Nonlinear channel equalization with QAM signal using Chebyshev artificial neural network

A computational efficient artificial neural network for adaptive channel equalization in a digital communication system with 4-QAM signal constellation is purposed. We proposed a single layer Chebyshev neural network (ChNN) by expanding the input pattern by Chebyshev polynomials. Performance comparison was carried out through extensive computer simulations with two other neural networks: an MLP and a functional link ANN together with a linear LIMS-based equalizer. It is shown that the ChNN provides satisfactory results in terms of convergence rate, MSE floor and BER over a wide range of EVR, SNR and nonlinear conditions with substantial reduction in the computational complexity.