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Joint Detection and Communication over Type-Sensitive Networks.

Joni Shaska1, Urbashi Mitra1

  • 1Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089, USA.

Entropy (Basel, Switzerland)
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Summary

This study introduces a new framework for decentralized detection in large networks with coupled observations. It enables accurate inference even when agent behavior is interdependent, improving performance in complex systems.

Keywords:
heterogeneous networksinformation measureslarge-scale networksmethod of types

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Area of Science:

  • Information Theory
  • Network Science
  • Signal Processing

Background:

  • Decentralized inference is challenging with dependent observations.
  • Large-scale heterogeneous networks require advanced analytical frameworks.
  • Existing methods struggle with high degrees of coupling between agents.

Purpose of the Study:

  • To develop a novel framework for decentralized detection with coupled observations.
  • To analyze agent behavior influenced by network states and hypotheses.
  • To derive performance metrics for highly coupled decentralized systems.

Main Methods:

  • Formulation of a decentralized detection framework for coupled observations.
  • Application of the method of types and equicontinuity arguments.
  • Derivation of the error exponent for identical agents.

Main Results:

  • The derived error exponent for identical agents depends on a single empirical distribution.
  • The framework successfully extends to multi-class detection problems.
  • Numerical results demonstrate utility in state-dependent signaling and channels.

Conclusions:

  • The proposed framework effectively analyzes decentralized detection in highly coupled environments.
  • It provides a robust method for understanding agent interdependence in networks.
  • This work advances the study of decentralized inference in complex systems.