Interference (communication)

Linguistics

Philosophy

Modulation (music)

Telecommunications

Computer science

Geology

Time delay neural network

Oceanography

Aesthetics

Pattern recognition (psychology)

Image (mathematics)

Data set

Feature (linguistics)

Artificial intelligence

Underwater

Deep learning

Channel (broadcasting)

Artificial neural network

Noise (video)

Feature extraction

Convolutional neural network

Recurrent neural network

Speech recognition

IEEE Transactions on Wireless Communications

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.

Modulation Recognition of Underwater Acoustic Signals Using Deep Hybrid Neural Networks