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Evolutionary MPNN for Channel Equalization

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Author(s): Archana Sarangi | Bijay Ketan Panigrahi | Siba Prasada Panigrahi

Journal: Journal of Signal and Information Processing
ISSN 2159-4465

Volume: 02;
Issue: 01;
Start page: 11;
Date: 2011;
Original page

Keywords: Channel Equalization | Probabilistic Neural Network | Bacteria Foraging | Ant Colony Optimization

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.
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