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<p>The challenge of 6&#xa0;G image semantic communication lies in preserving semantic integrity through effective cross-modal representation. This paper proposes SwinJSCC-GAN, a deep joint source-channel coding framework that integrates the hierarchical feature learning of the swin transformer with the perceptual optimisation of generative adversarial network (GAN). This framework significantly improves semantic fidelity and anti-interference capabilities for image transmission over Rayleigh fading channels by constructing VGG-based and PatchGAN-based deep joint source-channel coding for multi-scale semantic feature extraction and combining dynamic modulation with hybrid loss functions. Experimental results demonstrate that the proposed scheme outperforms conventional methods in terms of PSNR, MS-SSIM, and LPIPS metrics, thus verifying its effectiveness in complex wireless environments.</p>

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SwinJSCC-GAN: enhanced joint source-channel coding for semantic communication with swin transformer and generative adversarial networks

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月6日

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  • Youming Shi,
  • Lisu Yu,
  • Qibiao Zhu

摘要

The challenge of 6 G image semantic communication lies in preserving semantic integrity through effective cross-modal representation. This paper proposes SwinJSCC-GAN, a deep joint source-channel coding framework that integrates the hierarchical feature learning of the swin transformer with the perceptual optimisation of generative adversarial network (GAN). This framework significantly improves semantic fidelity and anti-interference capabilities for image transmission over Rayleigh fading channels by constructing VGG-based and PatchGAN-based deep joint source-channel coding for multi-scale semantic feature extraction and combining dynamic modulation with hybrid loss functions. Experimental results demonstrate that the proposed scheme outperforms conventional methods in terms of PSNR, MS-SSIM, and LPIPS metrics, thus verifying its effectiveness in complex wireless environments.