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The Contour Algorithm for Self-Training Adaptive Equalization

  • Giovanni Cherubini
  • Sedat Ölçer
  • Gottfried Ungerboeck
Conference paper

Abstract

We present a new algorithm to adjust the coefficients of a transversal equalizer in a self-training mode, where initial equalizer convergence is achieved without requiring the transmission of a known reference signal. The generation of the adjustment terms depends on whether the equalizer output signal is found outside or within a region bounded by a contour line connecting the outer points of the input symbol constellation. To characterize the convergence properties of the self-training equalization algorithm, a functional, the derivatives of which are closely related to the employed stochastic gradient, is introduced. This functional can exhibit only a set of equivalent global minima, which correspond to points of perfect equalization for different equalizer delays and signs of the output signal. A joint robust carrier-phase recovery algorithm is also presented. The convergence behavior of the algorithm is illustrated by simulations.

Keywords

Phase Error Stochastic Gradient Quadrature Amplitude Modulation Input Symbol Outer Point 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag London Limited 1998

Authors and Affiliations

  • Giovanni Cherubini
    • 1
  • Sedat Ölçer
    • 1
  • Gottfried Ungerboeck
    • 1
  1. 1.IBM Research DivisionZurich Research LaboratoryRüschlikonSwitzerland

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