A GenAI-Driven Framework for Context-Aware Semantic Communication in Digital Twin Networks

Semantic communication (SemCom) is expected to play a key role in 6G and digital twin network (DTN) environments, where reliability depends on preserving meaning and user intent rather than reconstructing bit sequences. This paper presents a GenAI-driven, contextual-knowledge-enhanced SemCom framework that strengthens semantic recovery by integrating relational, intra-sentential, inter-sentential, and historical knowledge. At the transmitter, a context-attentive (CA) mechanism combines sentence-level semantic features with relational knowledge generated by a relation-guided knowledge base. At the receiver, a knowledge enhancement (KE) mechanism employs Transformer-XL to incorporate historical semantic context and utilizes a relational knowledge modeling module to refine semantic inference. Experimental results demonstrate that the proposed framework achieves robust semantic recovery across a wide range of signal-to-noise ratios (SNRs), with significant improvements in low-SNR conditions. Ablation studies further highlight the complementary roles of contextual knowledge at the transmitter and receiver. These results confirm the effectiveness of contextual knowledge in enhancing deep learning-based SemCom and underscore its potential for deployment within future DTN-enabled 6G architectures.