{"ID":2887305,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.01719","arxiv_id":"2508.01719","title":"ModFus-DM: Explore the Representation in Modulated Signal Diffusion Generated Models","abstract":"Automatic modulation classification (AMC) is essential for wireless communication systems in both military and civilian applications. However, existing deep learning-based AMC methods often require large labeled signals and struggle with non-fixed signal lengths, distribution shifts, and limited labeled signals. To address these challenges, we propose a modulation-driven feature fusion via diffusion model (ModFus-DM), a novel unsupervised AMC framework that leverages the generative capacity of diffusion models for robust modulation representation learning. We design a modulated signal diffusion generation model (MSDGM) to implicitly capture structural and semantic information through a progressive denoising process. Additionally, we propose the diffusion-aware feature fusion (DAFFus) module, which adaptively aggregates multi-scale diffusion features to enhance discriminative representation. Extensive experiments on RML2016.10A, RML2016.10B, RML2018.01A and RML2022 datasets demonstrate that ModFus-DM significantly outperforms existing methods in various challenging scenarios, such as limited-label settings, distribution shifts, variable-length signal recognition and channel fading scenarios. Notably, ModFus-DM achieves over 88.27% accuracy in 24-type recognition tasks at SNR $\\geq $ 12dB with only 10 labeled signals per type.","short_abstract":"Automatic modulation classification (AMC) is essential for wireless communication systems in both military and civilian applications. However, existing deep learning-based AMC methods often require large labeled signals and struggle with non-fixed signal lengths, distribution shifts, and limited labeled signals. To add...","url_abs":"https://arxiv.org/abs/2508.01719","url_pdf":"https://arxiv.org/pdf/2508.01719v1","authors":"[\"Haoyue Tan\",\"Yu Li\",\"Zhenxi Zhang\",\"Xiaoran Shi\",\"Feng Zhou\"]","published":"2025-08-03T11:07:02Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[\"Diffusion Model\"]","has_code":false}