Modulation Recognition of Underwater Acoustic Signals using Deep Hybrid Neural Networks
Modulation Recognition of Underwater Acoustic Signals using Deep Hybrid Neural Networks
复制标题
基于深度混合神经网络的水声信号调制识别
DOI：
10.1109/twc.2022.3144608
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发表时间：
2022
影响因子：
10.4
通讯作者：
Weilong Zhang;Xing-tong Yang;C. Leng;Jingjing Wang;S. Mao
中科院分区：
文献类型：
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作者：
Weilong Zhang;Xing-tong Yang;C. Leng;Jingjing Wang;S. Mao
由于水下信道环境复杂、噪声干扰严重，正确识别调制类型对接收机来说是一个巨大的挑战。此外，实时通信在时间方面有严格的要求。为了解决这个众所周知的问题，在这项工作中，我们联合收割机的自动特征提取和学习能力的递归神经网络（RNN）和卷积神经网络（CNN）设计的调制识别模型的水声信号。该模型基于深度混合神经网络，称为递归和卷积神经网络（R&CNN）。与传统的调制识别技术相比，该方法无需人工提取特征，识别精度更高。实验结果表明，所提出的R&CNN的栈桥数据集上的验证准确率为98.21%。同样，建议的R&CNN在南海数据集上的验证准确率为99.38%。平均识别时间为7.164ms。与传统的深度学习方法相比，提出的R&CNN不仅具有更高的识别精度，而且大大减少了识别时间。
It is a huge challenge for the receiver to correctly identify the modulation types due to the complex underwater channel environment and severe noise interference. Additionally, the real-time communications have strict requirements in terms of time. In order to solve this well-known issue, in this work, we combine the automatic feature extraction and learning ability of recurrent neural network (RNN) and convolutional neural network (CNN) for designing a modulation recognition model for underwater acoustic signals. The proposed model is based on deep hybrid neural networks called recurrent and convolutional neural network (R&CNN). As compared with the traditional modulation recognition techniques, this method achieves higher recognition accuracy without manual feature extraction. The experimental results show that the validation accuracy of the proposed R&CNN’s on the Trestle data set is 98.21%. Similarly, the validation accuracy of the proposed R&CNN’s on the South China Sea data set is 99.38%. The average recognition time is 7.164ms. As compared with the conventional deep learning methods, the proposed R&CNN not only has a higher recognition accuracy, but also greatly reduces the recognition time.