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.
Highlights
Channel equalization plays an important role in digital communication systems
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
Summary
Channel equalization plays an important role in digital communication systems. There are tremendous developments in equalizer structures since the advent of neural networks in signal processing applications. Recent literature is healthy enough with newer applications of neural networks [1,2,3,4,5] and in particular to independent component analysis, noise cancellation and channel equalization [6,7,8,9,10,11,12,13,14,15,16] All of these papers overlooked two basic problems encountered. Novelty in this paper can be seen as, application of two known algorithms, ACO and BFO, to the problem of channel equalization. This underlines the improvement added by the optimization algorithms.
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