A cluster map based blind RBF decision feedback equalizer with reduced computational complexity
Recently. a cluster map based blind RBF equalizer (CM-BRE) has been proposed. By utilizing the underlying structure characteristics of RBF equalizer, the CM-BRE can be implemented by the combination of neural-gas algorithm (NGA) with several sorting operations. Although the CM-BRE is able to achieve almost identical performance with,he optimal RBF equalizer. the high computational load mainly caused by NGA limits it's application. In this paper, we propose a downsizing method that employs the inter-relation among RBF centers and significantly reduces the NGA's computational load. Furthermore, a method to determine the feedback vector is derived, then CM-BRE is extended to a cluster map based blind RBF decision feedback equalizer (CM-BRDFE). The proposed CM-BRDFE also shows the close performance with the optimal RBF decision feedback equalizer in simulations.
cluster map decision feedback rbf equalizer blind equalization