A Unified Hierarchical Semantic Knowledge Base for Multi-Task Semantic Communication
Lingyi Wang, Wei Wu, Fuhui Zhou, Feng Tian, Qihui Wu, Walid Saad
Abstract
Semantic communication is a promising approach to address the challenge of limited spectrum resources in the sixth-generation (6G) communication networks. However, prior works on semantic communication focus primarily on semantic coding, and they do not investigate how to efficiently construct a semantic knowledge base. In this paper, a codebook-based unified hierarchical semantic knowledge base (UH-SKB) framework is studied for multi-task semantic communications. To maximize semantic representation spaces and effectively explore the semantic relevance among multiple tasks, the semantic knowledge base is constructed jointly in both the horizontal and vertical directions. A deep K-subspace cluster method is proposed to facilitate semantic relevance extraction and semantic subspace construction for high-dimensional semantic information. Simulation results demonstrate that the proposed UH-SKB can support multi-task semantic communications efficiently, achieving up to 13.4%, 14% and 6.3% performance improvement respectively for reconstruction, segmentation and classification tasks compared to standalone semantic knowledge bases at the novel dataset when SNR is 0 dB. Moreover, the proposed UH-SKB exhibits 95.3% knowledge search efficiency improvement on the reconstruction task compared to standalone semantic knowledge bases.