Bayesian block-sparse channel estimation for large-scale MISO-OFDM systems

Hailin Li; Feng Li; Shuyuan Li

doi:10.1109/VTCSpring.2016.7504144

Bayesian block-sparse channel estimation for large-scale MISO-OFDM systems

Hailin Li
, Feng Li
, Shuyuan Li

Faculty of Electronic and Information Engineering

Xi'an Jiaotong University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This letter studies a new method based on Bayesian variational inference to estimate the sparse channel parameters in large-scale multiple-input-single-output orthogonal frequency division multiplexing (MISO-OFDM) systems. Also, the sparse common support of different channel impulse responses, which results in a block- structured model, is considered. The covariance matrix of the block is introduced in the block-structured model to effectively recover the channel parameters combining with the Bayesian hierarchical structure. Furthermore, variational message-passing (VMP) is applied to slove the problem. The simulation results show that the proposed algorithm outperforms the traditional ones.

Original language	English
Title of host publication	2016 IEEE 83rd Vehicular Technology Conference, VTC Spring 2016 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781509016983
DOIs	https://doi.org/10.1109/VTCSpring.2016.7504144
State	Published - 5 Jul 2016
Event	83rd IEEE Vehicular Technology Conference, VTC Spring 2016 - Nanjing, China Duration: 15 May 2016 → 18 May 2016

Publication series

Name	IEEE Vehicular Technology Conference
Volume	2016-July
ISSN (Print)	1550-2252

Conference

Conference	83rd IEEE Vehicular Technology Conference, VTC Spring 2016
Country/Territory	China
City	Nanjing
Period	15/05/16 → 18/05/16

Keywords

Block sparse
Multiple-input-single-output (MISO)
OFDM
Variational Bayesian inference
Variational message-passing (VMP)

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10.1109/VTCSpring.2016.7504144

Cite this

@inproceedings{dd23dc928f244503a87ede96aec30b0c,

title = "Bayesian block-sparse channel estimation for large-scale MISO-OFDM systems",

abstract = "This letter studies a new method based on Bayesian variational inference to estimate the sparse channel parameters in large-scale multiple-input-single-output orthogonal frequency division multiplexing (MISO-OFDM) systems. Also, the sparse common support of different channel impulse responses, which results in a block- structured model, is considered. The covariance matrix of the block is introduced in the block-structured model to effectively recover the channel parameters combining with the Bayesian hierarchical structure. Furthermore, variational message-passing (VMP) is applied to slove the problem. The simulation results show that the proposed algorithm outperforms the traditional ones.",

keywords = "Block sparse, Multiple-input-single-output (MISO), OFDM, Variational Bayesian inference, Variational message-passing (VMP)",

author = "Hailin Li and Feng Li and Shuyuan Li",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 83rd IEEE Vehicular Technology Conference, VTC Spring 2016 ; Conference date: 15-05-2016 Through 18-05-2016",

year = "2016",

month = jul,

day = "5",

doi = "10.1109/VTCSpring.2016.7504144",

language = "英语",

series = "IEEE Vehicular Technology Conference",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2016 IEEE 83rd Vehicular Technology Conference, VTC Spring 2016 - Proceedings",

}

Li, H, Li, F & Li, S 2016, Bayesian block-sparse channel estimation for large-scale MISO-OFDM systems. in 2016 IEEE 83rd Vehicular Technology Conference, VTC Spring 2016 - Proceedings., 7504144, IEEE Vehicular Technology Conference, vol. 2016-July, Institute of Electrical and Electronics Engineers Inc., 83rd IEEE Vehicular Technology Conference, VTC Spring 2016, Nanjing, China, 15/05/16. https://doi.org/10.1109/VTCSpring.2016.7504144

Bayesian block-sparse channel estimation for large-scale MISO-OFDM systems. / Li, Hailin; Li, Feng; Li, Shuyuan.
2016 IEEE 83rd Vehicular Technology Conference, VTC Spring 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2016. 7504144 (IEEE Vehicular Technology Conference; Vol. 2016-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Bayesian block-sparse channel estimation for large-scale MISO-OFDM systems

AU - Li, Hailin

AU - Li, Feng

AU - Li, Shuyuan

PY - 2016/7/5

Y1 - 2016/7/5

N2 - This letter studies a new method based on Bayesian variational inference to estimate the sparse channel parameters in large-scale multiple-input-single-output orthogonal frequency division multiplexing (MISO-OFDM) systems. Also, the sparse common support of different channel impulse responses, which results in a block- structured model, is considered. The covariance matrix of the block is introduced in the block-structured model to effectively recover the channel parameters combining with the Bayesian hierarchical structure. Furthermore, variational message-passing (VMP) is applied to slove the problem. The simulation results show that the proposed algorithm outperforms the traditional ones.

AB - This letter studies a new method based on Bayesian variational inference to estimate the sparse channel parameters in large-scale multiple-input-single-output orthogonal frequency division multiplexing (MISO-OFDM) systems. Also, the sparse common support of different channel impulse responses, which results in a block- structured model, is considered. The covariance matrix of the block is introduced in the block-structured model to effectively recover the channel parameters combining with the Bayesian hierarchical structure. Furthermore, variational message-passing (VMP) is applied to slove the problem. The simulation results show that the proposed algorithm outperforms the traditional ones.

KW - Block sparse

KW - Multiple-input-single-output (MISO)

KW - OFDM

KW - Variational Bayesian inference

KW - Variational message-passing (VMP)

UR - https://www.scopus.com/pages/publications/84979779180

U2 - 10.1109/VTCSpring.2016.7504144

DO - 10.1109/VTCSpring.2016.7504144

M3 - 会议稿件

AN - SCOPUS:84979779180

T3 - IEEE Vehicular Technology Conference

BT - 2016 IEEE 83rd Vehicular Technology Conference, VTC Spring 2016 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 83rd IEEE Vehicular Technology Conference, VTC Spring 2016

Y2 - 15 May 2016 through 18 May 2016

ER -

Bayesian block-sparse channel estimation for large-scale MISO-OFDM systems

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Keywords

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