Design of a Channel-Adaptive Denoiser for Digital Semantic Communications

WU Yanjun; LIU Zhangyuhang; YANG Wenxin; YAN Mubiao; ZHOU Hao; ZHAO Yajun; XIE Zhuochen; LIANG Xuwen

doi:10.11999/JEIT260523

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WU Yanjun, LIU Zhangyuhang, YANG Wenxin, YAN Mubiao, ZHOU Hao, ZHAO Yajun, XIE Zhuochen, LIANG Xuwen. Design of a Channel-Adaptive Denoiser for Digital Semantic Communications[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260523

Citation:

WU Yanjun, LIU Zhangyuhang, YANG Wenxin, YAN Mubiao, ZHOU Hao, ZHAO Yajun, XIE Zhuochen, LIANG Xuwen. Design of a Channel-Adaptive Denoiser for Digital Semantic Communications[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260523

Citation:

WU Yanjun, LIU Zhangyuhang, YANG Wenxin, YAN Mubiao, ZHOU Hao, ZHAO Yajun, XIE Zhuochen, LIANG Xuwen. Design of a Channel-Adaptive Denoiser for Digital Semantic Communications[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260523

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Design of a Channel-Adaptive Denoiser for Digital Semantic Communications

doi: 10.11999/JEIT260523 cstr: 32379.14.JEIT260523

WU Yanjun^{1, 2, 3},
LIU Zhangyuhang^{1, 2, 3},
YANG Wenxin^{1, 2, 3},
YAN Mubiao^{1, 2, 3},
ZHOU Hao^{1, 2, 3},
ZHAO Yajun⁴,
XIE Zhuochen^{1, 3, 5
,
,},
LIANG Xuwen^{1, 2, 6}

1.
Innovation Academy for Microsatellites of Chinese Academy of Sciences, Shanghai 201304, China
2.
University of Chinese Academy of Sciences, Beijing 100049, China
3.
Key Lab for Satellite Digitalization Technology, Chinese Academy of Sciences, Shanghai 201210, China
4.
Zhongxing Telecommunication Equipment Corporation, Beijing 100029, China
5.
Shanghai Engineering Center for Microsatellites, Chinese Academy of Sciences, Shanghai 201210, China
6.
ShanghaiTech University, Shanghai 201210, China

Funds: The National Key Research and Development Program of China(2025YFF0522600)

Received Date: 2026-04-27
Accepted Date: 2026-08-10
Rev Recd Date: 2026-08-10

Available Online: 2026-08-20

Abstract

Abstract

Objective Practical semantic communication should satisfy two requirements: compatibility with existing digital communication infrastructures and robustness under varying channel conditions. Semantic-Oriented Modulation (SOM) maps continuous semantic features to layered digital constellation symbols, enabling semantic transmission over conventional digital systems. However, the digitization process introduces structured quantization distortion, which increases the difficulty of receiver-side recovery. Although diffusion models provide channel-adaptive semantic recovery, their direct application to SOM-based digital semantic communication is limited by this structured distortion. Therefore, a channel-adaptive receiver is designed for digital semantic communication to compensate for SOM-induced structured distortion before subsequent recovery. Methods An SOM-based digital semantic communication system for image transmission over an Additive White Gaussian Noise (AWGN) channel is considered. The proposed receiver adopts a two-stage structure comprising a Quantization Noise Predictor (QNP) and a diffusion recovery module. In the first stage, QNP estimates and compensates for the structured quantization distortion introduced by SOM from layer-wise soft received symbols. In the second stage, the compensated semantic representation is further refined by a diffusion denoiser, with the number of inference steps adaptively selected according to the estimated Signal-to-Noise Ratio (SNR). The QNP consists of a shared feature-extraction front end, a classification branch that exploits discrete SOM constellation priors, and a regression branch that performs fine-grained compensation for quantization distortion in the continuous domain. A Feature-wise Linear Modulation (FiLM) mechanism is used to incorporate SOM parameters and channel state information, enabling a single QNP to adapt to different modulation configurations and channel conditions. In addition, a composite loss comprising classification, regression, and distribution regularization terms is designed to improve the statistical properties of the compensated residual noise. Results and Discussions Experiments are conducted on the CLIC dataset using Peak Signal-to-Noise Ratio (PSNR) and Multi-scale structural similarity (MS-SSIM) as evaluation metrics. The proposed method is compared with a Variational autoencoder (VAE), VAE+Diff, VAE+SOM, VAE+SOM+QNP, VAE+SOM+Diff, and the Joint coding-modulation (JCM) method. Direct SOM-based digitization causes noticeable performance degradation, whereas the proposed method consistently improves image reconstruction quality over digital semantic baselines. QNP alone improves PSNR by an average of 1.77 dB and MS-SSIM by 26.08% compared with the VAE+SOM baseline under different channel conditions. Moreover, compared with VAE+SOM and VAE+SOM+Diff, the complete method improves PSNR by 3.67 dB and 1.89 dB and MS-SSIM by 58.79% and 18.64%, respectively. These results demonstrate that QNP provides effective compensation independently of diffusion recovery and that its combination with diffusion recovery provides complementary gains. Two training strategies, namely independent QNP training and diffusion-assisted fine-tuning, are further compared. Diffusion-assisted fine-tuning improves PSNR and MS-SSIM by only 0.83 dB and 3.30% on average, respectively, but substantially increases training complexity and resource consumption. Therefore, independent training is adopted for the main experiments. Under different SOM configurations, the proposed method maintains stable gains across different modulation orders and numbers of SOM layers. Latency analysis shows that QNP requires only 4.127 ms for one forward pass, whereas one diffusion step requires approximately 85.746 ms. Thus, inference latency is mainly determined by diffusion iterations, and the diffusion-step schedule is selected according to the trade-off between reconstruction quality and latency. Finally, Gaussianity analysis based on Kullback-Leibler divergence and Wasserstein distance shows that QNP compensation makes the residual noise distribution more consistent with a Gaussian distribution. Without distribution regularization, the two distances decrease by 97.20% and 89.26% on average, respectively, compared with the uncompensated case. With distribution regularization, the corresponding reductions reach 99.26% and 98.47%, respectively. Conclusions A channel-adaptive receiver for digital semantic communication is proposed by combining QNP-based front-end compensation with diffusion-based semantic recovery. The QNP provides lightweight compensation for SOM-induced structured quantization distortion before subsequent recovery and remains effective when used independently. Experimental results show that stable digital semantic recovery gains are obtained under different SNR conditions and SOM configurations. When combined with diffusion recovery, the proposed method achieves the best overall performance. The proposed receiver therefore provides an effective approach for improving semantic recovery quality and channel adaptability while maintaining compatibility with existing digital communication infrastructures.
- Digital semantic communication,
- Channel adaptation,
- Semantic-Oriented modulation,
- Quantization noise prediction,
- Diffusion models

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References(21)

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