A Modulation Recognition System for Underwater Acoustic Communication Signals Based on Higher-Order Cumulants and Deep Learning

• Published in: Journal of marine science and engineering Vol. 11; no. 8; p. 1632
• Main Authors: Zhang, Run, He, Chengbing, Jing, Lianyou, Zhou, Chaopeng, Long, Chao, Li, Jiachao
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2023
• Subjects: Accuracy; Acoustics; Algorithms; Classification; Communication; Decision theory; Deep learning; Feature selection; higher-order cumulants; Hypotheses; Hypothesis testing; Lakes; Machine learning; Methods; Modulation; modulation recognition; Neural networks; Phase shift keying; Recognition; Simulation; Simulation methods; Sound channels; Underwater; underwater acoustic communication; Underwater acoustics; Underwater communication; Wavelet transforms; China
• ISSN: 2077-1312; 2077-1312
• DOI: 10.3390/jmse11081632

Underwater acoustic channels, influenced by time-varying, space-varying, frequency-varying, and multipath effects, pose significant interference challenges to underwater acoustic communication (UWAC) signals, especially in non-cooperative scenarios. The task of modulating and identifying distorted signals faces huge challenges. Although traditional modulation recognition methods can be useful in the radio field, they often prove inadequate in underwater environments. This paper introduces a modulation recognition system for recognizing UWAC signals based on higher-order cumulants and deep learning. The system achieves blind recognition of received UWAC signals even under non-cooperative conditions. Higher-order cumulants are employed due to their excellent noise resistance, enabling the differentiation of OFDM signals from PSK and FSK signals. Additionally, the high-order spectra differences among signals are utilized for the intra-class recognition of PSK and FSK signals. Both simulation and lake test results substantiate the effectiveness of the proposed method.