A new equalization algorithm based on minimizing error negentropy

Sooyong Choi, Te Won Lee

Research output: Contribution to journalConference articlepeer-review


We introduce and investigate a new adaptive equalization method based on minimizing approximate negentropy of the estimation error for a finite-length equalizer. We consider an approximate negentropy using non-polynomial expansions of the estimation error as a new performance criterion to improve performance of a linear equalizer based on minimizing minimum mean squared error (MMSE). The proposed equalizer, called the NEGMIN equalizer, has two kinds of solutions, the MMSE solution and the other one, depending on the ratio of the normalization parameters. The NEGMIN equalizer has the best bit error rate (BER) performance when the ratio of the normalization parameters is properly adjusted to maximize the output power(variance) of the NEGMIN equalizer. Simulation experiments show that BER performance of the NEGMIN equalizer with the other solution than the MMSE one has similar characteristics to the adaptive minimum BER (AMBER) equalizer. The main advantage is that the proposed equalizer needs significantly fewer training symbols than the AMBER equalizer. Furthermore, the proposed equalizer is much robust to nonlinear distortions compared to the MMSE equalizer.

Original languageEnglish
Pages (from-to)241-245
Number of pages5
JournalIEEE Vehicular Technology Conference
Issue number1
Publication statusPublished - 2003
Event2003 IEEE 58th Vehicular Technology Conference, VTC2003-Fall - Orlando, FL, United States
Duration: 2003 Oct 62003 Oct 9

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics


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