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Journal of Zhejiang University SCIENCE C 1998 Vol.-1 No.-1 P.

http://doi.org/10.1631/FITEE.2200253


Modulation recognition network of multi-scale analysis with deep threshold noise elimination


Author(s):  Xiang LI, Yibing LI, Chunrui TANG, Yingsong LI

Affiliation(s):  College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China; more

Corresponding email(s):   chunruitang@126.com

Key Words:  Signal noise elimination, Deep adaptive threshold learning network, Multi-scale feature fusion, Modulation recognition


Xiang LI, Yibing LI, Chunrui TANG, Yingsong LI. Modulation recognition network of multi-scale analysis with deep threshold noise elimination[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .

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author="Xiang LI, Yibing LI, Chunrui TANG, Yingsong LI",
journal="Frontiers of Information Technology & Electronic Engineering",
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year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2200253"
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DOI - 10.1631/FITEE.2200253


Abstract: 
To improve the accuracy of modulated signal recognition in variable environments and reduce the impact of factors such as lack of priori knowledge on recognition results, researchers have gradually adopted deep learning techniques to replace traditional modulated signal processing techniques. To address the problem of low recognition accuracy of the modulated signal at low signal-to-noise ratios, we have designed a novel network based on multi-scale analysis of deep threshold noise elimination to recognize the actual collected modulated signals under the action of a symmetric cross-entropy function of label smoothing. The network consists of a denoising encoder with deep adaptive threshold learning and a decoder with multi-scale feature fusion. The two modules are skip-connected to work together to improve the robustness of the overall network. Experimental results show that this method has better recognition at low signal-to-noise ratios than previous methods. The network demonstrates a flexible self-learning capability for noise thresholds and the effectiveness of the designed feature fusion module for multi-scale feature acquisition for various modulated types.

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