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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



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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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

Volume: 13

Issue: 6

Pages: 766-775

Print publication date: 01/04/2019

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


DOI: 10.1049/iet-com.2018.6185


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