Implicit Semantic-Aware Communication Based on Hypergraph Reasoning

2026-06-18

AI Summary

Existing semantic communication approaches are largely confined to pairwise relation modeling, which fails to capture the high-order implicit associations among multiple entities in real-world scenarios, resulting in limited semantic expressiveness and susceptibility to noise. To address this limitation, this work proposes HISR, a hypergraph-based implicit semantic reasoning framework that introduces hypergraphs into semantic communication for the first time. By leveraging hypergraphs to model high-order relationships and mapping entities and relations into dedicated semantic subspaces, HISR effectively disentangles complex semantic interactions and mitigates the over-smoothing problem commonly encountered in graph embeddings. The framework enables robust reasoning even under partial information loss, achieving up to a 36.6% improvement in implicit semantic interpretation accuracy over state-of-the-art methods.

Abstract

Semantic-aware communication has emerged as a transformative paradigm for next-generation communication systems, shifting the fundamental goal from transmitting bit-level symbols to reliably recovering and understanding the semantic meaning of information. Previous studies have demonstrated that representing the semantic content of source messages as graph-based structures can significantly improve communication efficiency and the accuracy of semantic inference at the receiver. However, existing solutions typically employ graphs that capture only pairwise relationships, thereby neglecting higher-order implicit correlations commonly observed in real-world scenarios, such as group interactions, multi-entity associations, and complex relational contexts. This limitation reduces semantic expressiveness and makes semantic inference susceptible to ambiguity and performance degradation, particularly under noisy or corrupted channel conditions. To address these issues, this paper proposes a novel hypergraph-based implicit semantic reasoning framework, HISR, which leverages hypergraphs to represent complex multi-entity relationships among semantic knowledge entities. In HISR, entities and their associated higher-order relations are mapped into dedicated semantic subspaces tailored to distinct relational contexts. This design not only disentangles diverse semantic interactions to mitigate the over-smoothing effects commonly found in traditional graph embedding methods but also enables robust semantic inference even when partial information loss occurs during transmission. Numerical results show that the proposed HISR achieves up to a 36.6% improvement in implicit semantic interpretation accuracy over the state-of-the-art benchmarks.

Problem

• semantic-aware communication
• higher-order correlations
• hypergraph
• semantic ambiguity
• noisy channels

Innovation

• hypergraph reasoning
• semantic-aware communication
• higher-order relations
• semantic subspace
• implicit semantic inference