In this paper, we propose a robust parameters estimation algorithm for channel coded systems based on the low-density parity-check (LDPC) code over fading channels with impulse noise. The estimated parameters are then used to generate bit log-likelihood ratios (LLRs) for a soft-inputLDPC decoder. The expectation-maximization (EM) algorithm is used to estimate the parameters, including the channel gain and the parameters of the Bernoulli-Gaussian (B-G) impulse noise model. The parameters can be estimated accurately and the average number of iterations of the proposed algorithm is acceptable. Simulation results show that over a wide range of impulse noise power, the proposed algorithm approaches the optimal performance under different Rician channel factors and even under Middleton class-A (M-CA) impulse noise models.
Chun-Yin CHEN
National Chung Cheng University
Mao-Ching CHIU
National Chung Cheng University
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Chun-Yin CHEN, Mao-Ching CHIU, "Parameters Estimation of Impulse Noise for Channel Coded Systems over Fading Channels" in IEICE TRANSACTIONS on Communications,
vol. E104-B, no. 7, pp. 903-912, July 2021, doi: 10.1587/transcom.2020EBP3132.
Abstract: In this paper, we propose a robust parameters estimation algorithm for channel coded systems based on the low-density parity-check (LDPC) code over fading channels with impulse noise. The estimated parameters are then used to generate bit log-likelihood ratios (LLRs) for a soft-inputLDPC decoder. The expectation-maximization (EM) algorithm is used to estimate the parameters, including the channel gain and the parameters of the Bernoulli-Gaussian (B-G) impulse noise model. The parameters can be estimated accurately and the average number of iterations of the proposed algorithm is acceptable. Simulation results show that over a wide range of impulse noise power, the proposed algorithm approaches the optimal performance under different Rician channel factors and even under Middleton class-A (M-CA) impulse noise models.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2020EBP3132/_p
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@ARTICLE{e104-b_7_903,
author={Chun-Yin CHEN, Mao-Ching CHIU, },
journal={IEICE TRANSACTIONS on Communications},
title={Parameters Estimation of Impulse Noise for Channel Coded Systems over Fading Channels},
year={2021},
volume={E104-B},
number={7},
pages={903-912},
abstract={In this paper, we propose a robust parameters estimation algorithm for channel coded systems based on the low-density parity-check (LDPC) code over fading channels with impulse noise. The estimated parameters are then used to generate bit log-likelihood ratios (LLRs) for a soft-inputLDPC decoder. The expectation-maximization (EM) algorithm is used to estimate the parameters, including the channel gain and the parameters of the Bernoulli-Gaussian (B-G) impulse noise model. The parameters can be estimated accurately and the average number of iterations of the proposed algorithm is acceptable. Simulation results show that over a wide range of impulse noise power, the proposed algorithm approaches the optimal performance under different Rician channel factors and even under Middleton class-A (M-CA) impulse noise models.},
keywords={},
doi={10.1587/transcom.2020EBP3132},
ISSN={1745-1345},
month={July},}
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TY - JOUR
TI - Parameters Estimation of Impulse Noise for Channel Coded Systems over Fading Channels
T2 - IEICE TRANSACTIONS on Communications
SP - 903
EP - 912
AU - Chun-Yin CHEN
AU - Mao-Ching CHIU
PY - 2021
DO - 10.1587/transcom.2020EBP3132
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E104-B
IS - 7
JA - IEICE TRANSACTIONS on Communications
Y1 - July 2021
AB - In this paper, we propose a robust parameters estimation algorithm for channel coded systems based on the low-density parity-check (LDPC) code over fading channels with impulse noise. The estimated parameters are then used to generate bit log-likelihood ratios (LLRs) for a soft-inputLDPC decoder. The expectation-maximization (EM) algorithm is used to estimate the parameters, including the channel gain and the parameters of the Bernoulli-Gaussian (B-G) impulse noise model. The parameters can be estimated accurately and the average number of iterations of the proposed algorithm is acceptable. Simulation results show that over a wide range of impulse noise power, the proposed algorithm approaches the optimal performance under different Rician channel factors and even under Middleton class-A (M-CA) impulse noise models.
ER -