用多模态自编码器高效去噪调制信号，少样本也能准分类。
DenoMAE: A Multimodal Autoencoder for Denoising Modulation Signals
- 输入噪声和星座图，让模型跨模态学习去噪机制。
- 仅需10%更少的无标签数据和3%更少的有标签数据即达顶尖性能。
- 在低信噪比下仍表现稳健，适合复杂噪声环境中的信号处理。
我们提出去噪掩码自编码器（DenoMAE），一种用于预训练阶段去噪调制信号的新型多模态自编码框架。DenoMAE通过引入噪声作为显式模态，扩展了掩码自编码器的概念，增强跨模态学习能力，提升去噪效果。模型使用无标签的含噪调制信号与星座图进行预训练，有效学习重建其对应的无噪信号与星座图。在自动调制分类任务中，DenoMAE实现当前最优精度，相比现有方法减少10%的无标签预训练数据和3%的有标签微调数据。此外，模型在不同信噪比（SNRs）下均表现鲁棒，并能对未见的低信噪比情况实现外推。结果表明，DenoMAE是一种高效、灵活且数据高效的去噪与调制信号分类解决方案，适用于高噪声挑战环境。
原文摘要 · Abstract (English)
We propose Denoising Masked Autoencoder (Deno-MAE), a novel multimodal autoencoder framework for denoising modulation signals during pretraining. DenoMAE extends the concept of masked autoencoders by incorporating multiple input modalities, including noise as an explicit modality, to enhance cross-modal learning and improve denoising performance. The network is pre-trained using unlabeled noisy modulation signals and constellation diagrams, effectively learning to reconstruct their equivalent noiseless signals and diagrams. Deno-MAE achieves state-of-the-art accuracy in automatic modulation classification tasks with significantly fewer training samples, demonstrating a 10% reduction in unlabeled pretraining data and a 3% reduction in labeled fine-tuning data compared to existing approaches. Moreover, our model exhibits robust performance across varying signal-to-noise ratios (SNRs) and supports extrapolation on unseen lower SNRs. The results indicate that DenoMAE is an efficient, flexible, and data-efficient solution for denoising and classifying modulation signals in challenging noise-intensive environments.
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