@inproceedings{093d05ca9d914974845a33756a8808de,
title = "Proposed machine learning system to predict and estimate impulse noise in OFDM communication system",
abstract = "This paper investigates the use of machine learning (ML) in predicting and estimating the impulse noise. Four ML's algorithms (Multilayer perceptron MLP, support vector machine SVM, k nearest neighbour kNN and na{\"i}ve Bayesian classifier NBC) were implemented in an OFDM system affected by impulse noise. The impulse noise model used was the Middleton Class A noise model. The ML's were trained with Middleton Class A impulse noise model so that they can be able to predict the presence of impulse noise in the communication system. In terms of prediction accuracy, results showed that kNN slightly outperformed MLP and NBC and accomplished high prediction accuracy of 99.8%. SVM achieved the lowest prediction accuracy among the four used methods. These results indicates that machine learning could be used to estimate impulse noise in OFDM communications system.",
keywords = "Impulse noise, Multilayer perceptron, Na{\"i}ve Bayesian, OFDM, Prediction, Support vector machines",
author = "Hasan, {Ali N.} and Thokozani Shongwe",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 42nd Conference of the Industrial Electronics Society, IECON 2016 ; Conference date: 24-10-2016 Through 27-10-2016",
year = "2016",
month = dec,
day = "21",
doi = "10.1109/IECON.2016.7793751",
language = "English",
series = "IECON Proceedings (Industrial Electronics Conference)",
publisher = "IEEE Computer Society",
pages = "1016--1020",
booktitle = "Proceedings of the IECON 2016 - 42nd Annual Conference of the Industrial Electronics Society",
address = "United States",
}