Wireless Signal Modulation Classification
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LM-GDMAF is proposed, which integrates lightweight Mamba modules with a GDMAF fusion module, which efficiently extract temporal features, while GDMAF deeply fuses dual-modality information from IQ time series and frequency-domain spectrograms.
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Automatic modulation recognition (AMR) is a key technique in modern communications. We propose LM-GDMAF, which integrates lightweight Mamba modules with a GDMAF fusion module. The lightweight Mamba modules efficiently extract temporal features, while GDMAF deeply fuses dual-modality information from IQ time series and frequency-domain spectrograms. On a public dataset, the proposed method improves average recognition accuracy by at least 4.25% compared to baseline methods. The number of computational parameters of the lightweight Mamba module is reduced by approximately 26.1% compared to the standard Mamba model. The complete model achieves average modulation recognition accuracy gains of approximately 11.89% and 14.74% over the respective single-branch models.
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@article{Zhang2026GDMAF,
title = {LM-GDMAF: A Lightweight Mamba Multimodal Fusion Algorithm for Small-Sample Modulation Recognition},
author = {Zhaoguang Zhang and Zhenhua Wei and Siming Han and Yuxin Yang and Jianwei Zhan and Wenpeng Wu and Haiyang You and Chenxi Li},
journal = {Algorithms},
year = {2026},
doi = {10.3390/a19070532},
url = {https://doi.org/10.3390/a19070532}
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