A polynomial-perceptron based decision feedback equalizer with a robust learning algorithm

Abstract

A new equalization scheme, including a decision feedback equalizer (DFE) equipped with polynomial-perceptron model of nonlinearities and a robust learning algorithm using l(p)-norm error criterion with p < 2, is presented in this paper, This equalizer exerts the benefit of using a DFE and achieves the required nonlinearities in a single-layer net. This makes it easier to train by a stochastic gradient algorithm in comparison with a multi-layer net. The algorithm is robust to aberrant noise for the addressed equalizer and, hence, converges much faster in comparison with the l(p)-norm. A detailed performance analysis considering possible numerical problem for p < 1 is given in this paper. Computer simulations show that the scheme has faster convergence rate and satisfactory bit error rate (BER) performance. It also shows that the new equalizer is capable of approaching the performance achieved by a minimum EER equalizer.