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CAIC-Net: Robust Radio Modulation Classification via Unified Dynamic Cross-Attention and Cross-Signal-to-Noise Ratio

Teng Wu^1,2, Quan Zhu^1,2, Runze Mao^1,2

¹School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing 100811, China.

Sensors (Basel, Switzerland)

|February 13, 2026

Summary

This study introduces CAIC-Net, a novel network for robust automatic modulation classification (AMC). It excels in low signal-to-noise ratio (SNR) conditions by using dynamic cross-attention and cross-SNR contrastive learning.

Keywords:

automatic modulation classification deep learning techniques dynamic cross-attention feature-based extraction

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Area of Science:

Wireless Communications
Signal Processing
Machine Learning

Background:

Automatic modulation classification (AMC) is crucial for wireless systems.
Existing AMC methods struggle with low signal-to-noise ratio (SNR) and feature integration.
Multi-scale feature representation is often inefficiently integrated.

Purpose of the Study:

To propose CAIC-Net, a robust network for AMC.
To enhance robustness in low-SNR environments.
To improve the efficiency of multi-scale feature representation integration.

Main Methods:

CAIC-Net utilizes a dual-stream feature extractor (ConvLSTM2D and Transformer).
A Dynamic Cross-Attention Unit (CAU) with SNR-awareness enables adaptive fusion.
A Cross-SNR Contrastive Learning (CSCL) module is employed for noise-invariant representation.

Main Results:

CAIC-Net achieves competitive performance at moderate-to-high SNRs.
The network demonstrates significant advantages in extremely low-SNR scenarios.
Experiments validate the proposed approach's effectiveness and generalization.

Conclusions:

CAIC-Net effectively addresses AMC challenges in complex wireless environments.
The integration of dynamic cross-attention and cross-SNR contrastive learning enhances robustness.
The proposed method offers a promising solution for reliable modulation classification under adverse conditions.