Analysis of Optimized Threshold with SLM based Blanking Non-Linearity for Impulsive Noise Reduction in Power Line Communication Systems

Ayaz, F. , Rabie, K. and Adebisi, B. (2018) Analysis of Optimized Threshold with SLM based Blanking Non-Linearity for Impulsive Noise Reduction in Power Line Communication Systems. In: 2018 11th International Symposium on Communication Systems, Networks & Digital Signal Processing (CSNDSP), Budapest, Hungary, 18-20 Jul 2018, ISBN 9781538613351 (doi: 10.1109/CSNDSP.2018.8471861)

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Abstract

High amplitude impulsive noise (IN) occurrence over power line channels severely degrades the performance of Orthogonal Frequency Division Multiplexing (OFDM) systems. One of the simplest methods to reduce IN is to precede the OFDM demodulator with a blanking non-linearity processor. In this respect, Selective Mapping (SLM) applied to an OFDM signal before the transmitter does not only reduce Peak-to-Average Power Ratio (PAPR) but also increases the resulting Signal-to-Noise Ratio (SNR) when blanking nonlinearity is applied at the receiver. This paper highlights another advantage of SLM based IN reduction, which is the reduced dependency on threshold used for blanking nonlinearity. The simulation results show that the optimal threshold to achieve maximum SNR is found to be constant for phase vectors greater than or equal to 64 in the SLM scheme. If the optimized threshold calculation method is used, the output SNR with SLM OFDM will result in SNR gains of up to 8.6dB compared to the unmodified system, i.e. without implementing SLM. Moreover, by using SLM, we not only get the advantage of low peak power, but also the need to calculate optimized threshold is eliminated, thereby reducing the additional computation.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Ayaz, Ms Ferheen
Authors: Ayaz, F., Rabie, K., and Adebisi, B.
College/School:College of Science and Engineering > School of Computing Science
ISBN:9781538613351
Published Online:27 September 2018

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