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CNN-Based Modulation Classification for OFDM Signal
- Song, Geonho;
- Jang, Mingyu;
- Yoon, Dongweon
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5초록
Automatic modulation classification (AMC) is one of the important parts in cooperative and noncooperative contexts. This paper approaches the AMC problem by using deep learning. We propose a convolutional neural network (CNN)-based AMC to classify the modulation type of received orthogonal frequency division multiplexing (OFDM) signal and analyze its classification performance. CNN model is trained by using received OFDM signals for different modulation types and signal-to-noise ratios, and then classification accuracy is validated through computer simulations.
키워드
automatic modulation classification (AMC); convolutional neural network (CNN); machine learning; orthogonal frequency division multiplexing (OFDM); Convolution; Convolutional neural networks; Deep learning; Modulation; Signal to noise ratio; Automatic modulation; Automatic modulation classification; Convolutional neural network; Modulation classification; Modulation types; Multiplexing signals; Network-based; Orthogonal frequency division multiplexing; Orthogonal frequency-division multiplexing; Orthogonal frequency division multiplexing
- 제목
- CNN-Based Modulation Classification for OFDM Signal
- 저자
- Song, Geonho; Jang, Mingyu; Yoon, Dongweon
- 발행일
- 2021-12
- 유형
- Proceedings Paper
- 저널명
- 12TH INTERNATIONAL CONFERENCE ON ICT CONVERGENCE (ICTC 2021): BEYOND THE PANDEMIC ERA WITH ICT CONVERGENCE INNOVATION
- 권
- 2021
- 호
- October
- 페이지
- 1326 ~ 1328
- 언어
- ENG
- 출판사
- IEEE
- 발행국가
- 미국
- 분량
- 3 페이지
- ISSN
- P 2162-1233