一种新的深度学习信号调制方式自动识别算法研究

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关键词： 深度学习； 调制方式自动识别； 多观测样本累积； 轻量级神经网络； 小波变换； 低信噪比中图分类号：TN761⁃34 文献标识码： A 文章编号：1004⁃373X（2025）12⁃0179⁃08

Abstract：Automatic modulation recognition based on deep learning （AMR⁃DL） is a research hotspot in the field of signal modulation recognition. The modulated signals with low signal ⁃ to ⁃ noise ratio （SNR） and too many parameters of deep learning network can significantly reduce the recognition accuracy and calculation efficiency of AMR ⁃DL. A new AMR ⁃DL algorithm is proposed to address these two key issues. In the new algorithm， the multi ⁃ observation sample accumulation method is used to increase SNR. The enhanced signal is transformed into a time⁃frequency image by means of wavelet transform， transforming the signal modulation recognition problem into the classification problem of time ⁃ frequency image. A lightweight neural network is designed to extract the features extraction and classification of wavelet time⁃frequency images. The simulation results show that the proposed new ⁃ type AMR ⁃ DL algorithm has fewer lightweight neural network parameters and can achieve an accuracy of 98.1% at 0dB . In comparison with other algorithms， the recognition accuracy of modulated signals can be improved significantly under low SNR conditions.

Keywords：deep learning; automatic modulation recognition; multiple observation sample accumulation; lightweight neural network; wavelet transform; low signal⁃to⁃noise ratio

0 引 言

调 制 方 式 自 动 识 别 （Automatic ModulationRecognition， AMR）作为信号检测和解调之间的一个关键步骤，在军事和民用领域有着广泛应用。（剩余10902字）