何荣荣,徐逸凡,刘洁,等. 基于软阈值深度学习的自动调制识别算法[J]. 电波科学学报,2022,37(3):465-470. DOI: 10.12265/j.cjors.2021052
      引用本文: 何荣荣,徐逸凡,刘洁,等. 基于软阈值深度学习的自动调制识别算法[J]. 电波科学学报,2022,37(3):465-470. DOI: 10.12265/j.cjors.2021052
      HE R R, XU Y F, LIU J, et al. A deep learning algorithm with soft threshold for automatic modulation recognition[J]. Chinese journal of radio science,2022,37(3):465-470. (in Chinese). DOI: 10.12265/j.cjors.2021052
      Citation: HE R R, XU Y F, LIU J, et al. A deep learning algorithm with soft threshold for automatic modulation recognition[J]. Chinese journal of radio science,2022,37(3):465-470. (in Chinese). DOI: 10.12265/j.cjors.2021052

      基于软阈值深度学习的自动调制识别算法

      A deep learning algorithm with soft threshold for automatic modulation recognition

      • 摘要: 自动调制识别是认知无线电、电子侦察、电磁态势生成中重要的环节. 由于电磁环境日益复杂,噪声对能否正确调制识别影响显著. 本文针对低信噪比(signal-noise ratio,SNR)环境条件设计了一种基于软阈值的深度学习模型,在卷积神经网络(convolutional neural networks, CNN)的基础上加入软阈值函数. 将IQ数据转化为幅度相位信息作为模型的输入,CNN用于提取幅度相位数据中的特征,软阈值学习网络可以针对不同特征设置不同阈值,用于滤除样本噪声,提高低SNR条件下的识别率. 在开源数据集RML2016.10a上验证了所提算法的有效性,对比其他网络结构,本文提出的模型识别率更高且效率更高.

         

        Abstract: Due to the increasing complexity of electromagnetic spectrum environment, noise has significant influence on correct modulation recognition. In this paper, a deep learning model based on soft threshold is designed under the condition of low signal-noise ratio (SNR). The model is based on the convolutional neural network(CNN), and the soft threshold function is added. The IQ data is transformed into amplitude and phase information as the input of the model. The CNN is used to extract the features from the amplitude and phase data, and the soft threshold learning network can learn different thresholds aims to different features, which filters out the sample noise and improves the recognition rate under the condition of low SNR. Validation on the open source data set RML2016.10a shows that compared with other network structures, the model proposed in this paper has higher recognition rate and lower time complexity.

         

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