Polynomial perceptrons and their applications to fading channel equalization and co-channel interference suppression

This paper investigates the behaviors of polynomial perceptrons and introduces a fractionally spaced recursive polynomial perceptron with low complexity and fast convergence rate. The nonlinear mapping ability of the polynomial perceptron is analyzed. It is shown that a polynomial perceptron with degree L(⩾4) satisfies the Stone-Weierstrass theorem and can approximate any continuous function to within a specified accuracy. Moreover, the nonlinear mapping ability of a polynomial perceptron with degree L is similar to that of the three-layer perceptron with one hidden layer for time same number of neurons in the input layer. The nonlinear mapping ability of the fractionally spaced recursive polynomial perceptron is also presented. Applications of polynomial perceptrons for fading channel equalization and co-channel interference suppression in a 16-level quadrature amplitude modulation receiver system are considered. Computer simulations are used to evaluate and compare the performance of polynomial perceptron (PP) and fractionally spaced bilinear perceptron (FSBLP) with that of the synchronous decision feedback multilayer perceptron (SDFMLP), fractionally spaced decision feedback multilayer perceptron (FSDFMLP), and the conventional decision feedback equalizer (DFE). The results show that the performance of the fractionally spaced bilinear perceptron is clearly superior to that of the other structures