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 simultaneously satisfy two requirements: compatibility with existing digital communication infrastructures and robustness under varying channel conditions. Semantic-oriented modulation (SOM) provides a feasible way to map continuous semantic features into layered digital constellation symbols, thereby making semantic transmission compatible with conventional digital systems. However, the digitization process also introduces structured quantization distortion, which makes receiver-side recovery more difficult than in continuous semantic transmission. Although diffusion models have shown strong capability in channel-adaptive semantic recovery, directly applying them to SOM-based digital semantic communication is still limited by the structured distortion introduced by SOM. Therefore, this paper focuses on channel-adaptive receiver design for digital semantic communication and investigates how to compensate 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 composed of a Quantization Noise Predictor (QNP) and a diffusion recovery module. In the first stage, QNP estimates and compensates the structured quantization distortion introduced by SOM from the layer-wise soft received symbols. In the second stage, the compensated semantic representation is further refined by a diffusion denoiser, whose inference step number is adaptively selected according to the estimated signal-to-noise ratio (SNR). The QNP includes a shared feature extraction frontend, a classification branch exploiting discrete SOM constellation priors, and a regression branch performing fine-grained continuous distortion compensation. A Feature-wise Linear Modulation (FiLM) mechanism is used to incorporate SOM parameters and channel-state information, so that the same QNP can adapt to different modulation configurations and channel conditions. In addition, a composite loss with classification loss, regression loss, and distribution regularization is designed to improve the statistical properties of the compensated residual noise. Results and Discussions Experiments are conducted on the CLIC dataset using PSNR and MS-SSIM. First, the proposed method is compared with VAE, VAE+Diff, VAE+SOM, VAE+SOM+QNP, VAE+SOM+Diff, and JCM. The results show that direct SOM-based digitization causes noticeable performance degradation, while the proposed method consistently improves reconstruction quality over digital semantic baselines. In particular, QNP alone already provides stable gains over the SOM-only receiver, indicating that its effectiveness does not rely on diffusion recovery itself. Moreover, VAE+SOM+QNP achieves performance close to VAE+SOM+Diff while requiring much lower computational cost, and combining QNP with diffusion yields the best overall performance. Second, two training strategies, namely independent QNP training and diffusion-assisted fine-tuning, are compared. The results show that diffusion-assisted fine-tuning provides only limited additional gains but significantly increases training cost and complexity, so independent training offers a more practical balance. Third, experiments under different SOM configurations and different SNR conditions verify that QNP provides stable gains across different modulation orders and SOM layer settings. Latency analysis further shows that QNP introduces only a small fixed overhead, whereas the diffusion module dominates the total inference time; therefore, the adaptive diffusion-step schedule is selected according to the measured latency-PSNR trade-off. Fourth, Gaussianity analysis based on the Kullback-Leibler divergence and Wasserstein distance shows that QNP compensation significantly improves the Gaussianity of the residual noise, while the version with distribution regularization achieves the best statistical consistency. Conclusions This paper proposes a channel-adaptive receiver for digital semantic communication, in which QNP-based front-end compensation is combined with diffusion-based semantic recovery. The main contribution lies in introducing a lightweight and independently effective QNP module to compensate SOM-induced structured quantization distortion before subsequent recovery. Experimental results show that QNP alone can already stably improve digital semantic reconstruction under different SNR conditions and different SOM configurations, while its combination with diffusion recovery yields the best overall performance. Therefore, the proposed method provides an effective way to improve semantic reconstruction quality and channel adaptability while preserving 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)

References

[1]	SHI Guangming, XIAO Yong, LI Yingyu, et al. From semantic communication to semantic-aware networking: Model, architecture, and open problems[J]. IEEE Communications Magazine, 2021, 59(8): 44–50. doi: 10.1109/MCOM.001.2001239.
[2]	GÜLER B, YENER A, and SWAMI A. The semantic communication game[J]. IEEE Transactions on Cognitive Communications and Networking, 2018, 4(4): 787–802. doi: 10.1109/TCCN.2018.2872596.
[3]	LI Yihao, ZHOU Fuhui, YUAN Lu, et al. Cognitive semantic communication: A new communication paradigm for 6G[J]. IEEE Communications Magazine, 2025, 63(6): 122–129. doi: 10.1109/MCOM.005.2400366.
[4]	SAGDUYU Y E, ERPEK T, YENER A, et al. Will 6G be semantic communications? Opportunities and challenges from task oriented and secure communications to integrated sensing[J]. IEEE Network, 2024, 38(6): 72–80. doi: 10.1109/MNET.2024.3425152.
[5]	张祖凡, 尹星然, 周建萍, 等. 面向语义重要性和鲁棒性的RIS增强语义通信系统[J]. 电子与信息学报, 2025, 47(8): 2608–2620. doi: 10.11999/JEIT250159. ZHANG Zufan, YIN Xingran, ZHOU Jianping, et al. RIS-enhanced semantic communication systems oriented towards se-mantic importance and robustness[J]. Journal of Electronics & Information Technology, 2025, 47(8): 2608–2620. doi: 10.11999/JEIT250159.
