EXK-SC: A Semantic Communication Model Based on Information Framework Expansion and Knowledge Collision

Xin, Gangtao; Fan, Pingyi

doi:10.3390/e24121842

Cited by 10 publications

(4 citation statements)

References 21 publications

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“…In the realm of human language, sentences are constructed from components such as subjects, predicates, objects, and attributive complements, enabling the expression of profound meanings that single words cannot convey. Xin and Fan [27,28] advocated for the extensibility of semantics, emphasizing that the representation of semantic entropy should encompass the notion of expansion. As semantics expand, knowledge often involves collisions.…”

Section: Semantic Entropymentioning

confidence: 99%

Semantic Communication: A Survey of Its Theoretical Development

Xin,

Fan,

Letaief

2024

Entropy

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In recent years, semantic communication has received significant attention from both academia and industry, driven by the growing demands for ultra-low latency and high-throughput capabilities in emerging intelligent services. Nonetheless, a comprehensive and effective theoretical framework for semantic communication has yet to be established. In particular, finding the fundamental limits of semantic communication, exploring the capabilities of semantic-aware networks, or utilizing theoretical guidance for deep learning in semantic communication are very important yet still unresolved issues. In general, the mathematical theory of semantic communication and the mathematical representation of semantics are referred to as semantic information theory. In this paper, we introduce the pertinent advancements in semantic information theory. Grounded in the foundational work of Claude Shannon, we present the latest developments in semantic entropy, semantic rate-distortion, and semantic channel capacity. Additionally, we analyze some open problems in semantic information measurement and semantic coding, providing a theoretical basis for the design of a semantic communication system. Furthermore, we carefully review several mathematical theories and tools and evaluate their applicability in the context of semantic communication. Finally, we shed light on the challenges encountered in both semantic communication and semantic information theory.

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Section: Semantic Entropymentioning

confidence: 99%

Semantic Communication: A Survey of Its Theoretical Development

Xin,

Fan,

Letaief

2024

Entropy

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“…Recently, with the great progress of artificial intelligence (AI), neural networks can extract semantic information, such as images, text, and speech, which makes semantic communications feasible [16]- [18]. However, the fundamental theory of semantic information theory has not been established, e.g, universal semantic entropy and semantic channel capacity are still open problems [19], [20]. Unlike Shannon entropy, which measures the amount of information based on probability, the existing measures of semantic information are based on logical probability [21]- [23], fuzzy mathematics theory [24], [25], language understanding model [26] or the complexity of query tasks [27].…”

Section: A Related Workmentioning

confidence: 99%

A Theory for Semantic Communications

Ma¹,

Wu²,

Qi³

et al. 2023

Preprint

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Semantic communications, as one of the potential key technologies of the sixth generation communications (6G), has attracted research interest from both academia and industry. However, semantic communication is still in its infancy and it faces many challenges, such as semantic information definition and semantic communication measurement. To address these challenges, we investigate unified semantic information measures and semantic channel coding theorem. Specifically, to address the shortcoming of existing semantic entropy definitions can only be applied to specific tasks, we propose a universal semantic entropy definition as the uncertainty in the semantic interpretation of random variable symbols in the context of knowledge bases. The proposed universal semantic entropy not only depends on the probability distribution, but also depends on the specific value of the symbol and the background knowledge base. Under the given conditions, the proposed universal semantic entropy definition can degenerate into the existing semantic entropy and Shannon entropy definitions. Moreover, since the accurate transmission of semantic symbols in the semantic communication system can allow a nonzero bit error rate, we conjecture that the bit rate of the semantic communication may exceed the Shannon channel capacity. Furthermore, we propose a semantic channel coding theorem, and prove its achievability and converse. Since the well-known Fano's inequality cannot be directly applied to semantic communications, we derive and prove the semantic Fano's inequality, and use it to prove the converse. To our best knowledge, this is the first theoretical proof that the transmission rate of semantic communication can exceed the Shannon channel capacity, which provides a theoretical basis for semantic communication research.

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“…In the realm of human language, sentences are constructed from components such as subjects, predicates, objects, and attributive complements, enabling the expression of profound meanings that single words cannot convey. Xin and Fan [27] advocated for the extensibility of semantics, emphasizing that the representation of semantic entropy should encompass the notion of expansion. As semantics expand, knowledge often collisions.…”

Section: Semantic Entropymentioning

confidence: 99%

Semantic Information Theory: Recent Advances and Future Challenges

Xin,

Fan,

Letaief

2023

Preprint

View full text Add to dashboard Cite

In recent years, semantic communication has received significant attention from both academia and industry, driven by the growing demands for ultra-low latency and high-throughput capabilities in emerging intelligent services. Nonetheless, a comprehensive and effective theoretical framework for semantic communication has yet to be established. In particular, finding the fundamental limits of semantic communication, exploring the capabilities of semantic-aware networks, or utilizing theoretical guidance for deep learning in semantic communication are very important but yet still unresolved issues. In this paper, we delve into the pertinent advancements in semantic information theory. Grounded in the foundational work of Claude Shannon, we present the latest developments in semantic entropy, semantic rate distortion, and semantic channel capacity. Additionally, we will analyze some open problems in semantic information measurement and semantic coding, providing a theoretical basis for the design of a semantic communication system. Furthermore, we carefully review several mathematical theories and tools and evaluate their applicability in the context of semantic communication. Finally, we shed light on the challenges encountered in both semantic communication and semantic information theory.

show abstract

EXK-SC: A Semantic Communication Model Based on Information Framework Expansion and Knowledge Collision

Cited by 10 publications

References 21 publications

Semantic Communication: A Survey of Its Theoretical Development

Semantic Communication: A Survey of Its Theoretical Development

A Theory for Semantic Communications

Semantic Information Theory: Recent Advances and Future Challenges

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