Pasteur Poda ; Samir Saoudi ; Thierry Chonavel ; Frédéric GUILLOUD ; Théodore Tapsoba
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Non-parametric kernel-based bit error probability estimation in digital communication systems: An estimator for soft coded QAM BER computation
arima:4348 -
Revue Africaine de Recherche en Informatique et Mathématiques Appliquées,
August 3, 2018,
Volume 27 - 2017 - Special issue CARI 2016
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https://doi.org/10.46298/arima.4348
Non-parametric kernel-based bit error probability estimation in digital communication systems: An estimator for soft coded QAM BER computationArticle
The standard Monte Carlo estimations of rare events probabilities suffer from too much computational time. To make estimations faster, kernel-based estimators proved to be more efficient for binary systems whilst appearing to be more suitable in situations where the probability density function of the samples is unknown. We propose a kernel-based Bit Error Probability (BEP) estimator for coded M-ary Quadrature Amplitude Modulation (QAM) systems. We defined soft real bits upon which an Epanechnikov kernel-based estimator is designed. Simulation results showed, compared to the standard Monte Carlo simulation technique, accurate, reliable and efficient BEP estimates for 4-QAM and 16-QAM symbols transmissions over the additive white Gaussian noise channel and over a frequency-selective Rayleigh fading channel.
Volume: Volume 27 - 2017 - Special issue CARI 2016
Published on: August 3, 2018
Accepted on: July 10, 2018
Submitted on: March 6, 2018
Keywords: Monte Carlo method, Kernel estimator, Bit error rate, Probability density function, Méthode Monte Carlo,Bit error probability, Fonction de densité de probabilité,Probabilité d’erreur binaire, Taux d’erreur binaire, Estimateur à noyau,Bit error rate,Probability density function,Monte Carlo method,Kernel estimator,
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SPI.SIGNAL
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Engineering Sciences [physics]/Signal and Image processing,
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SPI.OTHER
]
Engineering Sciences [physics]/Other
Funding:
Source : OpenAIRE Graph
Mobile and wireless communications Enablers for Twenty-twenty (2020) Information Society; Funder: European Commission; Code: 317669