CDDM: Channel Denoising Diffusion Models for Wireless Semantic Communications

Diffusion models (DM) can gradually learn to remove noise, which have been widely used in artificial intelligence generated content (AIGC) in recent years. The property of DM for eliminating noise leads us to wonder whether DM can be applied to wireless communications to help the receiver mitigate t...

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Veröffentlicht in:	IEEE transactions on wireless communications Jg. 23; H. 9; S. 11168 - 11183
Hauptverfasser:	Wu, Tong, Chen, Zhiyong, He, Dazhi, Qian, Liang, Xu, Yin, Tao, Meixia, Zhang, Wenjun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.09.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Adaptation models Algorithms Artificial intelligence Channel estimation Channel noise Codes Communications systems Decoding Diffusion layers Diffusion models Error correcting codes Generative adversarial networks Image coding Image communication Image transmission joint source-channel coding Mean square errors Noise reduction Sampling semantic communications Semantics Wireless communication Wireless communications wireless image transmission
ISSN:	1536-1276, 1558-2248
Online-Zugang:	Volltext
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