Conference proceeding
Detecting and Removing the Impulsive Noise in OFDM Channels Using Different ANN Techniques
2018 INTERNATIONAL CONFERENCE ON INTELLIGENT AND INNOVATIVE COMPUTING APPLICATIONS (ICONIC), pp.32-36
01 Jan 2018
Abstract
Orthogonal frequency division multiplexer (OFDM) is a recent modulation scheme used to transmit signals across power line communication (PLC) channel due to its robustness against some known PLC problems. However, this scheme is greatly affected by the impulsive noise (IN) and often causes corruption with the transmitted bits. Different impulsive noise error correcting methods have been introduced and used to remove impulsive noise in OFDM systems. However, these techniques suffer some limitations and require much signal to noise ratio (SNR) power to operate. In this paper, an approach of designing an effective impulsive-noise error-correcting technique was introduced using three-known artificial neural network techniques (Levenberg-Marquardt, Scaled conjugate gradient, and Bayesian regularization). Findings suggest that both Bayesian regularization and Levenberg-Marquardt ANN techniques can be used to effectively remove the impulsive noise present in an OFDM channel and using the least SNR power.
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Details
- Title
- Detecting and Removing the Impulsive Noise in OFDM Channels Using Different ANN Techniques
- Creators
- Olamide M. Shekoni - University of JohannesburgAli N. Hasan - University of JohannesburgT. Shongwe - University of Johannesburg
- Publication Details
- 2018 INTERNATIONAL CONFERENCE ON INTELLIGENT AND INNOVATIVE COMPUTING APPLICATIONS (ICONIC), pp.32-36
- Publisher
- IEEE
- Number of pages
- 5
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Engineering Leadership and Society/Engineering Technology
- Identifiers
- 991022004630304721