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Extended Blahut-Arimoto Algorithm for Semantic Rate-Distortion Function.

Yuxin Han1,2, Yang Liu1,2, Yaping Sun2,3

  • 1Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing 100876, China.

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|June 26, 2025
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Summary
This summary is machine-generated.

This study introduces the extended Blahut-Arimoto (EBA) algorithm for semantic communication, enhancing efficiency by focusing on meaning. The EBA algorithm calculates the semantic rate-distortion function, outperforming classical methods.

Keywords:
Blahut–Arimoto algorithmsemantic information theorysemantic knowledge basesemantic rate-distortion function

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Area of Science:

  • Information Theory
  • Communication Systems
  • Artificial Intelligence

Background:

  • Semantic communication offers improved efficiency over classical methods by prioritizing meaning.
  • The Blahut-Arimoto (BA) algorithm is a cornerstone for calculating rate-distortion functions in classical information theory.
  • Extending classical information theory to semantic communication requires new analytical tools.

Purpose of the Study:

  • To propose the extended Blahut-Arimoto (EBA) algorithm for calculating the semantic rate-distortion function.
  • To develop an optimization framework for scenarios with unknown synonymous mappings.
  • To provide a theoretical basis for analyzing semantic knowledge base (SKB) size and its impact on communication.

Main Methods:

  • The extended Blahut-Arimoto (EBA) algorithm iteratively updates distributions for semantic rate-distortion calculation.
  • An optimization framework combining EBA with simulated annealing addresses unknown synonymous mappings.
  • The semantic knowledge base (SKB) is utilized as a specific instance of synonymous mapping.

Main Results:

  • The EBA algorithm effectively calculates the semantic rate-distortion function.
  • For Gaussian sources, the semantic rate-distortion function decreases with increased synonymous number, outperforming classical approaches.
  • Larger SKB sizes correlate with higher compression efficiency in semantic communication, as shown on the CUB dataset.

Conclusions:

  • The EBA algorithm is a validated and effective tool for semantic communication analysis.
  • Semantic communication, particularly with larger SKBs, offers significant advantages in compression efficiency.
  • This work provides a theoretical foundation for understanding and optimizing semantic communication systems.