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Open-set modulation recognition for underwater acoustic communication based on Multi-Path ResNet.

Renliang Zhang^1,2, Yonglin Zhang¹, Leixin Nie¹

¹State Key Laboratory of Acoustics and Marine Information, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China.

The Journal of the Acoustical Society of America

|May 14, 2026

Summary

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Parallel Resonance

Parallel Resonance

The parallel RLC circuit is an arrangement where the resistor (R), inductor (L), and capacitor (C) are all connected to the same nodes and, as a result, share the same voltage across them. The parallel RLC circuit is analyzed in terms of admittance (Y), which reflects the ease with which current can flow. The admittance is given by:

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This study introduces a novel Multi-Path ResNet for robust modulation recognition in challenging underwater acoustic communication. The method enhances accuracy and effectively rejects unknown signal types, improving system reliability.

Area of Science:

Electrical Engineering
Signal Processing
Machine Learning

Background:

Underwater acoustic communication faces significant challenges from channel distortion and non-cooperative environments.
Accurate modulation recognition is crucial for reliable data transmission in these conditions.

Purpose of the Study:

To develop an advanced method for modulation recognition in underwater acoustic communication.
To improve performance in non-cooperative scenarios and reject unknown modulation types.

Main Methods:

A Multi-Path ResNet architecture was proposed to extract and fuse time-domain, frequency-domain, and multi-order spectral features.
A per-class distance thresholding method was developed to reject unknown modulation types in non-cooperative settings.

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Main Results:

Simulations demonstrated superior performance in shallow water channels.
Deep-water sea trials achieved 64.1% recognition accuracy and 74.4% rejection accuracy for unknown categories.
Recognition performance was found to correlate with channel delay spread and dynamic variation.

Conclusions:

The proposed Multi-Path ResNet and distance thresholding method significantly enhance modulation recognition and unknown type rejection in underwater acoustic communication.
The findings provide physical insights into deep learning-based modulation recognition, correlating performance with channel characteristics.