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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Bibliographic Details
Published in:	IEEE communications letters Vol. 29; no. 7; pp. 1659 - 1663
Main Authors:	Faysal, Atik, Boushine, Taha, Rostami, Mohammad, Roshan, Reihaneh Gh, Wang, Huaxia, Muralidhar, Nikhil, Sahoo, Avimanyu, Yao, Yu-Dong
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.07.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Adaptation models Autoencoders Classification Constellation diagram constellation diagrams Decoding denoising Image reconstruction Learning Modulation modulation classification Multi-modality Noise reduction Signal to noise ratio Training Transformers vision transformer
ISSN:	1089-7798, 1558-2558
Online Access:	Get full text
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