Spread Spectrum and Conventional Modulation Signal Recognition Method Based on Generative Adversarial Network and Multi-modal Attention Mechanism
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摘要: 针对低信噪比条件下的扩频与常规调制信号分类精度低的问题，该文提出一种基于生成式对抗网络(GAN)、卷积神经网络(CNN)和长短期记忆(LSTM)网络的多模态注意力机制信号调制识别方法。首先生成待识别信号的时频图像(TFIs)，并利用GAN实现TFIs降噪处理；然后将信号的同相正交数据(I/Q data)与TFIs作为模型输入，并搭建基于CNN的TFIs识别支路和基于LSTM的I/Q数据识别支路；最后，在模型中添加注意力机制，增强I/Q数据和TFIs中重要特征对分类结果的决定作用。实验结果表明，该文所提方法相较于单模态识别模型以及其它基线模型，整体分类精度有效提升2%～7%，并在低信噪比条件下具备更强的特征表达能力和鲁棒性。
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关键词:
- 深度学习 /
- 自动调制识别 /
- 生成对抗网络(GAN) /
- 多模态特征 /
- 时频分布
Abstract: Considering the low classification accuracy of spreading and conventional modulated signals under low signal-to-noise ratio conditions, a multimodal attention mechanism signal modulation recognition method based on Generative Adversarial Network (GAN) and Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) network is proposed. Firstly, the Time-Frequency Images (TFIs) of the to-be-recognized signals are generated and the noise reduction process of TFIs is realized by using GAN; Secondly, the In-phase and Quadrature data (I/Q data) of the signals with TFIs are used as model inputs, and the CNN-based TFIs recognition branch and the LSTM-based I/Q data recognition branch are built; Finally, an attentional mechanism is added to the model to enhance the role of important features in I/Q data and TFIs in the determination of classification results. The experimental results show that the proposed method effectively improves the overall classification accuracy by 2% to 7% compared with the unimodal recognition model and other baseline models, and possesses stronger feature expression capability and robustness under low signal-to-noise ratio conditions. -
表 1 数据集相关参数
参数 数值 码元速率(kHz) 2 采样率(kHz) 160 载波频率(kHz) 40 采样点数 960 信号持续时间(ms) 12 TFIs像素(RGB) (256,256)×3 噪声环境 AWGN(–10:2:8 dB) FHSS频点(kHz) 26, 30, 34, 38, 42, 46, 50, 54 
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表 2 网络相关参数
参数 数值 学习率 0.001 GAN网络中卷积核大小 4×4 CNN-Attention层中卷积核大小 7×7, 5×5, 3×3 LSTM-Attention层中卷积核大小 7×1, 5×1, 3×1 LSTM单元数量 128 丢弃率 0.5 批次大小 32 优化器 Adam
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