Adaptive equalization with neural networks: new multi-layer perceptron structures and their evaluation

Nonlinear equalizers find use in communication applications where the channel distortion is too severe for a linear equalizer to handle. Because of their nonlinear capability and other attractive properties, neural networks have become appealing candidates for equalization problems. The application of neural networks to adaptive equalization problems is investigated. In particular, realization structures (MLP-I, MLP-II) of a multilayer perceptron (MLP) with a backpropagation training algorithm are introduced, and it is shown that they work well for both PAM and QAM signals of any constellation size (e.g., 4-PAM, 8-PAM, 16-QAM, and 64-QAM). It is demonstrated that both MLP structures outperform the least mean square (LMS)-based linear equalizer when channel distortions are nonlinear