Blind Modulation Recognition of UWA Signals with Semi-Supervised Learning

Blind modulation recognition is one of the most important component of the intelligent communication. Recent years, supervised learning is proved to be an effective way to solve this problem. But in time space varying underwater acoustic (UWA) channels, high quality labeled data is very difficult to obtain. To solve this problem, we put forward a semi-supervised learning-based blind modulation recognition scheme SSLUWA for UWA signals. This scheme linearly interpolates the unlabeled signal as fake label, and trains it by interpolation consistency. It can test the knowledge learned from the labeled signals by the accuracy of training on unlabeled signals, to improve the recognition accuracy when the labeled signals are insufficient. In order to verify the performance of SSLUMA, extensive pool and simulation experiments have been conducted. The results show that, compared with 100% label samples, the recognition accuracy of the SSLUWA decreases slightly at low SNR when the label samples are only 10%.