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Bewley Lattice Diagram01:12

Bewley Lattice Diagram

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Lattice Centering and Coordination Number02:33

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Related Experiment Videos

Multilayer perceptron-based DFE with lattice structure.

A Zerguine1, A Shafi, M Bettayeb

  • 1Electrical Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia. azzedine@kfupm.edu.sa

IEEE Transactions on Neural Networks
|February 6, 2008
PubMed
Summary
This summary is machine-generated.

Whitening input data improves the performance of multilayer perceptron decision feedback equalizers (DFE). This technique enhances convergence rate and bit error rate, making MLPs more practical for communication systems.

Related Experiment Videos

Area of Science:

  • Digital Communications
  • Signal Processing
  • Machine Learning in Communications

Background:

  • Severely distorting channels necessitate nonlinear equalizers.
  • Multilayer perceptron (MLP)-based equalizers offer computational efficiency over traditional nonlinear filters.
  • A key limitation of MLP-based equalizers is their slow convergence rate.

Purpose of the Study:

  • To evaluate the impact of input data whitening on MLP-based decision feedback equalizers (DFE).
  • To improve the convergence speed and bit error rate (BER) performance of MLP-DFEs.
  • To compare MLP-DFE performance with least mean squares (LMS)-based DFE in various channel conditions.

Main Methods:

  • Employing adaptive lattice channel equalization algorithms for data whitening.
  • Modifying the adaptive lattice algorithm based on Ling and Proakis (1985).
  • Conducting computer simulations to assess equalizer performance.

Main Results:

  • Whitening received data significantly improves the convergence rate and bit error rate (BER) of MLP-DFEs.
  • Consistent performance improvements were observed in both time-invariant and time-varying channels.
  • MLP-DFEs outperformed LMS-based DFE in time-invariant channels.
  • Comparable performance was achieved between MLP-DFE and LMS-based DFE in time-varying channels.

Conclusions:

  • Input data whitening is an effective method to enhance MLP-DFE performance.
  • The adaptive lattice algorithm offers a viable approach for data whitening in MLP-DFEs.
  • MLP-DFEs present a competitive nonlinear equalization solution, particularly when convergence is improved.