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
Um algoritmo distribuído que utiliza o combinador MVDR (resposta sem distorção de variância mínima) e a informação local para ajustar a potência e o peso (combinação de peso) é proposto para diversidade conjunta e controle de potência em redes sem fio. O algoritmo proposto limita a potência de acordo com uma restrição e tem a propriedade de que se o requisito SINR (relação sinal-interferência e ruído) for alcançado, então a condição desta realização será mantida após cada tempo discreto. Provamos que a potência e o peso do algoritmo proposto convergem para a potência e o peso viáveis ótimos que minimizam o consumo total de energia. Os resultados da simulação também mostram que o combinador MVDR obtém menor nível médio de potência por usuário e maior probabilidade de sucesso no atendimento ao requisito SINR do que outros combinadores.
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Jui Teng WANG, "Distributed Joint Diversity and Power Control for Wireless Networks" in IEICE TRANSACTIONS on Communications,
vol. E91-B, no. 6, pp. 1962-1969, June 2008, doi: 10.1093/ietcom/e91-b.6.1962.
Abstract: A distributed algorithm that uses the MVDR (minimum variance distortionless response) combiner and the local information to adjust the power and weight (combining weight) is proposed for joint diversity and power control in wireless networks. The proposed algorithm limits the power according to a constraint and has the property that if the SINR (signal to interference and noise ratio) requirement is achieved, then the condition of this achievement will hold after every discrete time. We prove that the power and weight of the proposed algorithm converge to the optimal feasible power and weight that minimize the total power consumption. Simulation results also show that the MVDR combiner gets lower average power level per user and higher probability of success in meeting the SINR requirement than other combiners.
URL: https://global.ieice.org/en_transactions/communications/10.1093/ietcom/e91-b.6.1962/_p
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@ARTICLE{e91-b_6_1962,
author={Jui Teng WANG, },
journal={IEICE TRANSACTIONS on Communications},
title={Distributed Joint Diversity and Power Control for Wireless Networks},
year={2008},
volume={E91-B},
number={6},
pages={1962-1969},
abstract={A distributed algorithm that uses the MVDR (minimum variance distortionless response) combiner and the local information to adjust the power and weight (combining weight) is proposed for joint diversity and power control in wireless networks. The proposed algorithm limits the power according to a constraint and has the property that if the SINR (signal to interference and noise ratio) requirement is achieved, then the condition of this achievement will hold after every discrete time. We prove that the power and weight of the proposed algorithm converge to the optimal feasible power and weight that minimize the total power consumption. Simulation results also show that the MVDR combiner gets lower average power level per user and higher probability of success in meeting the SINR requirement than other combiners.},
keywords={},
doi={10.1093/ietcom/e91-b.6.1962},
ISSN={1745-1345},
month={June},}
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TY - JOUR
TI - Distributed Joint Diversity and Power Control for Wireless Networks
T2 - IEICE TRANSACTIONS on Communications
SP - 1962
EP - 1969
AU - Jui Teng WANG
PY - 2008
DO - 10.1093/ietcom/e91-b.6.1962
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E91-B
IS - 6
JA - IEICE TRANSACTIONS on Communications
Y1 - June 2008
AB - A distributed algorithm that uses the MVDR (minimum variance distortionless response) combiner and the local information to adjust the power and weight (combining weight) is proposed for joint diversity and power control in wireless networks. The proposed algorithm limits the power according to a constraint and has the property that if the SINR (signal to interference and noise ratio) requirement is achieved, then the condition of this achievement will hold after every discrete time. We prove that the power and weight of the proposed algorithm converge to the optimal feasible power and weight that minimize the total power consumption. Simulation results also show that the MVDR combiner gets lower average power level per user and higher probability of success in meeting the SINR requirement than other combiners.
ER -