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Nonlinear blind equalization schemes using complex-valued multilayer feedforward neural networks.

C You1, D Hong

  • 1Department of Electronic Engineering, Yonsei University, Seoul 120-749, South Korea.

IEEE Transactions on Neural Networks
|February 8, 2008
PubMed
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This study introduces four novel nonlinear blind equalization schemes using complex-valued multilayer perceptrons. These advanced methods improve convergence speed and reduce mean squared error for Quadrature Amplitude Modulation (QAM) signals.

Area of Science:

  • Digital Signal Processing
  • Machine Learning in Communications

Background:

  • Stochastic-gradient iterative equalization schemes often exhibit high residual estimation error due to linear filters.
  • Existing methods struggle with nonconvex cost functions and linear filter limitations.

Purpose of the Study:

  • To propose four nonlinear blind equalization schemes utilizing complex-valued multilayer perceptrons.
  • To develop a novel complex-valued activation function for enhanced performance with QAM signals.
  • To analyze the properties of suitable complex-valued activation functions.

Main Methods:

  • Development of four nonlinear blind equalization algorithms employing complex-valued multilayer perceptrons.
  • Derivation of learning algorithms for the proposed schemes.
  • Design and implementation of a new complex-valued activation function for QAM signals.

Related Experiment Videos

  • Mathematical proof of reduced correlation between real and imaginary parts of input data.

    • Main Results:

    • The proposed nonlinear schemes demonstrate improved initial convergence speed.
    • Reduced Mean Squared Error (MSE) in steady-state conditions was observed.
    • Effective correction of arbitrary phase rotation caused by channel distortion, even without carrier phase tracking.

    Conclusions:

    • The novel nonlinear blind equalization schemes offer superior performance compared to traditional methods.
    • The developed complex-valued activation function is effective for various QAM constellation sizes.
    • These schemes provide robust channel equalization capabilities, enhancing digital communication reliability.