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A Hybrid Unsupervised Clustering Algorithm for Channel Equalization

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8 Author(s)
Helder Knidel ; Laboratory of Bioinformatics and Bio-inspired Computing (LBiC) ¿ School of Electrical and Computer Engineering (FEEC) ¿ University of Campinas (Unicamp) ¿ Caixa Postal 6101 ¿ CEP 13083-970 ¿ Campinas ¿ SP ¿ Brazil. ; Rafael Ferrari ; Leonardo T. Duarte ; Ricardo Suyama
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In this work, we propose and analyze the applicability of a novel unsupervised data clustering technique in the problem of channel equalization. The proposal combines two different methods, a neuro-immune network called RABNET [1] and the iterated local search algorithm (ILS) [2], to produce a tool that, in contrast to classical solutions like the k-means algorithm, does not require a priori knowledge about the number of clusters to be found and, moreover, possesses mechanisms to avoid local convergence. Simulation results attest both the viability and efficiency of the proposal in scenarios conceived to highlight certain aspects that can be decisive insofar as real-world applications are concerned.

Published in:

2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing

Date of Conference:

6-8 Sept. 2006