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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Bibliographic Details
Published in:IEEE transactions on wireless communications Vol. 23; no. 9; pp. 11168 - 11183
Main Authors: Wu, Tong, Chen, Zhiyong, He, Dazhi, Qian, Liang, Xu, Yin, Tao, Meixia, Zhang, Wenjun
Format: Journal Article
Language:English
Published: New York IEEE 01.09.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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 Access:Get full text
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Summary: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 the channel noise. To address this, we propose channel denoising diffusion models (CDDM) for semantic communications over wireless channels in this paper. CDDM can be applied as a new physical layer module after the channel equalization to learn the distribution of the channel input signal, and then utilizes this learned knowledge to remove the channel noise. We derive corresponding training and sampling algorithms of CDDM according to the forward diffusion process specially designed to adapt the channel models. We also theoretically prove that the well-trained CDDM can effectively reduce the conditional entropy of the received signal under small sampling steps. Moreover, we apply CDDM to a semantic communications system based on joint source-channel coding (JSCC) for image transmission and design a three-stage training algorithm for combining them. Extensive experimental results demonstrate that CDDM can further reduce the mean square error (MSE) after minimum mean square error (MMSE) equalizer, and the joint CDDM and JSCC system achieves better performance than the JSCC system, the traditional JPEG2000 with low-density parity-check (LDPC) code approach and other benchmarks in diverse scenarios.
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ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2024.3379244