Abstract
To improve the accuracy of modulated signal recognition in variable environments and reduce the impact of factors such as lack of prior 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 modulation recognition network of multi-scale analysis with deep threshold noise elimination to recognize the actually collected modulated signals under 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 accuracy at low signal-to-noise ratios than previous methods. The network demonstrates a flexible self-learning capability for different noise thresholds and the effectiveness of the designed feature fusion module in multi-scale feature acquisition for various modulation types.
摘要
为了提高多变环境下调制信号识别的准确性、减少先验知识不足等因素对识别结果的影响,研究人员逐渐采用深度学习技术来替代传统的调制信号处理技术。为了解决低信噪比下调制信号识别精度低的问题,我们设计了一种具有深度阈值噪声消除的多尺度分析调制识别网络,在标签平滑的对称交叉熵函数作用下识别实际采集的调制信号。该网络由一个具有深度自适应阈值学习的消噪编码器和一个具有多尺度特征融合的解码器组成。将两个模块进行跳跃连接,共同作用以提高整体网络的鲁棒性。实验结果表明,该方法在低信噪比下比以前的方法具有更好的识别效果。该网络展示了对噪声阈值的灵活自学习能力以及所设计的特征融合模块对各种调制类型的多尺度特征获取的有效性。
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Data availability
Due to the nature of this research, participants of this study did not agree for their data to be shared publicly, so supporting data are not available.
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Contributions
Xiang LI, Yibing LI, and Chunrui TANG designed the study. Xiang LI processed the data and drafted the paper. Yibing LI organized the paper. Chunrui TANG and Yingsong LI revised and finalized the paper.
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Xiang LI, Yibing LI, Chunrui TANG, and Yingsong LI declare that they have no conflict of interest.
List of supplementary materials
1 Modulation classification methods
2 Architecture of the signal transceiver system
3 Network in the signal recognition system framework
4 Autoencoder
5 Deep residual network
Project supported by the National Key R&D Program of China (No. 2020YFF01015000ZL) and the Fundamental Research Funds for the Central Universities, China (No. 3072022CF0806)
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Li, X., Li, Y., Tang, C. et al. Modulation recognition network of multi-scale analysis with deep threshold noise elimination. Front Inform Technol Electron Eng 24, 742–758 (2023). https://doi.org/10.1631/FITEE.2200253
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DOI: https://doi.org/10.1631/FITEE.2200253
Key words
- Signal noise elimination
- Deep adaptive threshold learning network
- Multi-scale feature fusion
- Modulation recognition