Generative Channel Knowledge Base With Environmental Information for Joint Source-Channel Coding in Semantic Communications

2026-04-06

AI Summary

This work addresses a critical gap in existing semantic communication research, which overlooks the structural impact of the propagation environment on transmission performance and lacks a joint modeling mechanism for environment and channel dynamics. To bridge this gap, the paper introduces, for the first time, environmental structural information into semantic communication through a novel generative Channel Knowledge Base (CKB) framework. This framework constructs an environment-aware dataset encompassing spatial coordinates, visual features, fine-grained semantics, and corresponding channel matrices, and employs a region-of-interest (ROI) filtering algorithm to eliminate irrelevant semantic content. Leveraging a Transformer architecture with self-attention mechanisms, the model maps multidimensional heterogeneous features to channel matrices, which are then injected into the encoder–decoder to enable environment-aware end-to-end joint source-channel coding (JSCC). Experimental results demonstrate channel estimation errors on the order of 10⁻³ and show that the proposed method significantly outperforms existing benchmarks, substantially enhancing semantic transmission performance.

Abstract

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.

Problem

• semantic communications
• channel knowledge base
• environmental information
• joint source-channel coding
• propagation environment

Innovation

• Channel Knowledge Base
• Semantic Communications
• Joint Source-Channel Coding
• Environmental Information
• Transformer-based Generative Model