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Semantic Communication: A Survey of Its Theoretical Development
Gangtao Xin1,2, Pingyi Fan1,2, Khaled B Letaief3
1Department of Electronic Engineering, Tsinghua University, Beijing 100084, China.
Semantic information theory advances understanding of semantic communication, crucial for intelligent services. This research explores semantic entropy, rate-distortion, and capacity, addressing open challenges in semantic measurement and coding.
Area of Science:
- Information Theory
- Communication Systems
- Artificial Intelligence
Background:
- Semantic communication is vital for intelligent services demanding low latency and high throughput.
- A robust theoretical framework for semantic communication remains underdeveloped.
- Key unresolved issues include fundamental limits, semantic-aware network capabilities, and deep learning guidance.
Purpose of the Study:
- To introduce advancements in semantic information theory.
- To present developments in semantic entropy, rate-distortion, and channel capacity.
- To analyze open problems in semantic information measurement and coding.
Main Methods:
- Grounded in Claude Shannon's foundational work.
- Reviewing and evaluating applicable mathematical theories and tools.
- Analyzing open problems in semantic information measurement and coding.
Main Results:
- Latest developments in semantic entropy, semantic rate-distortion, and semantic channel capacity are presented.
- Open problems in semantic information measurement and semantic coding are analyzed.
- A theoretical basis for semantic communication system design is provided.
Conclusions:
- Semantic information theory is crucial for advancing semantic communication.
- Addressing open challenges is essential for practical system design.
- Further research is needed to overcome challenges in semantic communication and its theory.
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