IEICE TRANSACTIONS on Communications
Low Complexity Tree Searching-Based Iterative Precoding Techniques for Multiuser MIMO Broadcast Channel
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E91-B No.6 pp.2045-2048
- Publication Date
- 2008/06/01
- Publicized
- Online ISSN
- 1745-1345
- DOI
- 10.1093/ietcom/e91-b.6.2045
- Type of Manuscript
- LETTER
- Category
- Wireless Communication Technologies
Authors
Keyword
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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},}
Copy
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 -
English
