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
Dans cette lettre, nous proposons deux algorithmes de précodage efficaces sur le plan informatique qui atteignent des performances proches du ML pour la liaison descendante MIMO multi-utilisateur. Les algorithmes proposés effectuent une expansion de l’arbre après réduction du réseau. La première expansion complète est tentée en sélectionnant le nœud de premier niveau avec une métrique minimale, constituant une métrique de référence. Pour trouver une séquence optimale, ils visitent chaque nœud de manière itérative et terminent l’expansion en comparant les métriques du nœud avec la métrique de référence calculée. Ce faisant, ils réduisent considérablement le nombre de visites indésirables des nœuds. Les simulations de Monte-Carlo montrent que les deux algorithmes proposés donnent des performances proches du ML avec une réduction considérable de la complexité par rapport à celles des schémas conventionnels tels que le codage par sphère.
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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 -