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Effective Feature-Based Automatic Modulation Classification Method Using DNN Algorithm
- Lee, S.H.;
- Kim, K.-Y.;
- Kim, J.H.;
- Shin, Y.
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6초록
In this paper, we propose an effective feature-based automatic modulation classification (AMC) method using a deep neural network (DNN). In order to classify the modulation type, we consider effective features according to the modulation signals. The proposed method removes the meaningless features that have little influence on the classification and only uses the effective features that have high influence by analyzing the correlation coefficients. From the simulation results, we observe that the proposed method can make the AMC system low complexity. © 2019 IEEE.
키워드
automatic modulation classification; correlation; cumulant; deep neural network; effective features; Artificial intelligence; Classification (of information); Correlation methods; Deep neural networks; Automatic modulation classification; Automatic modulation classification (AMC); Correlation coefficient; Cumulants; effective features; Feature-based; Modulation signals; Modulation types; Modulation
- 제목
- Effective Feature-Based Automatic Modulation Classification Method Using DNN Algorithm
- 저자
- Lee, S.H.; Kim, K.-Y.; Kim, J.H.; Shin, Y.
- 발행일
- 2019-03
- 유형
- Conference Paper
- 저널명
- 1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019
- 페이지
- 557 ~ 559
- 언어
- ENG
- 출판사
- Institute of Electrical and Electronics Engineers Inc.
- 발행국가
- 미국
- 분량
- 3 페이지
- ISSN
- P 0000-0000