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Performance Evaluation of T-COFDM under Combined Noise in PLC with Log-NormalChannel Gain using Exact Derived Noise Distributions

Lookup NU author(s): Ghanim Al-Rubaye, Dr Charalampos Tsimenidis, Dr Martin Johnston

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This is the authors' accepted manuscript of an article that has been published in its final definitive form by The Institution of Engineering and Technology, 2019.

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Abstract

In this paper, the performance analyses of the proposed turbo-coded orthogonal frequency division multiplexing (TCOFDM) are investigated over the frequency-selective power-line communication (PLC) with log-normal channel gain based on derived effective complex-valued ratio distributions of the individual and combined noise samples at the zero-forcing (ZF) equalizer output. The effective noise samples are derived in the presence of Nakagami-m background interference (BI) noise, Middleton class A impulsive noise (MCAIN) and their combination. The performance of the soft decoder of the TC has been improved by computing the exact log-likelihood ratio (LLR) using derived distributions, with the derivation of pairwise error probability (PEP) and the average upper-bounds (AUBs). Moreover, the BER degradation in the conventional T-COFDM system has been improved by deriving two clipping thresholds to combat the effect of the non-Gaussian noise, the first one has been derived in the presence of the impulsive noise only modelled by MCAIN model and the second one in the presence of combined Nakagami-m BI noise and MCAIN model. Monte-Carlo simulation results demonstrate significant Bit Error Rate (BER) performance improvements of the proposed T-COFDM system compared to the improved conventional T-COFDM system with a close agreement to the AUBs derivation and analytical BER expression.


Publication metadata

Author(s): Alrubaye G, Tsimenidis C, Johnston M

Publication type: Article

Publication status: Published

Journal: IET Communications

Year: 2019

Pages: epub ahead of print

Online publication date: 18/01/2019

Acceptance date: 16/01/2019

Date deposited: 28/01/2019

ISSN (print): 1751-8628

ISSN (electronic): 1751-8636

Publisher: The Institution of Engineering and Technology

URL: https://doi.org/10.1049/iet-com.2018.6185

DOI: 10.1049/iet-com.2018.6185


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