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A deep learning based algorithm with multi-level feature extraction for automatic modulation recognition

机译：基于深度学习的自动调制识别多级特征提取算法

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摘要

Automatic modulation recognition is a critical challenge in the field of cognitive radio. In the process of communication, radio signals are modulated in various modes and are interfered by the complex electromagnetic environment. To cope with these problems and avoid manual selection of complex expert features, we propose a multi-level feature extraction algorithm based on deep learning to adequately exploit the hidden feature information of modulated signals. Our algorithm integrates the correlation between the channels of radio signals with convolutional neural networks and Bidirectional Long Short-term Memory (Bi-LSTM), and adopts the appropriate skip connection, which avoids the loss of valid information and achieves the complementarity between spatial and temporal features. In our model, the one-dimensional convolutional layer is specially utilized to enrich the feature representation of each sample point of in-phase and quadrature (I/Q) signals and emphasize the mutual influence of I channel (in-phase signal) and Q channel (quadrature signal). In addition, the label smoothing technique is used to improve the generalization ability of the model. Our proposed method is also of certain significance for other signal processing methods based on deep learning. Experiment results demonstrate that our algorithm outperforms the popular algorithms and is of higher robustness. Specifically, the proposed method improves the recognition accuracy, reaching 92.68% at high signal-to-noise ratio (SNR). In particular, it also reduces the difficulty of recognition for multiple quadrature amplitude modulation (MQAM) signals and significantly improves the recognition accuracy for 16QAM and 64QAM.
机译：自动调制识别是认知无线电领域的一个关键挑战。在通信过程中，以各种模式调制无线电信号，并且由复杂的电磁环境干扰。为了应对这些问题并避免手动选择复杂的专业特征，我们提出了一种基于深度学习的多级特征提取算法，以充分利用调制信号的隐藏特征信息。我们的算法通过卷积神经网络和双向长期短期存储器（Bi-LSTM）集成了无线电信号通道之间的相关性，并采用适当的跳过连接，避免了有效信息的丢失并实现了空间和时间之间的互补性特征。在我们的模型中，一维卷积层专门利用以丰富同相和正交（I / Q）信号的每个采样点的特征表示，并强调I通道（同相信号）和Q的相互影响信道（正交信号）。此外，标签平滑技术用于改善模型的泛化能力。我们所提出的方法对于基于深度学习的其他信号处理方法也具有一定的重要性。实验结果表明，我们的算法优于流行的算法并且具有更高的鲁棒性。具体地，所提出的方法改善了识别精度，高信噪比（SNR）达到92.68％。特别地，它还减少了对多个正交幅度调制（MQAM）信号的识别难度，并且显着提高了16QAM和64QAM的识别精度。

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  • 来源
    《Wireless Networks》 |2021年第7期|4665-4676|共12页
  • 作者

    Zhang Hang; Nie Ruihua; Lin Minghui; Wu Ruijuan; Xian Guo; Gong Xiaofeng; Yu Qin; Luo Ruisen;

  • 作者单位

    Sichuan Univ Coll Elect Engn 24 South Sect 1 One Ring Rd Chengdu 610065 Peoples R China;

    Sichuan Univ State Key Lab Hydraul & Mt River Engn Chengdu 610065 Peoples R China;

    Sichuan Univ Coll Elect Engn 24 South Sect 1 One Ring Rd Chengdu 610065 Peoples R China;

    Chengdu Dagongbochuang Informat Technol Co Ltd Chengdu 610059 Peoples R China;

    Chengdu Dagongbochuang Informat Technol Co Ltd Chengdu 610059 Peoples R China;

    Sichuan Univ Coll Elect Engn 24 South Sect 1 One Ring Rd Chengdu 610065 Peoples R China;

    Sichuan Univ Coll Elect Engn 24 South Sect 1 One Ring Rd Chengdu 610065 Peoples R China;

    Sichuan Univ Coll Elect Engn 24 South Sect 1 One Ring Rd Chengdu 610065 Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    Automatic modulation recognition; Deep learning; Spatial features; Temporal features; Attention mechanism;

    机译：自动调制识别;深度学习;空间特征;时间特征;注意机制;

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