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An Open-Set Modulation Recognition Scheme With Deep Representation Learning
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DOI：10.1109/LCOMM.2023.3241388.png)
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摘要
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This letter proposes a deep representation learning based automatic modulation recognition (AMR) algorithm in the open-set recognition (OSR) regime. The challenging recognition risk of unknown modulation classes is first analyzed for most state-of-the-art approaches, and interesting insights into this problem is then provided. Based on this, an openset AMR scheme is proposed with a combination of feature representation and classification, where a triplet loss function from metric learning is employed for the representor to form distinct clusters for N known modulation classes. Then, the degree of membership is calculated via extreme value theory (EVT) by modeling the distance between known training data to its corresponding clustering center, followed by N binary classifiers. Comprehensive experiments on public dataset confirm that the proposed scheme outperforms the other state-of-the-arts in terms of both balanced accuracy and openness.
Keyword:
Automatic modulation recognition
open-set recognition
extreme value theory
metric learning
deep learning
期刊
IF:
4.4
论文数:
1.3W
被引数:
2.2W
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