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
Les détecteurs non cohérents destinés à l'acquisition de signaux à spectre étalé à séquence directe (DS/SS) modulés par données sont examinés dans cet article. En tenant compte de la modulation des données et de l'incertitude temporelle et en utilisant les approches de détection du maximum de vraisemblance généralisée (GML) ou du maximum de vraisemblance (ML), nous dérivons des détecteurs optimaux au sens de Bayes ou de Neyman-Pearson et proposons divers détecteurs sous-optimaux. Un moyen systématique simple pour leur réalisation est suggéré et les performances numériques de ces détecteurs sont présentées. Nous comparons également leurs performances avec celles du détecteur à combinaison non cohérente (NC1) proposé pour répondre au même besoin. Les résultats numériques montrent que même les détecteurs sous-optimaux proposés peuvent surpasser le détecteur NC1 dans la plupart des cas intéressants.
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Ru-Chwen WU, Yu Ted SU, Wen-Chang LIN, "Noncoherent Detectors for PN Code Acquisition in the Presence of Data Modulation" in IEICE TRANSACTIONS on Communications,
vol. E83-B, no. 11, pp. 2455-2463, November 2000, doi: .
Abstract: Noncoherent detectors for use in acquiring data-modulated direct-sequence spread-spectrum (DS/SS) signals are considered in this paper. Taking data modulation and timing uncertainty into account and using the generalized maximum likelihood (GML) or maximum likelihood (ML) detection approaches, we derive optimal detectors in the sense of Bayes or Neyman-Pearson and propose various suboptimal detectors. A simple systematic means for their realization is suggested and the numerical performance of these detectors is presented. We also compare their performance with that of the noncoherent combining (NC1) detector that had been proposed to serve the same need. Numerical results show that even the proposed suboptimal detectors can outperform the NC1 detector in most cases of interest.
URL: https://global.ieice.org/en_transactions/communications/10.1587/e83-b_11_2455/_p
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@ARTICLE{e83-b_11_2455,
author={Ru-Chwen WU, Yu Ted SU, Wen-Chang LIN, },
journal={IEICE TRANSACTIONS on Communications},
title={Noncoherent Detectors for PN Code Acquisition in the Presence of Data Modulation},
year={2000},
volume={E83-B},
number={11},
pages={2455-2463},
abstract={Noncoherent detectors for use in acquiring data-modulated direct-sequence spread-spectrum (DS/SS) signals are considered in this paper. Taking data modulation and timing uncertainty into account and using the generalized maximum likelihood (GML) or maximum likelihood (ML) detection approaches, we derive optimal detectors in the sense of Bayes or Neyman-Pearson and propose various suboptimal detectors. A simple systematic means for their realization is suggested and the numerical performance of these detectors is presented. We also compare their performance with that of the noncoherent combining (NC1) detector that had been proposed to serve the same need. Numerical results show that even the proposed suboptimal detectors can outperform the NC1 detector in most cases of interest.},
keywords={},
doi={},
ISSN={},
month={November},}
Copier
TY - JOUR
TI - Noncoherent Detectors for PN Code Acquisition in the Presence of Data Modulation
T2 - IEICE TRANSACTIONS on Communications
SP - 2455
EP - 2463
AU - Ru-Chwen WU
AU - Yu Ted SU
AU - Wen-Chang LIN
PY - 2000
DO -
JO - IEICE TRANSACTIONS on Communications
SN -
VL - E83-B
IS - 11
JA - IEICE TRANSACTIONS on Communications
Y1 - November 2000
AB - Noncoherent detectors for use in acquiring data-modulated direct-sequence spread-spectrum (DS/SS) signals are considered in this paper. Taking data modulation and timing uncertainty into account and using the generalized maximum likelihood (GML) or maximum likelihood (ML) detection approaches, we derive optimal detectors in the sense of Bayes or Neyman-Pearson and propose various suboptimal detectors. A simple systematic means for their realization is suggested and the numerical performance of these detectors is presented. We also compare their performance with that of the noncoherent combining (NC1) detector that had been proposed to serve the same need. Numerical results show that even the proposed suboptimal detectors can outperform the NC1 detector in most cases of interest.
ER -