CNN-Based Modulation Classification for OFDM Signal
- Authors
- Song, Geonho; Jang, Mingyu; Yoon, Dongweon
- Issue Date
- Dec-2021
- Publisher
- IEEE Computer Society
- Keywords
- automatic modulation classification (AMC); convolutional neural network (CNN); machine learning; orthogonal frequency division multiplexing (OFDM)
- Citation
- International Conference on ICT Convergence, v.2021, no.October, pp.1326 - 1328
- Indexed
- SCOPUS
- Journal Title
- International Conference on ICT Convergence
- Volume
- 2021
- Number
- October
- Start Page
- 1326
- End Page
- 1328
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/140081
- DOI
- 10.1109/ICTC52510.2021.9620896
- ISSN
- 2162-1233
- Abstract
- 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.
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