A Theory for Semantic Communications

Ma, Siwei; Wu, Youlong; Qi, Huayan; Li, Hang; Shi, Guangming; Liang, Yong; Al-Dhahir, Naofal

doi:10.48550/arxiv.2303.05181

Cited by 3 publications

(3 citation statements)

References 27 publications

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“…, 2 ⌈αnR⌉ }, where the coefficient α falls within the range 0 ≤ α < 1. Thus, given the semantic mapping [1 : 2 nR ] → [1 : 2 αnR ] and the discrete memoryless channel p(y|x), Ma et al [56] define the semantic channel C s as follows:…”

Section: Semantic Capacitymentioning

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 Capacitymentioning

confidence: 99%

Semantic Communication: A Survey of Its Theoretical Development

Xin,

Fan,

Letaief

2024

Entropy

View full text Add to dashboard Cite

show abstract

“…, 2 ⌈αnR⌉ }, where the coefficient α falls within the range 0 ≤ α < 1. Thus, given the semantic mapping [1 : 2 nR ] → [1 : 2 αnR ] and the discrete memoryless channel p(y|x), Ma et al [56]…”

Section: Semantic Capacitymentioning

confidence: 99%

Semantic Information Theory: Recent Advances and Future Challenges

Xin,

Fan,

Letaief

2023

Preprint

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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 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.

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“…In recent years, the field of wireless communication has undergone a significant transformation due to the emergence of semantic communication principles, which are surpassing conventional wireless communication techniques. Unlike traditional methods that prioritize the accurate transmission of separate symbols or bits, semantic communication places its emphasis on effectively conveying the intended meaning and context from the source of information [10]- [12].…”

Section: Introductionmentioning

confidence: 99%

Semantics-Empowered UAV-assisted Wireless Communication System for Wildfire Detection

Basnayaka,

Jayakody,

Beko

2024

Preprint

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With the increasing occurrence of wildfires globally, quick and effective detection methods are vital. This paper proposes an innovative solution for wildfire detection using Unmanned Aerial Vehicle (UAV)-assisted detection systems. On the other hand, semantic communication, a technology designed for efficient data transmission in specialized tasks, plays a crucial role in next-generation wireless communications systems. In this paper, the deep joint source-channel coding (DJSCC) scheme has been used for efficient image transmission as a deep learning-based semantic communication technique for wildfire detection. DJSCC improves source and channel coding for semantic communications, offering advantages such as improved energy efficiency, reduced latency, and improved reliability compared to traditional source and channel code schemes. In this paper, the transmitter-receiver operations of the UAV communication system are modeled as a DJSCC, and they are jointly trained while taking into account the effects of the fading channel. The encoder transforms captured images into compact feature vectors, subsequently transmitting them using a reduced number of channels to minimize latency. Rather than engaging in the reconstruction of the input image in the receiver, the classifier performs a classification task using the received signals at the receiver. Alternatively, if the recovery of an image is required to understand the spread of the wildfire, the decoder reconstructs it by using the received signal at the receiver.

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A Theory for Semantic Communications

Cited by 3 publications

References 27 publications

Semantic Communication: A Survey of Its Theoretical Development

Semantic Communication: A Survey of Its Theoretical Development

Semantic Information Theory: Recent Advances and Future Challenges

Semantics-Empowered UAV-assisted Wireless Communication System for Wildfire Detection

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