Journal Article

Dual-branch CNN with GRU for underwater acoustic modulation recognition under non-gaussian noise

Chang, Pengxiang, Song, Ruiping, Sun, Haixin, Zhang, Ailing, Qian, Zhiwen, Wang, Junfeng, Wang, Longxu, Zhou, Mingzhang, Cui, Yue

2026

Overview

Automatic Modulation Recognition (AMR) in Underwater acoustic (UWA) channels is challenging due to severe noise and strong multipath propagation. The recognition of signals becomes particularly difficult when orthogonal frequency division multiplexing coexists with several recently presented types including orthogonal time frequency space, orthogonal chirp division multiplexing, and orthogonal time sequency multiplexing, because their characteristics are similar. To address these challenges, this study considers the WATERMARK UWA channel under non-Gaussian noise and proposes a lightweight model for AMR, capable of recognizing both conventional and emerging modulation types. The proposed model reorganizes each one-dimensional signal into a matrix, where each row represents a short-time segment of the signal. Then, it captures both local features within segments and dependencies across segments through parallel convolutional branches, each followed by gated recurrent units to integrate information, compressed via a 1×1 convolution, fused by a gated fusion unit, and finally fed into two fully connected layers for modulation recognition. Simulations show that compared with benchmark models, the proposed model achieves better recognition performance while maintaining low computational cost.

• Publisher: IOP Publishing
• Subject: Automatic modulation recognition / Noise propagation / Orthogonal Frequency Division Multiplexing / Random noise / Segments / Underwater acoustics