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
Nesta carta, propomos dois algoritmos de pré-codificação computacionalmente eficientes que alcançam desempenho próximo de ML para downlink MIMO multiusuário. Os algoritmos propostos realizam a expansão da árvore após a redução da rede. A primeira expansão completa é tentada selecionando o nó de primeiro nível com uma métrica mínima, constituindo uma métrica de referência. Para encontrar uma sequência ideal, eles visitam iterativamente cada nó e encerram a expansão comparando as métricas do nó com a métrica de referência calculada. Ao fazer isso, eles reduzem significativamente o número de visitas indesejáveis a nós. Simulações de Monte-Carlo mostram que ambos os algoritmos propostos produzem desempenho próximo ao ML com redução considerável na complexidade em comparação com esquemas convencionais, como codificação de esfera.
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Jongsub CHA, Kyungho PARK, Joonhyuk KANG, Hyuncheol PARK, "Low Complexity Tree Searching-Based Iterative Precoding Techniques for Multiuser MIMO Broadcast Channel" in IEICE TRANSACTIONS on Communications,
vol. E91-B, no. 6, pp. 2045-2048, June 2008, doi: 10.1093/ietcom/e91-b.6.2045.
Abstract: In this letter, we propose two computationally efficient precoding algorithms that achieve near-ML performance for multiuser MIMO downlink. The proposed algorithms perform tree expansion after lattice reduction. The first full expansion is tried by selecting the first level node with a minimum metric, constituting a reference metric. To find an optimal sequence, they iteratively visit each node and terminate the expansion by comparing node metrics with the calculated reference metric. By doing this, they significantly reduce the number of undesirable node visit. Monte-Carlo simulations show that both proposed algorithms yield near-ML performance with considerable reduction in complexity compared with that of the conventional schemes such as sphere encoding.
URL: https://global.ieice.org/en_transactions/communications/10.1093/ietcom/e91-b.6.2045/_p
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@ARTICLE{e91-b_6_2045,
author={Jongsub CHA, Kyungho PARK, Joonhyuk KANG, Hyuncheol PARK, },
journal={IEICE TRANSACTIONS on Communications},
title={Low Complexity Tree Searching-Based Iterative Precoding Techniques for Multiuser MIMO Broadcast Channel},
year={2008},
volume={E91-B},
number={6},
pages={2045-2048},
abstract={In this letter, we propose two computationally efficient precoding algorithms that achieve near-ML performance for multiuser MIMO downlink. The proposed algorithms perform tree expansion after lattice reduction. The first full expansion is tried by selecting the first level node with a minimum metric, constituting a reference metric. To find an optimal sequence, they iteratively visit each node and terminate the expansion by comparing node metrics with the calculated reference metric. By doing this, they significantly reduce the number of undesirable node visit. Monte-Carlo simulations show that both proposed algorithms yield near-ML performance with considerable reduction in complexity compared with that of the conventional schemes such as sphere encoding.},
keywords={},
doi={10.1093/ietcom/e91-b.6.2045},
ISSN={1745-1345},
month={June},}
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TY - JOUR
TI - Low Complexity Tree Searching-Based Iterative Precoding Techniques for Multiuser MIMO Broadcast Channel
T2 - IEICE TRANSACTIONS on Communications
SP - 2045
EP - 2048
AU - Jongsub CHA
AU - Kyungho PARK
AU - Joonhyuk KANG
AU - Hyuncheol PARK
PY - 2008
DO - 10.1093/ietcom/e91-b.6.2045
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E91-B
IS - 6
JA - IEICE TRANSACTIONS on Communications
Y1 - June 2008
AB - In this letter, we propose two computationally efficient precoding algorithms that achieve near-ML performance for multiuser MIMO downlink. The proposed algorithms perform tree expansion after lattice reduction. The first full expansion is tried by selecting the first level node with a minimum metric, constituting a reference metric. To find an optimal sequence, they iteratively visit each node and terminate the expansion by comparing node metrics with the calculated reference metric. By doing this, they significantly reduce the number of undesirable node visit. Monte-Carlo simulations show that both proposed algorithms yield near-ML performance with considerable reduction in complexity compared with that of the conventional schemes such as sphere encoding.
ER -