc.title | Deep Neural Network-Based Automatic Modulation Classification Technique | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1109/ICTC.2016.7763537 | - |
dc.relation.page | 19-21 | - |
dc.contributor.googleauthor | Kim, Byeoungdo | - |
dc.contributor.googleauthor | Kim, Jaekyum | - |
dc.contributor.googleauthor | Chae, Hyunmin | - |
dc.contributor.googleauthor | Yoon, Dongweon | - |
dc.contributor.googleauthor | Choi, Jun Won | - |
dc.sector.campus | S | - |
dc.sector.daehak | COLLEGE OF ENGINEERING[S] | - |
dc.sector.department | DEPARTMENT OF ELECTRONIC ENGINEERING | - |
dc.identifier.pid | dwyoon | - |
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