Semantic communication (SemCom) has recently emerged as a promising paradigm for next-generation wireless systems. Empowered by advanced artificial intelligence (AI) technologies, SemCom has achieved significant improvements in transmission quality and efficiency. However, existing SemCom systems either rely on training over large datasets and specific channel conditions or suffer from performance degradation under channel noise when operating in a training-free manner. To address these issues, we explore the use of generative diffusion models (GDMs) as training-free SemCom systems. Specifically, we design a semantic encoding and decoding method based on the inversion and sampling process of the denoising diffusion implicit model (DDIM), which introduces a two-stage forward diffusion process, split between the transmitter and receiver to enhance robustness against channel noise. Moreover, we optimize sampling steps to compensate for the increased noise level caused by channel noise. We also conduct a brief analysis to provide insights about this design. Simulations on the Kodak dataset validate that the proposed system outperforms the existing baseline SemCom systems across various metrics.

翻译：语义通信（SemCom）作为下一代无线系统的一种有前景的范式，近年来崭露头角。在先进人工智能（AI）技术的赋能下，SemCom在传输质量和效率方面取得了显著提升。然而，现有的SemCom系统要么依赖于在大型数据集和特定信道条件下的训练，要么在以免训练方式运行时，在信道噪声下会遭受性能下降。为了解决这些问题，我们探索了使用生成扩散模型（GDMs）作为免训练的SemCom系统。具体而言，我们设计了一种基于去噪扩散隐式模型（DDIM）的反转和采样过程的语义编码与解码方法，该方法引入了一个两阶段前向扩散过程，在发射端和接收端之间分割，以增强对信道噪声的鲁棒性。此外，我们优化了采样步骤，以补偿由信道噪声引起的噪声水平增加。我们还进行了简要分析，以提供对此设计的见解。在Kodak数据集上的仿真验证了所提出的系统在各种指标上均优于现有的基线SemCom系统。