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Evolutionary MPNN for Channel Equalization

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DOI: 10.4236/jsip.2011.21002    4,311 Downloads   7,921 Views  

ABSTRACT

This paper proposes a novel equalizer, termed here as Evolutionary MPNN, where a complex modified probabilistic Neural Networks (MPNN) acts as a filter for the detected signal pattern. The neurons were embedded with optimization algorithms. We have considered two optimization algorithms, Bacteria Foraging Optimization (BFO) and Ant Colony Optimization (ACO). The proposed structure have the ability to process complex signals also can perform for slowly varying channels. Also, Simulation results prove the superior performance of the proposed equalizer over the existing MPNN equalizers.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Sarangi, A. , Ketan Panigrahi, B. and Prasada Panigrahi, S. (2011) Evolutionary MPNN for Channel Equalization. Journal of Signal and Information Processing, 2, 11-17. doi: 10.4236/jsip.2011.21002.

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