Improving Conventional Equalizers with Neural Networks

In this paper we show that the low detection capabilities of conventional equalizers (linear and decision feedback equalizers) and the excessive complexity of those based on neural networks can be avoided by means of mixed schemes. Linear equalizers aided by centroid selection (as in Radial Basis Function networks) improve performance over the standard linear FIR equalization approach, and modified DFE based on bi-layer perceptron avoids error propagation, outperforming conventional schemes. ¹