Feature Image-Based Automatic Modulation Classification Method Using CNN Algorithm

Lee, J.H.; Kim, K.-Y.; Shin, Y.

doi:10.1109/ICAIIC.2019.8669002

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Feature Image-Based Automatic Modulation Classification Method Using CNN Algorithm

Lee, J.H.;
Kim, K.-Y.;
Shin, Y.

Citations

SCOPUS

78

초록

In this paper, we propose a feature image-based automatic modulation classification (AMC) method to classify modulation type. The proposed method uses a convolutional neural network (CNN) which is one of deep learning algorithms for image classification. In order to classify the modulation type, various features are transformed in a two-dimensional image and this image is used as the input of the CNN. From the simulation results, we show that the proposed method improves classification performance. © 2019 IEEE.

키워드

automatic modulation classification; convolutional neural network; cumulant; deep learning; feature extraction; Classification (of information); Computerized tomography; Convolution; Data mining; Deep learning; Feature extraction; Learning algorithms; Modulation; Neural networks; Automatic modulation classification; Automatic modulation classification (AMC); Classification performance; Convolutional neural network; Cumulants; Feature images; Modulation types; Two dimensional images; Image classification

제목: Feature Image-Based Automatic Modulation Classification Method Using CNN Algorithm

저자: Lee, J.H.; Kim, K.-Y.; Shin, Y.

DOI: 10.1109/ICAIIC.2019.8669002

발행일: 2019-03

유형: Conference Paper

저널명: 1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019

페이지: 560 ~ 563

언어: ENG

출판사: Institute of Electrical and Electronics Engineers Inc.

발행국가: 미국

분량: 4 페이지

ISSN: P 0000-0000