Equalization of satellite mobile communication channels using combined self-organizing maps and RBF networks

The paper proposes a neural network approach to equalize time varying nonlinear channels. The approach is applied to a satellite UMTS channel composed of time invariant linear filters, a non-linear memoryless amplifier and a time varying multipath propagation channel. The neural network equalizer has a radial basis function structure. The usual k-mean clustering algorithm is replaced by a Kohonen (1995) learning rule. This results in an RBF-SOM equalizer which outperforms the LMS equalizer, and which has better recovering abilities (after passing through a high fading area) than the former RBF equalizer