Semantic communication has emerged as a promising technology for enhancing communication efficiency. However, most existing research emphasizes single-task reconstruction, neglecting model adaptability and generalization across multi-task systems. In this paper, we propose a novel generative semantic communication system that supports both image reconstruction and segmentation tasks. Our approach builds upon semantic knowledge bases (KBs) at both the transmitter and receiver, with each semantic KB comprising a source KB and a task KB. The source KB at the transmitter leverages a hierarchical Swin-Transformer, a generative AI scheme, to extract multi-level features from the input image. Concurrently, the counterpart source KB at the receiver utilizes hierarchical residual blocks to generate task-specific knowledge. Furthermore, the two task KBs adopt a semantic similarity model to map different task requirements into pre-defined task instructions, thereby facilitating the feature selection of the source KBs. Additionally, we develop a unified residual block-based joint source and channel (JSCC) encoder and two task-specific JSCC decoders to achieve the two image tasks. In particular, a generative diffusion model is adopted to construct the JSCC decoder for the image reconstruction task. Experimental results demonstrate that our multi-task generative semantic communication system outperforms previous single-task communication systems in terms of peak signal-to-noise ratio and segmentation accuracy.

翻译：语义通信作为一种提升通信效率的前沿技术已崭露头角。然而，现有研究大多聚焦于单任务重建，忽视了模型在多任务系统中的适应性与泛化能力。本文提出一种新型生成式语义通信系统，可同时支持图像重建与分割任务。该方法建立在收发双方的语义知识库基础上，每个语义知识库均由源知识库与任务知识库构成。发射端的源知识库采用分层Swin-Transformer这一生成式人工智能架构，从输入图像中提取多层次特征；与此同时，接收端的对应源知识库则利用分层残差块生成面向特定任务的知识。此外，两个任务知识库采用语义相似度模型，将不同任务需求映射至预定义的任务指令，从而促进源知识库的特征选择。我们还开发了基于统一残差块的联合信源信道编码器及两个任务专属的联合信源信道解码器，以实现双图像任务。特别地，图像重建任务的联合信源信道解码器采用生成式扩散模型构建。实验结果表明，本多任务生成式语义通信系统在峰值信噪比与分割精度方面均优于先前的单任务通信系统。