Maximum entropy whitening algorithm with adaptive neuron slope for blind DFE

Abstract

The addressed blind decision feedback equalizer (DFE) reverses the classical order of its feed-forward and feedback filters at the beginning of adaptation to achieve the best equalization of the minimum and maximum phase components of a channel transfer function. Although very effective, this blind equalization approach deals with the feedback filter mismatch at the time of its transformation from the front-end all-pole whitener of the received signal to the decision-directed feedback equalizer placed after the feedforward filter. To eliminate this weakness, the adaptive neuron slope is introduced instead of the fixed one into the stochastic gradient whitening algorithm based on the joint entropy maximization cost. The performance of the innovated algorithm is verified by simulating m-QAM (quadrature amplitude modulation) signals transmission over multipath channels. The algorithm with the adaptive neuron slope achieves a better whitening of the received signal spectrum, and, hence, increases the equalization successfulness.