Conference proceeding
Proposed Machine Learning System to Predict and Estimate Impulse Noise in OFDM Communication System
Proceedings of the Annual Conference of the IEEE Industrial Electronics Society, pp.1016-1020
01 Jan 2016
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 naive 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.
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Details
- Title
- Proposed Machine Learning System to Predict and Estimate Impulse Noise in OFDM Communication System
- Creators
- Ali N. Hasan - University of JohannesburgThokozani Shongwe - University of Johannesburg
- Publication Details
- Proceedings of the Annual Conference of the IEEE Industrial Electronics Society, pp.1016-1020
- Series
- IEEE Industrial Electronics Society
- Publisher
- IEEE
- Number of pages
- 5
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Engineering Leadership and Society/Engineering Technology
- Identifiers
- 991022004774104721