[6]	陈阳, 马欢, 姬智, 等. 面向图像恢复任务的语义通信网络能耗优化[J]. 电子与信息学报, 2026, 48(1): 183–190. doi: 10.11999/JEIT250915. CHEN Yang, MA Huan, JI Zhi, et al. Optimization of energy consumption in semantic communication networks for image recovery tasks[J]. Journal of Electronics & Information Technology, 2026, 48(1): 183–190. doi: 10.11999/JEIT250915.
[7]	申滨, 李旋, 赖雪冰, 等. 基于Swin Transformer的宽带无线图传语义联合编解码方法[J]. 电子与信息学报, 2025, 47(8): 2665–2674. doi: 10.11999/JEIT250039. SHEN Bin, LI Xuan, LAI Xuebing, et al. Swin Transformer-based wideband wireless image transmission semantic joint encoding and decoding method[J]. Journal of Electronics & Information Technology, 2025, 47(8): 2665–2674. doi: 10.11999/JEIT250039.
[8]	BOURTSOULATZE E, KURKA D B, and GÜNDÜZ D. Deep joint source-channel coding for wireless image transmission[J]. IEEE Transactions on Cognitive Communications and Networking, 2019, 5(3): 567–579. doi: 10.1109/TCCN.2019.2919300.
[9]	KURKA D B and GÜNDÜZ D. DeepJSCC-f: Deep joint source-channel coding of images with feedback[J]. IEEE Journal on Selected Areas in Information Theory, 2020, 1(1): 178–193. doi: 10.1109/JSAIT.2020.2987203.
[10]	BO Yufei, DUAN Yiheng, SHAO Shuo, et al. Joint coding-modulation for digital semantic communications via variational autoencoder[J]. IEEE Transactions on Communications, 2024, 72(9): 5626–5640. doi: 10.1109/TCOMM.2024.3386577.
[11]	GUO Lei, CHEN Wei, SUN Yuxuan, et al. Diffusion-driven semantic communication for generative models with bandwidth constraints[J]. IEEE Transactions on Wireless Communications, 2025, 24(8): 6490–6503. doi: 10.1109/TWC.2025.3553851.
[12]	SONG Mengshu, MA Nan, DONG Chen, et al. Deep joint source-channel coding for wireless image transmission with adaptive models[J]. Electronics, 2023, 12(22): 4637. doi: 10.3390/electronics12224637.
[13]	CHEN Weixuan, CHEN Yuhao, YANG Qianqian, et al. Deep joint source-channel coding for wireless image transmission with entropy-aware adaptive rate control[C]. GLOBECOM 2023-2023 IEEE Global Communications Conference, Kuala Lumpur, Malaysia, 2023: 2239–2244. doi: 10.1109/GLOBECOM54140.2023.10437482.
[14]	WANG Yangfan, ZHANG Xuefei, SUN Yao, et al. Semantic-oriented modulation for wireless communication[J]. IEEE Internet of Things Journal, 2025, 12(14): 28259–28273. doi: 10.1109/JIOT.2025.3565017.
[15]	宋淼, 陈志强, 王培松, 等. DetDiffRS: 面向细节优化的遥感图像超分辨率扩散模型[J]. 电子与信息学报, 2025, 47(12): 4763–4778. doi: 10.11999/JEIT250995. SONG Miao, CHEN Zhiqiang, WANG Peisong, et al. DetDiffRS: A detail-enhanced diffusion model for remote sensing image super-resolution[J]. Journal of Electronics & Information Technology, 2025, 47(12): 4763–4778. doi: 10.11999/JEIT250995.
[16]	XU Bingxuan, MENG Rui, CHEN Yue, et al. Latent semantic diffusion-based channel adaptive de-noising SemCom for future 6G systems[C]. GLOBECOM 2023-2023 IEEE Global Communications Conference, Kuala Lumpur, Malaysia, 2023: 1229–1234. doi: 10.1109/GLOBECOM54140.2023.10437849.
[17]	WU Tong, CHEN Zhiyong, HE Dazhi, et al. CDDM: Channel denoising diffusion models for wireless semantic communications[J]. IEEE Transactions on Wireless Communications, 2024, 23(9): 11168–11183. doi: 10.1109/TWC.2024.3379244.
[18]	HO J, JAIN A, and ABBEEL P. Denoising diffusion probabilistic models[C]. Proceedings of the 34th International Conference on Neural Information Processing Systems, Vancouver, Canada, 2020: 574.
[19]	ROMBACH R, BLATTMANN A, LORENZ D, et al. High-resolution image synthesis with latent diffusion models[C]. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, USA, 2022: 10674–10685. doi: 10.1109/CVPR52688.2022.01042.
[20]	PEREZ E, STRUB F, DE VRIES H, et al. FiLM: Visual reasoning with a general conditioning layer[C]. Proceedings of the 32nd AAAI Conference on Artificial Intelligence, New Orleans, USA, 2018: 3942–3951. doi: 10.1609/aaai.v32i1.11671.
[21]	QIAO Li, MASHHADI M B, GAO Zhen, et al. Latency-aware generative semantic communications with pre-trained diffusion models[J]. IEEE Wireless Communications Letters, 2024, 13(10): 2652–2656. doi: 10.1109/LWC.2024.3429295.