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.
References
Shongwe, T., Vinck, A.J.H., Ferreira, H.C.: On impulse noise and its models. In: Proceedings of the 2014 International Symposium on Power-Line Communications and its Applications, Glasgow, Scotland, March 30 - April 2, 2014, pp. 12–17 (2014)
Zhidkov, S.V.: Impulsive noise suppression in OFDM-based communication systems. IEEE Trans. Consum. Electron. 49(4), 944–948 (2003)
Häring, J., Vinck, A.J.H.: OFDM transmission corrupted by impulsive noise. In: Proceedings of the 2000 International Symposium on Power-Line Communications and its Applications, Limerick, Ireland, April 5–7, 2000, pp. 5–7 (2000)
Zhidkov, S.V.: Performance analysis and, optimization of OFDM receiver with blanking nonlinearity in impulsive noise environment. IEEE Trans. Veh. Technol. 55(1), 234–242 (2006)
Tseng, D.-F., Han, Y.S., Mow, W.H., Chang, L.-C., Vinck, A.J.H.: Robust clipping for OFDM transmissions over memoryless impulsive noise channels. IEEE Commun. Lett. 16(7), 1110–1113 (2012)
Sargrad, D.H., Modestino, J.W.: Errors-and-erasures coding to combat impulse noise on digital subscriber loops. IEEE Trans. Commun. 38(8), 1145–1155 (1990)
Li, T., Mow, W.H., Siu, M.: Joint erasure marking and viterbi decoding algorithm for unknown impulsive noise channels. IEEE Trans. Wireless Commun. 7(9), 3407–3416 (2008)
Mengi, A., Vinck, A.J.H.: Successive impulsive noise suppression in OFDM. In: Proceedings of the 2009 IEEE International Symposium on Power Line Communications, Rio de Janeiro, Brazil, Mar. 5–7, 2009, pp. 33–37 (2009)
Faber, T., Scholand, T., Jung, P.: Turbo decoding in impulsive noise environments. Electron. Lett. 39(14), 1069–1071 (2003)
Shongwe, T., Vinck, A.J.H., Ferreira, H.C.: A study on impulse noise and its models. SAIEE Afr. Res. J. 106(3), 119–131 (2015)
Witten, I., Frank, E.: Data Mining, Practical Machine Learning Tools and Techniques, 2nd (2005). ISBN: 0-12-088407-0
Mitchell, T., McGraw, H.: Machine learning, 2nd, Chap. 1, January 2010
Hasan, A.N., Twala, B., Marwala, T.: Moving Towards Accurate Monitoring and Prediction of Gold Mine Underground Dam Levels. In: IEEE IJCNN WCCI, Beijing, China (2014)
Sun, Q., Pfahringer, B.: Bagging Ensemble Selection. The University of Waikato, Hamilton, New Zealand (2010)
Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123–140 (1996)
Vemulapalli, S., Luo, X., Pitrelli, J., Zitouni, I.: Using bagging and boosting techniques for improving coreference resolution. Informatica 34, 111–118 (2010)
Buhlmann, P.: Bagging, Boosting and Ensemble Methods. In: ETH Zurich, Seminar fur Statistik, HG G17, CH-8092 Zurich, Switzerland (2010)
Breiman, L.: Random Forests. Mach. Learn. 45(1), 5–32 (2001)
Wolpert, D.H.: Stacked generalization. Neural Netw. 5, 241–259 (1992)
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Hasan, A.N., Shongwe, T. (2017). Impulse Noise Detection in OFDM Communication System Using Machine Learning Ensemble Algorithms. In: Graña, M., López-Guede, J.M., Etxaniz, O., Herrero, Á., Quintián, H., Corchado, E. (eds) International Joint Conference SOCO’16-CISIS’16-ICEUTE’16. SOCO CISIS ICEUTE 2016 2016 2016. Advances in Intelligent Systems and Computing, vol 527. Springer, Cham. https://doi.org/10.1007/978-3-319-47364-2_9
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