DenoMAE: A Multimodal Autoencoder for Denoising Modulation Signals

We propose Denoising Masked Autoencoder (DenoMAE), 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-mo...

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