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A clustering technique for digital communications channel equalization using radial basis function networks.

S Chen1, B Mulgrew, P M Grant

  • 1Dept. of Electr. Eng., Edinburgh Univ.

IEEE Transactions on Neural Networks
|January 1, 1993
PubMed
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Radial basis function networks offer an identical structure to optimal Bayesian equalizers for digital communications. This approach enables efficient training and real-time tracking of time-varying channels, compensating for distortions.

Area of Science:

  • Digital Communications
  • Machine Learning
  • Signal Processing

Background:

  • Channel equalization is crucial for reliable digital communication.
  • Traditional methods face challenges with nonlinear distortions and time-varying channels.

Purpose of the Study:

  • To investigate the application of radial basis function networks for digital communications channel equalization.
  • To demonstrate the structural equivalence between radial basis function networks and optimal Bayesian equalizers.

Main Methods:

  • Utilizing a radial basis function network architecture.
  • Employing a supervised clustering algorithm for efficient network training.
  • Implementing a decision-directed clustering algorithm for tracking time-varying environments.

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Main Results:

  • Radial basis function networks structurally match optimal Bayesian equalizers.
  • The supervised clustering algorithm efficiently trains the network for Bayesian equalization.
  • The decision-directed approach allows real-time tracking and compensation for channel distortions.

Conclusions:

  • Radial basis function networks provide an effective implementation of Bayesian equalizers.
  • The proposed training and tracking methods are robust for digital communication systems.
  • This technique automatically compensates for nonlinear channel and equipment distortions.