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
Para a estimativa cega de sequências de pseudo-ruído (PN) de sinal de espectro de sequência direta de código curto (DSSS), o algoritmo de decomposição de autovalor (EVD), o algoritmo de decomposição de valor singular (SVD) e o rastreamento de subespaço de aproximação de projeção dupla periódica com O algoritmo de deflação (DPASTd) é frequentemente usado para estimar a sequência PN. No entanto, quando o atraso de tempo assíncrono é desconhecido, o maior autovalor e o segundo maior autovalor podem estar muito próximos, resultando no maior autovetor estimado sendo qualquer combinação linear diferente de zero do maior autovetor realmente necessário e do segundo maior autovetor realmente necessário. Em outras palavras, o maior autovetor estimado apresenta ambiguidade unitária. Isso degrada o desempenho de qualquer algoritmo que estima a sequência PN a partir do maior autovetor estimado. Para resolver este problema, este artigo propõe um algoritmo de estimativa cega de sequência de espalhamento baseado na matriz de rotação. Em primeiro lugar, o sinal recebido é dividido em vetores temporais de comprimento de dois períodos de informação sobrepostos por um período de informação. O algoritmo SVD ou DPASTd pode então ser aplicado para obter o maior autovetor e o segundo maior autovetor. A matriz composta pelo maior autovetor e pelo segundo maior autovetor pode ser girada pela matriz de rotação para eliminar qualquer ambiguidade unitária. Desta forma, a melhor estimativa da sequência PN pode ser obtida. Os resultados da simulação mostram que o algoritmo proposto não apenas resolve o problema de estimar a sequência PN quando o maior autovalor e o segundo maior autovalor estão próximos, mas também tem um bom desempenho em valores baixos de relação sinal-ruído (SNR).
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Kejun LI, Yong GAO, "Using the Rotation Matrix to Eliminate the Unitary Ambiguity in the Blind Estimation of Short-Code DSSS Signal Pseudo-Code" in IEICE TRANSACTIONS on Communications,
vol. E103-B, no. 9, pp. 979-988, September 2020, doi: 10.1587/transcom.2019EBP3147.
Abstract: For the blind estimation of short-code direct sequence spread spectrum (DSSS) signal pseudo-noise (PN) sequences, the eigenvalue decomposition (EVD) algorithm, the singular value decomposition (SVD) algorithm and the double-periodic projection approximation subspace tracking with deflation (DPASTd) algorithm are often used to estimate the PN sequence. However, when the asynchronous time delay is unknown, the largest eigenvalue and the second largest eigenvalue may be very close, resulting in the estimated largest eigenvector being any non-zero linear combination of the really required largest eigenvector and the really required second largest eigenvector. In other words, the estimated largest eigenvector exhibits unitary ambiguity. This degrades the performance of any algorithm estimating the PN sequence from the estimated largest eigenvector. To tackle this problem, this paper proposes a spreading sequence blind estimation algorithm based on the rotation matrix. First of all, the received signal is divided into two-information-period-length temporal vectors overlapped by one-information-period. The SVD or DPASTd algorithm can then be applied to obtain the largest eigenvector and the second largest eigenvector. The matrix composed of the largest eigenvector and the second largest eigenvector can be rotated by the rotation matrix to eliminate any unitary ambiguity. In this way, the best estimation of the PN sequence can be obtained. Simulation results show that the proposed algorithm not only solves the problem of estimating the PN sequence when the largest eigenvalue and the second largest eigenvalue are close, but also performs well at low signal-to-noise ratio (SNR) values.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2019EBP3147/_p
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@ARTICLE{e103-b_9_979,
author={Kejun LI, Yong GAO, },
journal={IEICE TRANSACTIONS on Communications},
title={Using the Rotation Matrix to Eliminate the Unitary Ambiguity in the Blind Estimation of Short-Code DSSS Signal Pseudo-Code},
year={2020},
volume={E103-B},
number={9},
pages={979-988},
abstract={For the blind estimation of short-code direct sequence spread spectrum (DSSS) signal pseudo-noise (PN) sequences, the eigenvalue decomposition (EVD) algorithm, the singular value decomposition (SVD) algorithm and the double-periodic projection approximation subspace tracking with deflation (DPASTd) algorithm are often used to estimate the PN sequence. However, when the asynchronous time delay is unknown, the largest eigenvalue and the second largest eigenvalue may be very close, resulting in the estimated largest eigenvector being any non-zero linear combination of the really required largest eigenvector and the really required second largest eigenvector. In other words, the estimated largest eigenvector exhibits unitary ambiguity. This degrades the performance of any algorithm estimating the PN sequence from the estimated largest eigenvector. To tackle this problem, this paper proposes a spreading sequence blind estimation algorithm based on the rotation matrix. First of all, the received signal is divided into two-information-period-length temporal vectors overlapped by one-information-period. The SVD or DPASTd algorithm can then be applied to obtain the largest eigenvector and the second largest eigenvector. The matrix composed of the largest eigenvector and the second largest eigenvector can be rotated by the rotation matrix to eliminate any unitary ambiguity. In this way, the best estimation of the PN sequence can be obtained. Simulation results show that the proposed algorithm not only solves the problem of estimating the PN sequence when the largest eigenvalue and the second largest eigenvalue are close, but also performs well at low signal-to-noise ratio (SNR) values.},
keywords={},
doi={10.1587/transcom.2019EBP3147},
ISSN={1745-1345},
month={September},}
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TY - JOUR
TI - Using the Rotation Matrix to Eliminate the Unitary Ambiguity in the Blind Estimation of Short-Code DSSS Signal Pseudo-Code
T2 - IEICE TRANSACTIONS on Communications
SP - 979
EP - 988
AU - Kejun LI
AU - Yong GAO
PY - 2020
DO - 10.1587/transcom.2019EBP3147
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E103-B
IS - 9
JA - IEICE TRANSACTIONS on Communications
Y1 - September 2020
AB - For the blind estimation of short-code direct sequence spread spectrum (DSSS) signal pseudo-noise (PN) sequences, the eigenvalue decomposition (EVD) algorithm, the singular value decomposition (SVD) algorithm and the double-periodic projection approximation subspace tracking with deflation (DPASTd) algorithm are often used to estimate the PN sequence. However, when the asynchronous time delay is unknown, the largest eigenvalue and the second largest eigenvalue may be very close, resulting in the estimated largest eigenvector being any non-zero linear combination of the really required largest eigenvector and the really required second largest eigenvector. In other words, the estimated largest eigenvector exhibits unitary ambiguity. This degrades the performance of any algorithm estimating the PN sequence from the estimated largest eigenvector. To tackle this problem, this paper proposes a spreading sequence blind estimation algorithm based on the rotation matrix. First of all, the received signal is divided into two-information-period-length temporal vectors overlapped by one-information-period. The SVD or DPASTd algorithm can then be applied to obtain the largest eigenvector and the second largest eigenvector. The matrix composed of the largest eigenvector and the second largest eigenvector can be rotated by the rotation matrix to eliminate any unitary ambiguity. In this way, the best estimation of the PN sequence can be obtained. Simulation results show that the proposed algorithm not only solves the problem of estimating the PN sequence when the largest eigenvalue and the second largest eigenvalue are close, but also performs well at low signal-to-noise ratio (SNR) values.
ER -