Decision Feedback Equalization Using RBF and MLP Networks

An important problem in channel equalization, is that of, removing distortions introduced by linear or nonlinear message corrupting mechanisms in the reconstruction of the original signals. Severe nonlinear distortions can make it difficult for conventional equalizers to reconstruct the original signals. In this paper, we propose a Decision Feedback Equalizer which can recover the original signals correctly under severe nonlinear distortion. They are more powerful than linear equalizers especially for severe inter-symbol interference (ISI) channels without as much noise enhancement as the linear equalizers. In this paper, a radial basis function (RBF) network is used to implement DFE. Advantages and problems of this system are discussed and its results are then compared with DFE using multi layer perceptron net (MLP). Results indicate that the implemented system outperforms MLP, given the same signal to noise ratio as it offers minimum mean square error. The learning rate of the implemented system is also faster than the multilayered case.