One2ThreeNet: An Automatic Microscale-Based Modulation Recognition Method for Underwater Acoustic Communication Systems

Automatic modulation recognition (AMR) technology enables receivers to automatically recognize the modulation type of the received signal for correct demodulation of the received data, but there are still many shortcomings to be addressed. To achieve accurate and efficient AMR, this paper proposes a data augmentation method for AMR, which can increase the amount of data by seven times and solve the problem of a small sample size more effectively than the existing methods. In addition, this paper proposes a concept of microscale, rationalizes the underwater acoustic signal into time series, and proposes a temporal feature extractor named One2Three block, which can extract temporal features of signals from three microscales. Finally, a spatial feature extractor named the Dual-Stream squeeze-and-excitation (SE) block is designed to abstract and synthesize more advanced spatial features for AMR. The recognition accuracy of the proposed method is verified with eight commonly used modulation modes in underwater acoustic communications on the datasets collected in the South China Sea and the Yellow Sea. The results show that the proposed method can achieve a recognition accuracy of 99% with a lower time and space complexity, and has high robustness to noisy data.