Channel Prediction Techniques for a Multi-User MIMO System in Time-Varying Environments

Kanako YAMAGUCHI; Huu Phu BUI; Yasutaka OGAWA; Toshihiko NISHIMURA; Takeo OHGANE

doi:10.1587/transcom.E97.B.2747

Channel Prediction Techniques for a Multi-User MIMO System in Time-Varying Environments

Kanako YAMAGUCHI, Huu Phu BUI, Yasutaka OGAWA, Toshihiko NISHIMURA, Takeo OHGANE

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Summary :

Although multi-user multiple-input multiple-output (MI-MO) systems provide high data rate transmission, they may suffer from interference. Block diagonalization and eigenbeam-space division multiplexing (E-SDM) can suppress interference. The transmitter needs to determine beamforming weights from channel state information (CSI) to use these techniques. However, MIMO channels change in time-varying environments during the time intervals between when transmission parameters are determined and actual MIMO transmission occurs. The outdated CSI causes interference and seriously degrades the quality of transmission. Channel prediction schemes have been developed to mitigate the effects of outdated CSI. We evaluated the accuracy of prediction of autoregressive (AR)-model-based prediction and Lagrange extrapolation in the presence of channel estimation error. We found that Lagrange extrapolation was easy to implement and that it provided performance comparable to that obtained with the AR-model-based technique.

Publication: IEICE TRANSACTIONS on Communications Vol.E97-B No.12 pp.2747-2755

Publication Date: 2014/12/01

Publicized

Online ISSN: 1745-1345

DOI: 10.1587/transcom.E97.B.2747

Type of Manuscript: PAPER

Category: Antennas and Propagation

Authors

Kanako YAMAGUCHI
  Hokkaido University
Huu Phu BUI
  University of Technology, Vietnam National University
Yasutaka OGAWA
  Hokkaido University
Toshihiko NISHIMURA
  Hokkaido University
Takeo OHGANE
  Hokkaido University

Keyword

channel prediction, multi-user MIMO system, block diagonalization, eigenbeam-space division multiplexing, time-varying environments, AR model, lagrange extrapolation

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Kanako YAMAGUCHI, Huu Phu BUI, Yasutaka OGAWA, Toshihiko NISHIMURA, Takeo OHGANE, "Channel Prediction Techniques for a Multi-User MIMO System in Time-Varying Environments" in IEICE TRANSACTIONS on Communications, vol. E97-B, no. 12, pp. 2747-2755, December 2014, doi: 10.1587/transcom.E97.B.2747.
Abstract: Although multi-user multiple-input multiple-output (MI-MO) systems provide high data rate transmission, they may suffer from interference. Block diagonalization and eigenbeam-space division multiplexing (E-SDM) can suppress interference. The transmitter needs to determine beamforming weights from channel state information (CSI) to use these techniques. However, MIMO channels change in time-varying environments during the time intervals between when transmission parameters are determined and actual MIMO transmission occurs. The outdated CSI causes interference and seriously degrades the quality of transmission. Channel prediction schemes have been developed to mitigate the effects of outdated CSI. We evaluated the accuracy of prediction of autoregressive (AR)-model-based prediction and Lagrange extrapolation in the presence of channel estimation error. We found that Lagrange extrapolation was easy to implement and that it provided performance comparable to that obtained with the AR-model-based technique.
URL: https://globals.ieice.org/en_transactions/communications/10.1587/transcom.E97.B.2747/_p

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@ARTICLE{e97-b_12_2747,
author={Kanako YAMAGUCHI, Huu Phu BUI, Yasutaka OGAWA, Toshihiko NISHIMURA, Takeo OHGANE, },
journal={IEICE TRANSACTIONS on Communications},
title={Channel Prediction Techniques for a Multi-User MIMO System in Time-Varying Environments},
year={2014},
volume={E97-B},
number={12},
pages={2747-2755},
abstract={Although multi-user multiple-input multiple-output (MI-MO) systems provide high data rate transmission, they may suffer from interference. Block diagonalization and eigenbeam-space division multiplexing (E-SDM) can suppress interference. The transmitter needs to determine beamforming weights from channel state information (CSI) to use these techniques. However, MIMO channels change in time-varying environments during the time intervals between when transmission parameters are determined and actual MIMO transmission occurs. The outdated CSI causes interference and seriously degrades the quality of transmission. Channel prediction schemes have been developed to mitigate the effects of outdated CSI. We evaluated the accuracy of prediction of autoregressive (AR)-model-based prediction and Lagrange extrapolation in the presence of channel estimation error. We found that Lagrange extrapolation was easy to implement and that it provided performance comparable to that obtained with the AR-model-based technique.},
keywords={},
doi={10.1587/transcom.E97.B.2747},
ISSN={1745-1345},
month={December},}

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TY - JOUR
TI - Channel Prediction Techniques for a Multi-User MIMO System in Time-Varying Environments
T2 - IEICE TRANSACTIONS on Communications
SP - 2747
EP - 2755
AU - Kanako YAMAGUCHI
AU - Huu Phu BUI
AU - Yasutaka OGAWA
AU - Toshihiko NISHIMURA
AU - Takeo OHGANE
PY - 2014
DO - 10.1587/transcom.E97.B.2747
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E97-B
IS - 12
JA - IEICE TRANSACTIONS on Communications
Y1 - December 2014
AB - Although multi-user multiple-input multiple-output (MI-MO) systems provide high data rate transmission, they may suffer from interference. Block diagonalization and eigenbeam-space division multiplexing (E-SDM) can suppress interference. The transmitter needs to determine beamforming weights from channel state information (CSI) to use these techniques. However, MIMO channels change in time-varying environments during the time intervals between when transmission parameters are determined and actual MIMO transmission occurs. The outdated CSI causes interference and seriously degrades the quality of transmission. Channel prediction schemes have been developed to mitigate the effects of outdated CSI. We evaluated the accuracy of prediction of autoregressive (AR)-model-based prediction and Lagrange extrapolation in the presence of channel estimation error. We found that Lagrange extrapolation was easy to implement and that it provided performance comparable to that obtained with the AR-model-based technique.
ER -