Modulation Recognition of Underwater Acoustic Signals Using Deep Hybrid Neural Networks
Weilong Zhang, Xinghai Yang, Changli Leng, Jingjing Wang, Shiwen Mao
Abstract
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.