The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
O ponto de partida deste artigo é a definição de um modelo não linear do processo de leitura em discos ópticos de alta densidade. Sob condições de alta densidade, a leitura do sinal não é um processo linear e sofre também de interferência. Para lidar com estes problemas, é necessária a identificação de um modelo não linear adequado. Um modelo físico baseado na teoria escalar óptica é usado para identificar os núcleos de um modelo não linear baseado na série de Volterra. Tanto a análise quanto as simulações mostram que um modelo bidimensional de segunda ordem descreve com precisão o processo de leitura. Uma vez equipados com o modelo de canal Volterra, avaliamos o desempenho de vários receptores não lineares. Primeiro consideramos a Equalização Adaptativa Não Linear de Volterra (NAVE). Simulações mostram que o desempenho de estruturas clássicas para canais lineares é significativamente afetado pela resposta não linear. O receptor NAVE não linear pode alcançar melhor desempenho que o Estimador de Sequência de Máxima Verossimilhança (MLSE), com menor complexidade. É apresentado um inovador Estimador de Sequência de Máxima Verossimilhança Não Linear (NMLSE), baseado na combinação de MLSE e cancelamento não linear de Interferência Inter-Símbolos (ISI). O NMLSE oferece vantagens significativas em relação ao MLSE tradicional e tem desempenho melhor que a equalização tradicional para canais não lineares (como NAVE). Finalmente, o artigo trata do cancelamento de cross talk de trilhas adjacentes. Propomos e analisamos um cancelador de cross talk não linear adaptativo baseado em um sistema de detecção de três pontos. Por uma questão de simplicidade, todas as comparações de desempenho apresentadas neste artigo são baseadas na suposição de que o ruído é Aditivo, Branco e Gaussiano (modelo AWGN).
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Luigi AGAROSSI, Sandro BELLINI, Pierangelo MIGLIORATI, "Effective Nonlinear Receivers for High Density Optical Recording" in IEICE TRANSACTIONS on Electronics,
vol. E85-C, no. 9, pp. 1675-1683, September 2002, doi: .
Abstract: The starting point of this paper is the definition of a nonlinear model of the read out process in high density optical discs. Under high density condition, the signal read out is not a linear process, and suffers also from cross talk. To cope with these problems, the identification of a suitable nonlinear model is required. A physical model based on the optical scalar theory is used to identify the kernels of a nonlinear model based on the Volterra series. Both analysis and simulations show that a second order bidimensional model accurately describes the read out process. Once equipped with the Volterra channel model, we evaluate the performance of various nonlinear receivers. First we consider Nonlinear Adaptive Volterra Equalization (NAVE). Simulations show that the performance of classical structures for linear channels is significantly affected by the nonlinear response. The nonlinear NAVE receiver can achieve better performance than Maximum Likelihood Sequence Estimator (MLSE), with lower complexity. An innovative Nonlinear Maximum Likelihood Sequence Estimator (NMLSE), based on the combination of MLSE and nonlinear Inter-Symbol Interference (ISI) cancellation, is presented. NMLSE offers significant advantages with respect to traditional MLSE, and performs better than traditional equalization for nonlinear channels (like NAVE). Finally, the paper deals with cancellation of cross talk from adjacent tracks. We propose and analyze an adaptive nonlinear cross talk canceller based on a three spot detection system. For the sake of simplicity, all the performance comparisons presented in this paper are based on the assumption that noise is Additive, White, and Gaussian (AWGN model).
URL: https://global.ieice.org/en_transactions/electronics/10.1587/e85-c_9_1675/_p
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@ARTICLE{e85-c_9_1675,
author={Luigi AGAROSSI, Sandro BELLINI, Pierangelo MIGLIORATI, },
journal={IEICE TRANSACTIONS on Electronics},
title={Effective Nonlinear Receivers for High Density Optical Recording},
year={2002},
volume={E85-C},
number={9},
pages={1675-1683},
abstract={The starting point of this paper is the definition of a nonlinear model of the read out process in high density optical discs. Under high density condition, the signal read out is not a linear process, and suffers also from cross talk. To cope with these problems, the identification of a suitable nonlinear model is required. A physical model based on the optical scalar theory is used to identify the kernels of a nonlinear model based on the Volterra series. Both analysis and simulations show that a second order bidimensional model accurately describes the read out process. Once equipped with the Volterra channel model, we evaluate the performance of various nonlinear receivers. First we consider Nonlinear Adaptive Volterra Equalization (NAVE). Simulations show that the performance of classical structures for linear channels is significantly affected by the nonlinear response. The nonlinear NAVE receiver can achieve better performance than Maximum Likelihood Sequence Estimator (MLSE), with lower complexity. An innovative Nonlinear Maximum Likelihood Sequence Estimator (NMLSE), based on the combination of MLSE and nonlinear Inter-Symbol Interference (ISI) cancellation, is presented. NMLSE offers significant advantages with respect to traditional MLSE, and performs better than traditional equalization for nonlinear channels (like NAVE). Finally, the paper deals with cancellation of cross talk from adjacent tracks. We propose and analyze an adaptive nonlinear cross talk canceller based on a three spot detection system. For the sake of simplicity, all the performance comparisons presented in this paper are based on the assumption that noise is Additive, White, and Gaussian (AWGN model).},
keywords={},
doi={},
ISSN={},
month={September},}
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TY - JOUR
TI - Effective Nonlinear Receivers for High Density Optical Recording
T2 - IEICE TRANSACTIONS on Electronics
SP - 1675
EP - 1683
AU - Luigi AGAROSSI
AU - Sandro BELLINI
AU - Pierangelo MIGLIORATI
PY - 2002
DO -
JO - IEICE TRANSACTIONS on Electronics
SN -
VL - E85-C
IS - 9
JA - IEICE TRANSACTIONS on Electronics
Y1 - September 2002
AB - The starting point of this paper is the definition of a nonlinear model of the read out process in high density optical discs. Under high density condition, the signal read out is not a linear process, and suffers also from cross talk. To cope with these problems, the identification of a suitable nonlinear model is required. A physical model based on the optical scalar theory is used to identify the kernels of a nonlinear model based on the Volterra series. Both analysis and simulations show that a second order bidimensional model accurately describes the read out process. Once equipped with the Volterra channel model, we evaluate the performance of various nonlinear receivers. First we consider Nonlinear Adaptive Volterra Equalization (NAVE). Simulations show that the performance of classical structures for linear channels is significantly affected by the nonlinear response. The nonlinear NAVE receiver can achieve better performance than Maximum Likelihood Sequence Estimator (MLSE), with lower complexity. An innovative Nonlinear Maximum Likelihood Sequence Estimator (NMLSE), based on the combination of MLSE and nonlinear Inter-Symbol Interference (ISI) cancellation, is presented. NMLSE offers significant advantages with respect to traditional MLSE, and performs better than traditional equalization for nonlinear channels (like NAVE). Finally, the paper deals with cancellation of cross talk from adjacent tracks. We propose and analyze an adaptive nonlinear cross talk canceller based on a three spot detection system. For the sake of simplicity, all the performance comparisons presented in this paper are based on the assumption that noise is Additive, White, and Gaussian (AWGN model).
ER -