TITLE:
Evolutionary MPNN for Channel Equalization
AUTHORS:
Archana Sarangi, Bijay Ketan Panigrahi, Siba Prasada Panigrahi
KEYWORDS:
Channel Equalization, Probabilistic Neural Network, Bacteria Foraging, Ant Colony Optimization
JOURNAL NAME:
Journal of Signal and Information Processing,
Vol.2 No.1,
February
23,
2011
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.