Channel equalization using neural networks

In this paper the equalization of different communication channels with different signaling constellations using artificial neural networks is investigated. We will show that applying a fuzzy rule to the adjustment of the learning rate and momentum of the back-propagation network will increase the convergence rate of the equalizer. We will use the complex backpropagation network to equalize complex-valued constellations. Using the geometrical interpretaion of the equalization problem, we will propose a decision device which decides on whether the channel must be equalized by a linear equalizer or a neural network equalizer.