Semantic knowledge bases are regarded as a promising technology for upcoming 6G communications. However, existing studies mainly focus on source-side semantic modeling while overlooking the structural impact of propagation environments on semantic transmission performance. To address this issue, we propose a generative channel knowledge base (CKB) with environmental information to facilitate joint source-channel coding (JSCC) in semantic communications (SemCom) systems. First, to enable the construction of the CKB, an environment-aware dataset is established by collecting spatial position information, global image features, fine-grained semantic features, and the corresponding channel matrices. A region-of-interest (ROI)-based filtering algorithm is further designed to remove semantic components that are irrelevant to signal propagation. Second, a Transformer-based generative framework is developed to learn the mapping between multidimensional environmental information and channel matrices. A self-attention mechanism is introduced to adaptively fuse heterogeneous features, enabling the construction of a structured CKB. Third, a CKB-driven JSCC SemCom architecture is proposed, where the generated channel knowledge is injected into both of the encoder and decoder to jointly exploit source semantics and channel-environment priors in an end-to-end manner. Experimental results demonstrate that the proposed multidimensional feature fusion method achieves a channel matrix estimation error at the $10^{-3}$ level. Moreover, the CKB-driven JSCC SemCom framework integrated into SemCom systems significantly outperforms existing benchmark schemes in terms of transmission performance.

翻译：语义知识库被视为未来6G通信中极具前景的技术。然而，现有研究主要聚焦于信源端语义建模，忽视了传播环境结构对语义传输性能的影响。针对这一问题，本文提出了一种融合环境信息的生成式信道知识库（CKB），以促进语义通信（SemCom）系统中的联合信源信道编码（JSCC）。首先，为构建信道知识库，通过采集空间位置信息、全局图像特征、细粒度语义特征以及相应的信道矩阵，建立了环境感知数据集。进一步设计了基于感兴趣区域（ROI）的过滤算法，以移除与信号传播无关的语义成分。其次，开发了基于Transformer的生成式框架，学习多维环境信息与信道矩阵之间的映射关系，并引入自注意力机制自适应融合异构特征，从而构建结构化的信道知识库。第三，提出了CKB驱动的JSCC SemCom架构，将生成的信道知识注入编码器与解码器，以端到端方式联合利用信源语义与信道环境先验信息。实验结果表明，所提多维特征融合方法在信道矩阵估计误差上达到$10^{-3}$量级。此外，集成于SemCom系统的CKB驱动的JSCC SemCom框架在传输性能上显著优于现有基准方案。