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Multi-Component Feature Extraction for Few-Sample Automatic Modulation Classification
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AI
DOI：10.1109/LCOMM.2023.3318288.png)
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摘要
En 中文
With the rapid development of deep learning (DL), Automatic Modulation Classification (AMC) has also taken a huge leap forward. The DL-based AMC methods are able to achieve high accuracy through training massive labeled samples. However, these DL-based AMC methods would deteriorate dramatically with insufficient samples. Modulation recognition under few sample condition gradually become an urgent problem. To address this problem, we propose a novel learning framework for few-sample AMC, which is termed Multi-Component Extraction Network (MCENet) and can effectively extract potentially and easily distinguishable features. Experimental results on the public available dataset RadioML2016.10a show that the proposed MCENet outperforms other contrastive few-sample AMC methods and achieves better results.
Keyword:
Automatic modulation classification
few-sample
multi-component
feature extraction
期刊
IF:
4.4
论文数:
1.3W
被引数:
2.2W
机构
引用论文
MCNet: An Efficient CNN Architecture for Robust Automatic Modulation ClassificationMCNet: 一种用于鲁棒自动调制分类的高效CNN架构
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IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏Automatic Modulation Classification Based on Decentralized Learning and Ensemble Learning基于分散学习和集成学习的自动调制分类
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