Semantics-Guided Diffusion for Deep Joint Source-Channel Coding in Wireless Image Transmission
Joint source-channel coding (JSCC) offers a promising avenue for enhancing transmission efficiency by jointly incorporating source and channel statistics into the system design. A key advancement in this area is the deep joint source and channel coding (DeepJSCC) technique that designs a direct mapp...
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| Veröffentlicht in: | IEEE transactions on wireless communications S. 1 |
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| Sprache: | Englisch |
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2025
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| Abstract | Joint source-channel coding (JSCC) offers a promising avenue for enhancing transmission efficiency by jointly incorporating source and channel statistics into the system design. A key advancement in this area is the deep joint source and channel coding (DeepJSCC) technique that designs a direct mapping of input signals to channel symbols parameterized by a neural network, which can be trained for arbitrary channel models and semantic quality metrics. This paper advances the DeepJSCC framework toward a semantics-aligned, high-fidelity transmission approach, called semantics-guided diffusion DeepJSCC (SGD-JSCC). Existing schemes that integrate diffusion models (DMs) with JSCC face challenges in transforming random generation into accurate reconstruction and adapting to varying channel conditions. SGD-JSCC incorporates two key innovations: (1) utilizing some inherent information that contributes to the semantics of an image, such as text description or edge map, to guide the diffusion denoising process; and (2) enabling seamless adaptability to varying channel conditions with the help of a semantics-guided DM for channel denoising. The DM is guided by diverse semantic information and integrates seamlessly with DeepJSCC. In a slow fading channel, SGD-JSCC dynamically adapts to the instantaneous channel state information (CSI) directly estimated from the channel output, thereby eliminating the need for additional pilot transmissions for channel estimation. In a fast fading channel, we introduce a training-free denoising strategy, allowing SGD-JSCC to effectively adjust to fluctuations in channel gains. Numerical results demonstrate that, guided by semantic information and leveraging the powerful DM, our method outperforms existing DeepJSCC schemes, delivering satisfactory reconstruction performance even at extremely poor channel conditions. The proposed scheme highlights the potential of incorporating diffusion models in future communication systems. The code and pretrained checkpoints will be publicly available at https://github.com/MauroZMJ/SGDJSCC, allowing integration of this scheme with existing DeepJSCC models, without the need for retraining from scratch. |
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| AbstractList | Joint source-channel coding (JSCC) offers a promising avenue for enhancing transmission efficiency by jointly incorporating source and channel statistics into the system design. A key advancement in this area is the deep joint source and channel coding (DeepJSCC) technique that designs a direct mapping of input signals to channel symbols parameterized by a neural network, which can be trained for arbitrary channel models and semantic quality metrics. This paper advances the DeepJSCC framework toward a semantics-aligned, high-fidelity transmission approach, called semantics-guided diffusion DeepJSCC (SGD-JSCC). Existing schemes that integrate diffusion models (DMs) with JSCC face challenges in transforming random generation into accurate reconstruction and adapting to varying channel conditions. SGD-JSCC incorporates two key innovations: (1) utilizing some inherent information that contributes to the semantics of an image, such as text description or edge map, to guide the diffusion denoising process; and (2) enabling seamless adaptability to varying channel conditions with the help of a semantics-guided DM for channel denoising. The DM is guided by diverse semantic information and integrates seamlessly with DeepJSCC. In a slow fading channel, SGD-JSCC dynamically adapts to the instantaneous channel state information (CSI) directly estimated from the channel output, thereby eliminating the need for additional pilot transmissions for channel estimation. In a fast fading channel, we introduce a training-free denoising strategy, allowing SGD-JSCC to effectively adjust to fluctuations in channel gains. Numerical results demonstrate that, guided by semantic information and leveraging the powerful DM, our method outperforms existing DeepJSCC schemes, delivering satisfactory reconstruction performance even at extremely poor channel conditions. The proposed scheme highlights the potential of incorporating diffusion models in future communication systems. The code and pretrained checkpoints will be publicly available at https://github.com/MauroZMJ/SGDJSCC, allowing integration of this scheme with existing DeepJSCC models, without the need for retraining from scratch. |
| Author | Jin, Richeng Zhu, Guangxu Gunduz, Deniz Zhang, Maojun Wu, Haotian Chen, Xiaoming |
| Author_xml | – sequence: 1 givenname: Maojun orcidid: 0009-0005-7649-9260 surname: Zhang fullname: Zhang, Maojun email: zhmj@zju.edu.cn organization: College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China – sequence: 2 givenname: Haotian orcidid: 0000-0003-2137-6907 surname: Wu fullname: Wu, Haotian email: haotian.wu17@imperial.ac.uk organization: Department of Electrical and Electronic Engineering, Imperial College London, United Kingdom – sequence: 3 givenname: Guangxu orcidid: 0000-0001-9532-9201 surname: Zhu fullname: Zhu, Guangxu email: gxzhu@sribd.cn organization: Shenzhen Research Institute of Big Data, Shenzhen, China – sequence: 4 givenname: Richeng orcidid: 0000-0002-1480-585X surname: Jin fullname: Jin, Richeng email: richengjin@zju.edu.cn organization: Department of Information and Communication Engineering, Zhejiang University, China – sequence: 5 givenname: Xiaoming orcidid: 0000-0002-1818-2135 surname: Chen fullname: Chen, Xiaoming email: chen_xiaoming@zju.edu.cn organization: College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China – sequence: 6 givenname: Deniz orcidid: 0000-0002-7725-395X surname: Gunduz fullname: Gunduz, Deniz email: d.gunduz@imperial.ac.uk organization: Department of Electrical and Electronic Engineering, Imperial College London, United Kingdom |
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| SubjectTerms | Accuracy Channel estimation Data models Fading channels Image coding Image reconstruction joint source-channel coding Noise reduction Receivers Semantics semantics-guided diffusion models Wireless communication wireless image transmission |
| Title | Semantics-Guided Diffusion for Deep Joint Source-Channel Coding in Wireless Image Transmission |
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