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
Neste artigo, aplicamos a pré-codificação de inversão de canal regularizada estendida para resolver o problema de transmissão de downlink multiantena multiusuário. Diferente da pré-codificação convencional de inversão de canal regularizado, a pré-codificação RCI estendida considera canais não homogêneos, ajusta mais parâmetros de regularização e explora as informações obtidas pela inversão da matriz de covariância do canal. Duas formas de determinar os parâmetros de regularização são investigadas. Primeiro, os parâmetros podem ser determinados resolvendo um problema SINR max-min. As restrições do problema podem ser transformadas em restrições de cone de segunda ordem (SOC). A solução ótima do problema pode ser obtida resolvendo iterativamente um problema de programação em cone de segunda ordem (SOCP). Para reduzir a complexidade computacional, um algoritmo one-shot é proposto. Em segundo lugar, o problema da maximização da taxa de soma é discutido. O método simples baseado em gradiente é usado para resolver o problema e obter os parâmetros de regularização. Os resultados da simulação indicam que os algoritmos propostos exibem melhor desempenho SINR máximo-mínimo e desempenho de taxa de soma em relação à pré-codificação RCI.
Yanqing LIU
Jiangxi University of Finance and Economics,Baylor University
Liyun DAI
Jiangxi University of Finance and Economics
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Yanqing LIU, Liyun DAI, "Multiuser Multiantenna Downlink Transmission Using Extended Regularized Channel Inversion Precoding" in IEICE TRANSACTIONS on Communications,
vol. E101-B, no. 12, pp. 2462-2470, December 2018, doi: 10.1587/transcom.2017EBP3417.
Abstract: In this paper, we apply extended regularized channel inversion precoding to address the multiuser multiantenna downlink transmission problem. Different from conventional regularized channel inversion precoding, extended RCI precoding considers non-homogeneous channels, adjusts more regularization parameters, and exploits the information gained by inverting the covariance matrix of the channel. Two ways of determining the regularization parameters are investigated. First, the parameters can be determined by solving a max-min SINR problem. The constraints of the problem can be transformed to the second-order cone (SOC) constraints. The optimal solution of the problem can be obtained by iteratively solving a second-order cone programming (SOCP) problem. In order to reduce the computational complexity, a one-shot algorithm is proposed. Second, the sum-rate maximization problem is discussed. The simple gradient-based method is used to solve the problem and get the regularization parameters. The simulation results indicate that the proposed algorithms exhibit improved max-min SINR performance and sum-rate performance over RCI precoding.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2017EBP3417/_p
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@ARTICLE{e101-b_12_2462,
author={Yanqing LIU, Liyun DAI, },
journal={IEICE TRANSACTIONS on Communications},
title={Multiuser Multiantenna Downlink Transmission Using Extended Regularized Channel Inversion Precoding},
year={2018},
volume={E101-B},
number={12},
pages={2462-2470},
abstract={In this paper, we apply extended regularized channel inversion precoding to address the multiuser multiantenna downlink transmission problem. Different from conventional regularized channel inversion precoding, extended RCI precoding considers non-homogeneous channels, adjusts more regularization parameters, and exploits the information gained by inverting the covariance matrix of the channel. Two ways of determining the regularization parameters are investigated. First, the parameters can be determined by solving a max-min SINR problem. The constraints of the problem can be transformed to the second-order cone (SOC) constraints. The optimal solution of the problem can be obtained by iteratively solving a second-order cone programming (SOCP) problem. In order to reduce the computational complexity, a one-shot algorithm is proposed. Second, the sum-rate maximization problem is discussed. The simple gradient-based method is used to solve the problem and get the regularization parameters. The simulation results indicate that the proposed algorithms exhibit improved max-min SINR performance and sum-rate performance over RCI precoding.},
keywords={},
doi={10.1587/transcom.2017EBP3417},
ISSN={1745-1345},
month={December},}
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TY - JOUR
TI - Multiuser Multiantenna Downlink Transmission Using Extended Regularized Channel Inversion Precoding
T2 - IEICE TRANSACTIONS on Communications
SP - 2462
EP - 2470
AU - Yanqing LIU
AU - Liyun DAI
PY - 2018
DO - 10.1587/transcom.2017EBP3417
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E101-B
IS - 12
JA - IEICE TRANSACTIONS on Communications
Y1 - December 2018
AB - In this paper, we apply extended regularized channel inversion precoding to address the multiuser multiantenna downlink transmission problem. Different from conventional regularized channel inversion precoding, extended RCI precoding considers non-homogeneous channels, adjusts more regularization parameters, and exploits the information gained by inverting the covariance matrix of the channel. Two ways of determining the regularization parameters are investigated. First, the parameters can be determined by solving a max-min SINR problem. The constraints of the problem can be transformed to the second-order cone (SOC) constraints. The optimal solution of the problem can be obtained by iteratively solving a second-order cone programming (SOCP) problem. In order to reduce the computational complexity, a one-shot algorithm is proposed. Second, the sum-rate maximization problem is discussed. The simple gradient-based method is used to solve the problem and get the regularization parameters. The simulation results indicate that the proposed algorithms exhibit improved max-min SINR performance and sum-rate performance over RCI precoding.
ER -