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Rate-Distortion Analysis of Distributed Indirect Source Coding.

Jiancheng Tang1, Qianqian Yang1

  • 1College of information Science and Electronic Engineering, Zhejiang University, Hangzhou 310007, China.

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|August 28, 2025
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Summary
This summary is machine-generated.

This study addresses distributed indirect source coding for semantic communication and learning systems. Researchers derived the rate-distortion function for conditionally independent sources and developed a Blahut-Arimoto algorithm for computation.

Keywords:
Blahut–Arimoto algorithmdistributed source codingrate-distortion theorysemantic communicationside information

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Area of Science:

  • Information Theory
  • Distributed Systems
  • Machine Learning

Background:

  • Task-oriented semantic communication and distributed learning systems require efficient data processing.
  • Distributed indirect source coding involves multiple sources encoded independently for a central decoder.

Purpose of the Study:

  • To study a distributed indirect source coding problem for recovering a latent variable under distortion constraints.
  • To characterize the rate-distortion function for conditionally independent sources given side information.

Main Methods:

  • Derivation of the exact rate-distortion function for a specific source dependency case.
  • Development of a distributed Blahut-Arimoto (BA) algorithm for numerical computation.
  • Utilizing correlated side information at the decoder.

Main Results:

  • The exact rate-distortion function is characterized for sources conditionally independent given side information.
  • A novel distributed Blahut-Arimoto algorithm is proposed and validated.
  • The algorithm effectively computes the rate-distortion region.

Conclusions:

  • The proposed framework provides a method for distributed indirect source coding.
  • The developed algorithm is effective for calculating rate-distortion functions in complex systems.
  • This work advances semantic communication and distributed learning capabilities.