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
L'algorithme de module constant (CMA) est une méthode largement connue sous le nom de formation de faisceau adaptative aveugle, car elle ne nécessite aucune connaissance du signal, sauf que la forme d'onde du signal transmis a une enveloppe constante. Bien que CMA ait le mérite de cette opération aveugle, elle présente des problèmes dans sa propriété de convergence. Dans cet article, les problèmes inhérents à cet algorithme sont résolus en utilisant une combinaison de CMA et d'un autre algorithme adaptatif majeur SMI (Sample Matrix Inversion). L’idée est d’utiliser SMI pour déterminer les poids initiaux pour le fonctionnement de la CMA. Bien que l’avantage du CMA en tant qu’algorithme aveugle ne soit pas pleinement exploité, de bons aspects du SMI et du CMA peuvent être introduits. En utilisant cette approche, deux problèmes majeurs liés aux propriétés de convergence des CMA peuvent être résolus. L'un de ces problèmes est la fiabilité et cela concerne les performances de convergence dans certains cas. Lorsque le signal interférent est plus fort que le signal souhaité, l’algorithme a tendance à trouver la mauvaise solution en capturant le signal interférent qui a la puissance la plus forte. De plus, le temps de convergence de cet algorithme est lent, limitant son application dans un environnement dynamique, bien que le temps de convergence lent de CMA ait été étudié précédemment et que plusieurs méthodes aient été proposées pour surmonter ce défaut. Grâce à la méthode proposée, la détérioration due à ces deux problèmes peut être atténuée. Les résultats de simulation sont présentés pour confirmer la théorie. De plus, des évaluations sont effectuées concernant les caractéristiques d'évanouissement. La simulation confirme également que les performances de suivi de cette méthode peuvent être considérées comme suffisantes dans les communications mobiles personnelles.
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Rumiko YONEZAWA, Isamu CHIBA, "A Combination of Two Adaptive Algorithms SMI and CMA" in IEICE TRANSACTIONS on Communications,
vol. E84-B, no. 7, pp. 1768-1773, July 2001, doi: .
Abstract: Constant Modulus Algorithm (CMA) is a method that has been widely known as blind adaptive beamforming because it requires no knowledge about the signal except that the transmitted signal waveform has a constant envelope. Although CMA has the merit of this blind operation, it possesses problems in its convergence property. In this paper, problems that are inherent to this algorithm is resolved using a combination of CMA and another major adaptive algorithm SMI (Sample Matrix Inversion). The idea is to use SMI to determine the initial weights for CMA operation. Although the benefit of CMA being a blind algorithm is not fully taken advantage of, good aspects of both SMI and CMA can be introduced. By using this approach, two major problems in convergence properties of CMA can be solved. One of these problems is the reliability and this relates to the convergence performance in certain cases. When the interfering signal is stronger than the desired signal, the algorithm tends to come up with the wrong solution by capturing the interfering signal which has the stronger power. Also, the convergence time of this algorithm is slow, limiting its application in dynamic environment, although the slow convergence time of CMA has been studied previously and several methods have been proposed to overcome this defect. Using the proposed method, the deterioration due to both of these problems can be mitigated. Simulation results are shown to confirm the theory. Furthermore, evaluations are done concerning the fading characteristics. It is also confirmed from the simulation that the tracking performance of this method can be regarded as sufficient in personal mobile communication.
URL: https://global.ieice.org/en_transactions/communications/10.1587/e84-b_7_1768/_p
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@ARTICLE{e84-b_7_1768,
author={Rumiko YONEZAWA, Isamu CHIBA, },
journal={IEICE TRANSACTIONS on Communications},
title={A Combination of Two Adaptive Algorithms SMI and CMA},
year={2001},
volume={E84-B},
number={7},
pages={1768-1773},
abstract={Constant Modulus Algorithm (CMA) is a method that has been widely known as blind adaptive beamforming because it requires no knowledge about the signal except that the transmitted signal waveform has a constant envelope. Although CMA has the merit of this blind operation, it possesses problems in its convergence property. In this paper, problems that are inherent to this algorithm is resolved using a combination of CMA and another major adaptive algorithm SMI (Sample Matrix Inversion). The idea is to use SMI to determine the initial weights for CMA operation. Although the benefit of CMA being a blind algorithm is not fully taken advantage of, good aspects of both SMI and CMA can be introduced. By using this approach, two major problems in convergence properties of CMA can be solved. One of these problems is the reliability and this relates to the convergence performance in certain cases. When the interfering signal is stronger than the desired signal, the algorithm tends to come up with the wrong solution by capturing the interfering signal which has the stronger power. Also, the convergence time of this algorithm is slow, limiting its application in dynamic environment, although the slow convergence time of CMA has been studied previously and several methods have been proposed to overcome this defect. Using the proposed method, the deterioration due to both of these problems can be mitigated. Simulation results are shown to confirm the theory. Furthermore, evaluations are done concerning the fading characteristics. It is also confirmed from the simulation that the tracking performance of this method can be regarded as sufficient in personal mobile communication.},
keywords={},
doi={},
ISSN={},
month={July},}
Copier
TY - JOUR
TI - A Combination of Two Adaptive Algorithms SMI and CMA
T2 - IEICE TRANSACTIONS on Communications
SP - 1768
EP - 1773
AU - Rumiko YONEZAWA
AU - Isamu CHIBA
PY - 2001
DO -
JO - IEICE TRANSACTIONS on Communications
SN -
VL - E84-B
IS - 7
JA - IEICE TRANSACTIONS on Communications
Y1 - July 2001
AB - Constant Modulus Algorithm (CMA) is a method that has been widely known as blind adaptive beamforming because it requires no knowledge about the signal except that the transmitted signal waveform has a constant envelope. Although CMA has the merit of this blind operation, it possesses problems in its convergence property. In this paper, problems that are inherent to this algorithm is resolved using a combination of CMA and another major adaptive algorithm SMI (Sample Matrix Inversion). The idea is to use SMI to determine the initial weights for CMA operation. Although the benefit of CMA being a blind algorithm is not fully taken advantage of, good aspects of both SMI and CMA can be introduced. By using this approach, two major problems in convergence properties of CMA can be solved. One of these problems is the reliability and this relates to the convergence performance in certain cases. When the interfering signal is stronger than the desired signal, the algorithm tends to come up with the wrong solution by capturing the interfering signal which has the stronger power. Also, the convergence time of this algorithm is slow, limiting its application in dynamic environment, although the slow convergence time of CMA has been studied previously and several methods have been proposed to overcome this defect. Using the proposed method, the deterioration due to both of these problems can be mitigated. Simulation results are shown to confirm the theory. Furthermore, evaluations are done concerning the fading characteristics. It is also confirmed from the simulation that the tracking performance of this method can be regarded as sufficient in personal mobile communication.
ER -