Iterative Channel Estimation in MIMO Antenna Selection Systems for Correlated Gauss-Markov Channel
Summary :
We address the issue of MIMO channel estimation with the aid of a priori temporal correlation statistics of the channel as well as the spatial correlation. The temporal correlations are incorporated to the estimation scheme by assuming the Gauss-Markov channel model. Under the MMSE criteria, the Kalman filter performs an iterative optimal estimation. To take advantage of the enhanced estimation capability, we focus on the problem of channel estimation from a partial channel measurement in the MIMO antenna selection system. We discuss the optimal training sequence design, and also the optimal antenna subset selection for channel measurement based on the statistics. In a highly correlated channel, the estimation works even when the measurements from some antenna elements are omitted at each fading block.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E92-B No.3 pp.922-932
- Publication Date
- 2009/03/01
- Publicized
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.E92.B.922
- Type of Manuscript
- PAPER
- Category
- Wireless Communication Technologies
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Yousuke NARUSE, Jun-ichi TAKADA, "Iterative Channel Estimation in MIMO Antenna Selection Systems for Correlated Gauss-Markov Channel" in IEICE TRANSACTIONS on Communications,
vol. E92-B, no. 3, pp. 922-932, March 2009, doi: 10.1587/transcom.E92.B.922.
Abstract: We address the issue of MIMO channel estimation with the aid of a priori temporal correlation statistics of the channel as well as the spatial correlation. The temporal correlations are incorporated to the estimation scheme by assuming the Gauss-Markov channel model. Under the MMSE criteria, the Kalman filter performs an iterative optimal estimation. To take advantage of the enhanced estimation capability, we focus on the problem of channel estimation from a partial channel measurement in the MIMO antenna selection system. We discuss the optimal training sequence design, and also the optimal antenna subset selection for channel measurement based on the statistics. In a highly correlated channel, the estimation works even when the measurements from some antenna elements are omitted at each fading block.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.E92.B.922/_p
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@ARTICLE{e92-b_3_922,
author={Yousuke NARUSE, Jun-ichi TAKADA, },
journal={IEICE TRANSACTIONS on Communications},
title={Iterative Channel Estimation in MIMO Antenna Selection Systems for Correlated Gauss-Markov Channel},
year={2009},
volume={E92-B},
number={3},
pages={922-932},
abstract={We address the issue of MIMO channel estimation with the aid of a priori temporal correlation statistics of the channel as well as the spatial correlation. The temporal correlations are incorporated to the estimation scheme by assuming the Gauss-Markov channel model. Under the MMSE criteria, the Kalman filter performs an iterative optimal estimation. To take advantage of the enhanced estimation capability, we focus on the problem of channel estimation from a partial channel measurement in the MIMO antenna selection system. We discuss the optimal training sequence design, and also the optimal antenna subset selection for channel measurement based on the statistics. In a highly correlated channel, the estimation works even when the measurements from some antenna elements are omitted at each fading block.},
keywords={},
doi={10.1587/transcom.E92.B.922},
ISSN={1745-1345},
month={March},}
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TY - JOUR
TI - Iterative Channel Estimation in MIMO Antenna Selection Systems for Correlated Gauss-Markov Channel
T2 - IEICE TRANSACTIONS on Communications
SP - 922
EP - 932
AU - Yousuke NARUSE
AU - Jun-ichi TAKADA
PY - 2009
DO - 10.1587/transcom.E92.B.922
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E92-B
IS - 3
JA - IEICE TRANSACTIONS on Communications
Y1 - March 2009
AB - We address the issue of MIMO channel estimation with the aid of a priori temporal correlation statistics of the channel as well as the spatial correlation. The temporal correlations are incorporated to the estimation scheme by assuming the Gauss-Markov channel model. Under the MMSE criteria, the Kalman filter performs an iterative optimal estimation. To take advantage of the enhanced estimation capability, we focus on the problem of channel estimation from a partial channel measurement in the MIMO antenna selection system. We discuss the optimal training sequence design, and also the optimal antenna subset selection for channel measurement based on the statistics. In a highly correlated channel, the estimation works even when the measurements from some antenna elements are omitted at each fading block.
ER -
English
