Bit Error Probability of M-QAM under Impulsive Noise and Fading Modeled by Markov Chains

Silva, Hugerles S.; Alencar, Marcelo S.; Queiroz, Wamberto J. L.; Coelho, Rodrigo de Almeida; Madeiro, Francisco

doi:10.13164/re.2018.1183

Cited by 5 publications

(3 citation statements)

References 17 publications

(25 reference statements)

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“…Hence, to decide the number of states which accurately model the bursty noise behavior is challenging. In [27], a two-state Markov chain model is used to study the effect of non-Gaussian noise and fading on BER performance. It takes into account gated additive white Gaussian noise (GAWGN) with η-μ fading as one state and double-gated additive white Gaussian noise (G 2 AWGN) with η-μ fading as another state.…”

Section: Bit Error Rate (Ber) Analysis Over Different Non-gaussian Noise Modelsmentioning

confidence: 99%

Ber Analysis in Non-Homogeneous Fading Environments With Impulsive Noise

Ashraf¹,

Begh²

2021

PIER M

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In this paper, using binary phase-shift keying (BPSK) modulation, analytical expressions of bit-error-rate (BER) for various non-homogeneous fading environments (α-μ, η-μ and κ-μ) subjected to SαS noise are obtained. The derived results are expressed in terms of Meijer's G-function and Gamma function. These expressions are used to study the performance of other prominent fading models (like Nakagami-m, Rayleigh, Rician, and Hoyt) available in the technical literature. Further, it is shown that the effect of the impulsive index (α) over BER is much pronounced compared to the amount of fading (AF). Numerical results are provided for different impulsive settings. The results obtained agree with those from simulations.

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Section: Bit Error Rate (Ber) Analysis Over Different Non-gaussian Noise Modelsmentioning

confidence: 99%

Ber Analysis in Non-Homogeneous Fading Environments With Impulsive Noise

Ashraf¹,

Begh²

2021

PIER M

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“…The expressions presented by Queiroz et al, corroborated by computational simulations, are obtained from the classical method, which consists of weighting the BEP conditioned to the fading by the PDF of fading. In the work of Silva et al, exact expressions for the BEP are presented for the modulation scheme M ‐QAM considering the channel modeled by a Markov chain with N states, with each state representing different scenarios. Several BEP curves, confirmed by simulations, are plotted for different parameters that characterize the channel.…”

Section: Related Workmentioning

confidence: 99%

Bit error probability of the M‐QAM scheme subject to multilevel double gated additive white Gaussian noise and η‐μ, κ‐μ, or α‐μ fading

Silva

Queiroz

Almeida

et al. 2019

Trans Emerging Tel Tech

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This paper presents new and exact expressions for the bit error probability (BEP) of the square M-ary quadrature amplitude modulation scheme subject to -, -or -fading and multilevel double gated additive white Gaussian noise, which is composed by the sum of a white Gaussian component of variance 2 g and a white Gaussian noise component of variance 2 i gated by a discrete random process defined in continuous time, which takes values into a finite discrete set. BEP curves, with and without imperfect channel estimation, corroborated by simulations using the Monte Carlo method, are presented for different values of parameters that characterize mathematically the channel.Trans Emerging Tel Tech. 2019;30:e3682.wileyonlinelibrary.com/journal/ett

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“…Canais sujeitos a ruído impulsivo e desvanecimento generalizado η -µ [8] podem caracterizar uma ampla variedade de cenários de transmissão [9], incluindo desvanecimento em pequena e larga escala, com ausência de linha de visada entre o transmissor e o receptor, englobando, como casos especiais, por exemplo, as distribuições: Rayleigh, Weibull, Nakagami-m e Hoyt.…”

Section: Introductionunclassified

Algoritmo ABC Aplicado à Atribuição de Índices para Quantização Vetorial Robusta de Imagens

Barros¹,

Santana²,

Almeida³

et al. 2020

Anais Do 14. Congresso Brasileiro De Inteligência Computacional

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Resumo-Nesse artigoé abordado o problema da Quantização Vetorial Robusta (QVR) noâmbito da transmissão de imagens por canal com desvanecimento rápido e ruído aditivo gaussiano branco convencional ou duplamente gatilhado (impulsivo). São apresentadas modificações no algoritmo Artificial Bee Colony (ABC) aplicado ao problema de otimização combinatorial de atribuição deíndices na quantização vetorial robusta. O desempenho do ABCé comparado com outro algoritmo de otimização amplamente utilizado na literatura, o Simulated Annealing (SA). Resultados de simulação mostram que o algoritmo ABC modificado apresenta uma superioridade sobre o SA em termos de redução dó ındice de desordem em todos os tamanhos de dicionários testados, permitindo obter imagens reconstruídas com qualidade superioràquelas reconstruídas sem uso de QVR por canais ruidosos. Palavras-chave-Quantização vetorial robusta, otimização combinatorial, inteligência de exames, ruído impulsivo, desvanecimento generalizado, transmissão de imagens.

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Bit Error Probability of M-QAM under Impulsive Noise and Fading Modeled by Markov Chains

Cited by 5 publications

References 17 publications

Ber Analysis in Non-Homogeneous Fading Environments With Impulsive Noise

Ber Analysis in Non-Homogeneous Fading Environments With Impulsive Noise

Bit error probability of the M‐QAM scheme subject to multilevel double gated additive white Gaussian noise and η‐μ, κ‐μ, or α‐μ fading

Algoritmo ABC Aplicado à Atribuição de Índices para Quantização Vetorial Robusta de Imagens

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