@inproceedings{f114c43f04664694be01dfcb0bf6ff9b,
title = "Mitigation of impulse noise in powerline systems using ANFIS technique",
abstract = "The use of OFDM channel for the transmission of data in power line communication (PLC) system has been of several importance to technology development. However, during transmission, the OFDM channel is greatly disturbed by impulse noise that causes a wrong information to be received. Several techniques such as iteration, coding, clipping and nulling methods have been used to lessen the upshot of impulse noise in OFDM channel. However, these techniques still suffer some drawbacks and require a high signal-to-noise (SNR) power for high performance. This paper presents an advanced use of artificial neuro-fuzzy inference system (ANFIS) technique in removing the complete impulse noise and some of the additive white Gaussian noise (AWGN) that were mixed with the transmitted data in an OFDM channel and using the minimum SNR power. Obtained results propose that ANFIS technique can be used to mitigate impulse noise from a powerline communication channel.",
keywords = "ANFIS, Additive white Gaussian noise, Bit error rate, Encoders, Impulse noise, Machine learning, Orthogonal frequency division multiplexing, Power line communication",
author = "Shekoni, {Olamide M.} and Hasan, {Ali N.} and T. Shongwe",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 International Conference on Intelligent and Innovative Computing Applications, ICONIC 2018 ; Conference date: 06-12-2018 Through 07-12-2018",
year = "2018",
month = jul,
day = "2",
doi = "10.1109/ICONIC.2018.8601270",
language = "English",
series = "2018 International Conference on Intelligent and Innovative Computing Applications, ICONIC 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2018 International Conference on Intelligent and Innovative Computing Applications, ICONIC 2018",
address = "United States",
}