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Summary
This summary is machine-generated.

Gaussian sources and noise minimize data communication distortion over memoryless networks. This research shows Gaussian distributions are least compressible, enabling optimal achievable distortion tuples for correlated sources and additive noise channels.

Keywords:
Worst-case sourcejoint source-channel codingnetwork compressionworst-case noise

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Area of Science:

  • Information Theory
  • Network Communication
  • Signal Processing

Background:

  • Distributed communication systems involve transmitting data from multiple sources to destinations.
  • Quadratic distortion constraints are commonly used to measure the fidelity of reconstructed data.
  • Understanding the impact of source and noise distributions on communication efficiency is crucial.

Purpose of the Study:

  • To determine the least compressible distributed memoryless sources under given correlations.
  • To identify the noise processes that minimize achievable distortion over memoryless additive-noise networks.
  • To establish fundamental limits on data communication for correlated sources and networks.

Main Methods:

  • Investigated the compressibility of arbitrary memoryless sources over memoryless networks.
  • Analyzed the impact of different noise processes on communication over additive-noise networks.
  • Developed constructive schemes based on Gaussian source/noise properties for non-Gaussian cases with identical covariance.

Main Results:

  • Gaussian sources are the least compressible among all distributed memoryless sources with a given correlation.
  • Gaussian noise processes minimize the achievable distortion tuples for any memoryless source communicated over an additive-noise network.
  • Schemes designed for Gaussian problems can be adapted to achieve similar performance for non-Gaussian distributions sharing the same covariance.

Conclusions:

  • Gaussian distributions represent a fundamental limit in terms of compressibility for distributed data sources.
  • Gaussian noise is optimal for minimizing distortion in memoryless additive-noise communication channels.
  • The covariance matrix plays a critical role in determining achievable communication performance, even for non-Gaussian distributions.