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Impulse Noise Detection in OFDM Communication System Using Machine Learning Ensemble Algorithms

  • Ali N. HasanEmail author
  • Thokozani Shongwe
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 527)

Abstract

An impulse noise detection scheme employing machine learning (ML) algorithm in Orthogonal Frequency Division Multiplexing (OFDM) is investigated. Four powerful ML’s multi-classifiers (ensemble) algorithms (Boosting (Bos), Bagging (Bag), Stacking (Stack) and Random Forest (RF)) were used at the receiver side of the OFDM system to detect if the received noisy signal contained impulse noise or not. The ML’s ensembles were trained with the Middleton Class A noise model which was the noise model used in the OFDM system. In terms of prediction accuracy, the results obtained from the four ML’s Ensembles techniques show that ML can be used to predict impulse noise in communication systems, in particular OFDM.

Keywords

Ensemble Prediction Bagging Boosting Stacking Random forest OFDM and impulse noise 

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Authors and Affiliations

  1. 1.Department of Electrical and Electronic Engineering TechnologyUniversity of JohannesburgJohannesburgSouth Africa

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