Semantic communication (SemCom) shifts the focus from bit-level accuracy to the preservation of meaning, enabling more efficient and robust transmission. To achieve a high utilization of the wireless channel in SemCom, in this paper, we propose a channel assignment approach for polar code-based SemCom that allocates polarized channels according to semantic importance. Specifically, by combining eye-tracking data with semantic segmentation, we define two metrics that capture the contribution and correlation of semantic entities within an image. Leveraging these semantic metrics and polarized channel reliabilities, we formulate a constrained 0-1 optimization problem for polarized channel assignment and develop a priority-based algorithm that dynamically prioritizes semantically important content. Simulation results demonstrate that our method significantly outperforms the traditional channel allocation policy, especially under harsh channel conditions, by preserving critical visual information while reducing overall transmission redundancy.

Semantic communication (SemCom), a paradigm that emphasizes conveying the meaning of information, faces challenges in precise reasoning in semantic coding models. Knowledge graphs (KGs) offer a potential solution by providing structured triples (entities and relations), enabling inference via entity attributes and relational logic. Several key challenges exist in leveraging KGs within SemCom. The first challenge lies in developing methods to create semantic representations aligning and integrating source data and KG information. Second, reconstructing the original data using KGs becomes challenging particularly under poor communication conditions. Moreover, integrating KGs with source data inevitably increases the transmission overhead. In this paper, we propose a novel SemCom framework named KG-SemCom with sophisticated KG-based semantic encoding and decoding designs to solve these challenges. This framework aligns KG entities with message tokens, and then encodes messages into a semantic fusion of contextual and knowledge-based information. Furthermore, KG-SemCom can utilize the KG and contextual relationships to assist in predicting incomplete or distorted messages during the decoding process. Finally, simulation results demonstrate that KG-SemCom achieves higher accuracy and greater robustness compared to existing benchmarks without incorporating KGs, especially in challenging communication environments.