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.
In this paper exact expressions are presented for the bit error probability (BEP) of M-ary Quadrature Amplitude Modulation (M-QAM) signals subject to Double Gated Additive White Gaussian Noise (G 2 AWGN) combined with Rayleigh fading. In this study, the Rayleigh fading channel can be seen as a channel subject to an additive noise, R, obtained by the ratio between the received signal and the fading amplitude. Besides, for each model of impulsive noise considered, the probability density function (PDF) of R is presented, which is defined as the ratio between the random variable of the impulsive noise and the random variable of the fading.
In this paper, a new closed-form expression is presented for determining the Bit Error Probability (BEP), Pe, of Mary Quadrature Amplitude Modulation (M-QAM) signals subject to Double Gated Additive White Gaussian Noise (G 2 AWGN) and α-µ fading, from an approach referred to as Dirac delta function approximation. In this approach, the sampling property of the Dirac delta function and an alternative representation of the tail distribution function of the standard normal distribution, known as Q-function, are used to obtain the BEP expression as a function of the Signal to Permanent Noise Ratio (SNR), Signal to Impulsive Noise Ratio (SNI) and the parameters that characterizes the channel. All BEP curves shown in this article are corroborated by simulations performed with the Monte Carlo method.
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