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 the 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.

The purpose of this paper is to propose a novel cluster map based blind RBF equalizer for received signal constellation (RSC) independent channel, which belongs to RSC based blind equalization approach. Without channel estimator, firstly, the desired numbers of unlabeled RBF centers are obtained by an unsupervised clustering algorithm. Then a cluster map generated from the known RBF equalizer structure is used to partition the unlabeled centers into appropriate subsets merely by several simple sorting operations, which corresponds to the weight initialization. Finally, the weight is adjusted iteratively by an unsupervised least mean square (LMS) algorithm. Since the process of the weight initialization using the underlying structure of RBF equalizer is very effective, the proposed blind RBF equalizer can achieve almost identical performance with the optimal RBF equalizer. The validity of the proposed equalizer is also demonstrated by computer simulations.