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

• Hasan, Ali N. ^[1] ; Thokozani Shongwe ^[1]
  1. [1] University of Johannesburg

     University of Johannesburg

     City of Johannesburg, Sudáfrica

     
• Localización: International Joint Conference SOCO’16-CISIS’16-ICEUTE’16: San Sebastián, Spain, October 19th-21st, 2016 Proceedings / coord. por Manuel Graña Romay, José Manuel López Guede, Oier Etxaniz, Álvaro Herrero Cosío, Héctor Quintián Pardo, Emilio Santiago Corchado Rodríguez, 2017, ISBN 978-3-319-47364-2, págs. 85-94
• Idioma: inglés
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• Resumen

  • 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.