The deep learning methods powerfully enhance the identification performance by retrieving the deep data features in many fields, which can be used in automatic modulation classification (AMC) work for the better results in the acoustic underwater communication. A novel hybrid time series network structure is scheduled for AMC in this paper. It can accommodate the variable-length signal datas to match the fixed-length input request in the common neural network, and there is the ability to suitably deal with the zero data in the signal sequence to alleviate the effect losses. The proposed network has the mix of two time series network styles to enrich the extracted signal modulation classification features, and dramatically improves the recognition capability and owns the low computation complexity. In the meanwhile, the internal network structure is optimized by the well-designed cascading order, which acquires more hidden signal data representations to increase the accuracy. The simulation experiments shows that the proposed network is more effective and robust than the conventional deep learning methods to identify ten modulation modes in the serious interference communication environment.

深度学习方法通​​过检索许多领域中的深度数据特征，可以有力地提高识别性能，可将其用于自动调制分类（AMC）工作，从而在水下声波通信中获得更好的结果。本文为AMC计划了一种新颖的混合时间序列网络结构。它可以容纳可变长度的信号数据，以匹配公共神经网络中的固定长度的输入请求，并且具有适当处理信号序列中的零数据以减轻效果损失的能力。该网络融合了两种时间序列网络样式，丰富了提取的信号调制分类特征，极大地提高了识别能力，并且计算复杂度低。与此同时，内部网络结构通过精心设计的级联顺序进行了优化，该顺序可以获取更多隐藏信号数据表示形式，从而提高准确性。仿真实验表明，在严重的干扰通信环境中，该网络比常规的深度学习方法更有效，更健壮，可以识别十种调制模式。