A Numerical Study of Cosmic Proton Modulation Using AMS-02 Observations
A Numerical Study of Cosmic Proton Modulation Using AMS-02 Observations
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使用 AMS-02 观测进行宇宙质子调制的数值研究
DOI：
10.3847/1538-4357/ab1b2a
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Xueshang Feng
中科院分区：
文献类型：
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作者：
Xi Luo;Marius S. Potgieter;V. Bindi;Ming Zhang;Xueshang Feng
针对无线频谱监测应用，提出了一种新颖高效的端到端自动调制分类（AMC）学习模型，该模型无需手工设计专家特征，即可从时域同相和正交（IQ）数据中自动学习。利用卷积层的直观性，以池化为前端特征提取和降维，开发了顺序卷积递归神经网络（SCRNNs），以互补卷积神经网络（cnn）的并行计算能力和递归神经网络（rnn）的时间敏感性。实验结果表明，该架构在信噪比（SNR）在-10 dB以上的范围内具有整体优越的性能，在高信噪比下，分类准确率从80%提高到92.1%，训练时间和预测时间分别大幅减少约74%和67%。此外，还进行了比较研究，探讨了不同SCRNN结构设置对分类性能的影响。提出了一种具有代表性的scnn结构，该结构具有两层CNN和随后的两层长短期记忆（LSTM），为快速AMC提供了选择。
A novel and efficient end-to-end learning model for automatic modulation classification (AMC) is proposed for wireless spectrum monitoring applications, which automatically learns from the time domain in-phase and quadrature (IQ) data without requiring the design of hand-crafted expert features. With the intuition of convolutional layers with pooling serving as front-end feature distillation and dimensionality reduction, sequential convolutional recurrent neural networks (SCRNNs) are developed to take complementary advantage of parallel computing capability of convolutional neural networks (CNNs) and temporal sensitivity of recurrent neural networks (RNNs). Experimental results demonstrate that the proposed architecture delivers overall superior performance in signal to noise ratio (SNR) range above -10 dB, and achieves significantly improved classification accuracy from 80% to 92.1% at high SNRs, while drastically reduces the training and prediction time by approximately 74% and 67%, respectively. Furthermore, a comparative study is performed to investigate the impacts of various SCRNN structure settings on classification performance. A representative SCRNN architecture with the two-layer CNN and subsequent two-layer long short-term memory (LSTM) is developed to suggest the option for fast AMC.