Conference proceeding
Impulse Noise Detection in OFDM Communication System Using Machine Learning Ensemble Algorithms
INTERNATIONAL JOINT CONFERENCE SOCO'16- CISIS'16-ICEUTE'16, v 527, pp 85-91
01 Jan 2017
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.
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Details
- Title
- Impulse Noise Detection in OFDM Communication System Using Machine Learning Ensemble Algorithms
- Creators
- Ali N. Hasan - University of JohannesburgThokozani Shongwe - University of Johannesburg
- Contributors
- M Grana (Editor)J M LopezGuede (Editor)O Etxaniz (Editor)A Herrero (Editor)H Quintian (Editor)E Corchado (Editor)
- Publication Details
- INTERNATIONAL JOINT CONFERENCE SOCO'16- CISIS'16-ICEUTE'16, v 527, pp 85-91
- Series
- Advances in Intelligent Systems and Computing
- Publisher
- Springer Nature
- Number of pages
- 7
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Engineering Technology
- Web of Science ID
- WOS:000405330000009
- Scopus ID
- 2-s2.0-84992456069
- Other Identifier
- 991022004199404721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Interdisciplinary Applications
- Computer Science, Software Engineering
- Computer Science, Theory & Methods