A new equalization algorithm based on minimizing error negentropy

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 more robust to nonlinear distortions compared to the NMSE equalizer.