Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICAIIC.2019.8669002 | 2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) |
Keywords | Field | DocType |
Modulation,Feature extraction,Signal to noise ratio,Classification algorithms,Convolutional neural networks,Deep learning | Convolutional neural network,Computer science,Signal-to-noise ratio,Algorithm,Image based,Feature extraction,Modulation,Artificial intelligence,Deep learning,Contextual image classification,Statistical classification | Conference |
ISBN | Citations | PageRank |
978-1-5386-7822-0 | 3 | 0.38 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jung-Ho Lee | 1 | 13 | 4.50 |
| Kwang-Yul Kim | 2 | 7 | 5.89 |
| Yoan Shin | 3 | 368 | 58.41 |