Equalization of satellite mobile communication channels using combined self-organizing maps and RBF networks
S Bouchired, M Ibnkahla, D Roviras… - Proceedings of the …, 1998 - ieeexplore.ieee.org
Proceedings of the 1998 IEEE International Conference on Acoustics …, 1998•ieeexplore.ieee.org
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 …
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 …
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.
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