MCANet: A Coherent Multimodal Collaborative Attention Network for Advanced Modulation Recognition in Adverse Noisy Environments

TL;DR

MCANet is a novel multimodal deep learning framework that enhances automatic modulation recognition in noisy environments by employing collaborative attention and refined feature extraction, outperforming existing models especially at low SNR levels.

Contribution

This paper introduces MCANet, a new multimodal deep learning model with collaborative attention for robust modulation recognition in adverse noisy conditions.

Findings

Outperforms mainstream AMR models in benchmark tests

Demonstrates superior robustness at low SNR levels

Effective in complex, noisy wireless environments

Abstract

As wireless communication systems evolve, automatic modulation recognition (AMR) plays a key role in improving spectrum efficiency, especially in cognitive radio systems. Traditional AMR methods face challenges in complex, noisy environments, particularly in low signal-to-noise ratio (SNR) conditions. This paper introduces MCANet (Multimodal Collaborative Attention Network), a multimodal deep learning framework designed to address these challenges. MCANet employs refined feature extraction and global modeling to support its fusion strategy.Experimental results across multiple benchmark datasets show that MCANet outperforms mainstream AMR models, offering better robustness in low-SNR conditions.