The burgeoning generative artificial intelligence technology offers novel insights into the development of semantic communication (SemCom) frameworks. These frameworks hold the potential to address the challenges associated with the black-box nature inherent in existing end-to-end training manner for the existing SemCom framework, as well as deterioration of the user experience caused by the inevitable error floor in deep learning-based SemCom. In this paper, we focus on the widespread remote monitoring scenario, and propose a semantic change driven generative SemCom framework. Therein, the semantic encoder and semantic decoder can be optimized independently. Specifically, we develop a modular semantic encoder with value of information based semantic sampling function. In addition, we propose a conditional denoising diffusion probabilistic mode-assisted semantic decoder that relies on received semantic information from the source, namely, the semantic map, and the local static scene information to remotely regenerate scenes. Moreover, we demonstrate the effectiveness of the proposed semantic encoder and decoder as well as the considerable potential in reducing energy consumption through simulation based on the realistic $\mathcal{F}$ composite channel fading model. The code is available at https://github.com/wty2011jl/SCDGSC.git.

翻译：蓬勃发展的生成式人工智能技术为语义通信框架的发展提供了新思路。这些框架有望解决现有端到端训练方式中语义通信框架固有的黑箱特性挑战，以及基于深度学习的语义通信中不可避免的误差基底导致的用户体验恶化问题。本文针对广泛存在的远程监控场景，提出了一种语义变化驱动的生成式语义通信框架。在该框架中，语义编码器和语义解码器可独立优化。具体而言，我们开发了具有基于信息价值的语义采样功能的模块化语义编码器。此外，提出了一种条件去噪扩散概率模型辅助的语义解码器，该解码器依赖于从信源接收的语义信息（即语义图）和本地静态场景信息，远程重建场景。进一步地，通过基于实际复合信道衰落模型的仿真，证明了所提语义编码器和解码器的有效性以及在降低能耗方面的显著潜力。代码已开源在https://github.com/wty2011jl/SCDGSC.git。