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1. WO2020130277 - METHOD FOR AUTOMATIC MODULATION CLASSIFICATION THROUGH CORRELATION ANALYSIS BETWEEN FEATURES

Publication Number WO/2020/130277
Publication Date 25.06.2020
International Application No. PCT/KR2019/010539
International Filing Date 20.08.2019
IPC
G06N 3/08 2006.01
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
08Learning methods
G06N 3/04 2006.01
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
04Architecture, e.g. interconnection topology
Applicants
  • 숭실대학교산학협력단 FOUNDATION OF SOONGSIL UNIVERSITY-INDUSTRY COOPERATION [KR]/[KR]
Inventors
  • 신요안 SHIN, Yo An
  • 이상훈 LEE, Sang Hoon
  • 김광열 KIM, Kwang Yul
Agents
  • 심경식 SHIM, Kyoung-Shik
  • 홍성욱 HONG, Sung-Wook
Priority Data
10-2018-016496419.12.2018KR
10-2019-002260626.02.2019KR
Publication Language Korean (KO)
Filing Language Korean (KO)
Designated States
Title
(EN) METHOD FOR AUTOMATIC MODULATION CLASSIFICATION THROUGH CORRELATION ANALYSIS BETWEEN FEATURES
(FR) PROCÉDÉ DE CLASSIFICATION DE MODULATION AUTOMATIQUE PAR ANALYSE DE CORRÉLATION ENTRE DES CARACTÉRISTIQUES
(KO) 특징 간 상관 분석을 통한 자동 변조 분류 방법
Abstract
(EN)
A method for automatic modulation classification through correlation analysis between features, according to the present invention comprises the steps of: (a) an automatic modulation classification (AMC) system defining feature values to classify a plurality of signals; (b) the automatic modulation classification system computing correlation coefficients between the feature values for the respective signals; (c) the automatic modulation classification system calculating representative values for the respective signals; and (d) the automatic modulation classification system measuring classification rates through the features, with the features excluded one by one, in SNR environments by using a DNN structure, whereby the correlation coefficients are analyzed without meaningless features that minimally influence the classification in satellite communication or wireless communication, to allow the automatic modulation classification system to use only effective features with great influence so that the complexity of the AMC system can be reduced.
(FR)
L'invention concerne un procédé de classification de modulation automatique par analyse de corrélation entre des caractéristiques, comprenant les étapes suivantes : (A) un système de classification de modulation automatique (AMC) définit des valeurs de caractéristiques pour classer une pluralité de signaux ; (b) le système de classification de modulation automatique calcule des coefficients de corrélation entre les valeurs de caractéristiques pour les signaux respectifs ; (c) le système de classification de modulation automatique calcule des valeurs représentatives pour les signaux respectifs ; et (d) le système de classification de modulation automatique mesure les taux de classification par l'intermédiaire des caractéristiques, les caractéristiques étant exclues une par une, dans des environnements SNR à l'aide d'une structure DNN, ce par quoi les coefficients de corrélation sont analysés sans caractéristiques non significatives qui influencent de manière minimale la classification dans une communication par satellite ou dans une communication sans fil, pour permettre au système de classification de modulation automatique d'utiliser uniquement des caractéristiques efficaces avec une grande influence de telle sorte que la complexité du système AMC peut être réduite.
(KO)
본 발명에 따른 특징 간 상관 분석을 통한 자동 변조 분류 방법은 (a) 상기 자동 변조 분류 시스템이 복수의 신호를 분류하기 위해 특징값을 정의하는 단계; (b) 상기 자동 변조 분류 시스템이 각 신호별 특징값 간에 상관 계수를 계산하는 단계; (c) 상기 자동 변조 분류 시스템이 각 신호별 대표값을 산출하는 단계; 및 (d) 상기 자동 변조 분류 시스템이 DNN 구조를 사용하여 SNR 환경에서 각 특징으로 한 개씩 제외하면서 분류율을 측정하는 단계;를 포함하여 위성통신 또는 무선통신에서 분류에 거의 영향을 미치지 않는 의미 없는 특징들을 제거하고 상관 계수를 분석함으로써 영향력이 큰 효과적인 특징만을 사용하기 때문에 자동 변조 분류(AMC:Automatic Modulation Classification)시스템의 복잡성을 낮출 수 있는 효과가 있다.
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