A Cumulant-Deep Learning Fusion Model for Underwater Modulation Recognition

doi:10.3969/j.issn.1671-1122.2025.10.007

Abstract

Abstract:

In complex and demanding underwater acoustic communication environments, modulation recognition technology is crucial for improving the anti-interception capabilities and information security of underwater communication systems. However, nonlinearity, multipath effects, and strong noise interference in underwater acoustic channels pose significant challenges to automatic modulation recognition (AMR). To address these challenges, this paper proposed a deep modulation recognition model (CRT) that integrates wavelet denoising and high-order cumulants. This model optimized the residual network (ResNet) and Transformer encoder (Trans-Encoder) architectures to model local and global temporal features, respectively. Furthermore, it integrated high-order cumulants based on the time-frequency distribution of underwater acoustic signals. This model achieves an average recognition accuracy of 93.56% for nine typical underwater modulation modes, a 2.4% improvement over the current best model. In particular, in low signal-to-noise ratio (SNR) environments of -10 dB to -2 dB, the recognition accuracy improves by over 10%, demonstrating the effectiveness and practical value of the CRT model in complex underwater acoustic scenarios.

Key words: automatic modulation recognition, underwater acoustics, wavelet transform, higher-order cumulants, deep learning

CLC Number:

TP309

LI Guyue, ZHANG Zihao, MAO Chenghai, LYU Rui. A Cumulant-Deep Learning Fusion Model for Underwater Modulation Recognition[J]. Netinfo Security, 2025, 25(10): 1554-1569.

Figures/Tables 13

References 36

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参数	参数值
实验环境	峡湾
实验时间	6个月
水深	10 m
多普勒频率覆盖范围	7.8 kHz
发射与接收距离	750 m

模型	准确率	模型参数量	训练时间/h
CRT	93.56%	503000	0.66
CNN^[19]	60.51%	2803000	0.59
LSTM^[27]	66.67%	201000	0.26
MCLDNN^[33]	69.00%	406000	2.70
CCNN&Atten^[34]	76.23%	481000	1.61
DHN^[35]	87.06%	537000	1.23
TSTR^[36]	91.12%	346000	1.54

模块	参数量	密集区域	优化后的参数量	优化后的计算量占比（FLOPs）
ResNet主干网络	544000	深层卷积层	384000	68.2%
Transformer编码器	376000	自注意力投影矩阵	94000	28.5%
分类头（FC层）	13000	全连接层	13000	3.3%