Enhanced sparse Bayesian learning-based channel estimation for massive MIMO-OFDM systems

Hayder Qahtan Kshash Al-Salihi; Mohammad Reza Nakhai; Tuan Anh Le

doi:10.1109/EuCNC.2017.7980703

Enhanced sparse Bayesian learning-based channel estimation for massive MIMO-OFDM systems

Hayder Qahtan Kshash Al-Salihi, Mohammad Reza Nakhai, Tuan Anh Le

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

4 Citations (Scopus)

254 Downloads (Pure)

Abstract

Pilot contamination limits the potential benefits of massive multiple input multiple output (MIMO) systems. To mitigate pilot contamination, in this paper, an efficient channel estimation approach is proposed for massive MIMO systems, using sparse Bayesian learning (SBL) namely coupled hierarchical Gaussian framework where the sparsity of each coefficient is controlled by its own hyperparameter and the hyperparameters of its immediate neighbours. The simulation results show that the proposed method can reconstruct original channel coefficients more effectively compared to the conventional channel estimators in terms of channel estimation accuracy in the presence of pilot contamination.

Original language	English
Title of host publication	2017 European Conference on Networks and Communications (EuCNC)
Subtitle of host publication	EuCNC 2017 Travel Grant Support
Place of Publication	Oulu, Finland
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781538638736
DOIs	https://doi.org/10.1109/EuCNC.2017.7980703
Publication status	Published - 15 Jun 2017
Event	2017 European Conference on Networks and Communications, EuCNC 2017 - Oulu, Finland Duration: 12 Jun 2017 → 15 Jun 2017

Conference

Conference	2017 European Conference on Networks and Communications, EuCNC 2017
Country/Territory	Finland
City	Oulu
Period	12/06/2017 → 15/06/2017

Keywords

channel estimation
massive MIMO
Sparse Bayesian learning

Access to Document

10.1109/EuCNC.2017.7980703

EuCNC 2017

Cite this

@inbook{8c6e3a892a3e4144a2a18c561b3b1af2,

title = "Enhanced sparse Bayesian learning-based channel estimation for massive MIMO-OFDM systems",

abstract = "Pilot contamination limits the potential benefits of massive multiple input multiple output (MIMO) systems. To mitigate pilot contamination, in this paper, an efficient channel estimation approach is proposed for massive MIMO systems, using sparse Bayesian learning (SBL) namely coupled hierarchical Gaussian framework where the sparsity of each coefficient is controlled by its own hyperparameter and the hyperparameters of its immediate neighbours. The simulation results show that the proposed method can reconstruct original channel coefficients more effectively compared to the conventional channel estimators in terms of channel estimation accuracy in the presence of pilot contamination.",

keywords = "channel estimation, massive MIMO, Sparse Bayesian learning",

author = "Al-Salihi, {Hayder Qahtan Kshash} and Nakhai, {Mohammad Reza} and Le, {Tuan Anh}",

year = "2017",

month = jun,

day = "15",

doi = "10.1109/EuCNC.2017.7980703",

language = "English",

booktitle = "2017 European Conference on Networks and Communications (EuCNC)",

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

note = "2017 European Conference on Networks and Communications, EuCNC 2017 ; Conference date: 12-06-2017 Through 15-06-2017",

}

Al-Salihi, HQK , Nakhai, MR & Le, TA 2017, Enhanced sparse Bayesian learning-based channel estimation for massive MIMO-OFDM systems. in 2017 European Conference on Networks and Communications (EuCNC): EuCNC 2017 Travel Grant Support., 7980703, Institute of Electrical and Electronics Engineers Inc., Oulu, Finland , 2017 European Conference on Networks and Communications, EuCNC 2017, Oulu, Finland, 12/06/2017. https://doi.org/10.1109/EuCNC.2017.7980703

Enhanced sparse Bayesian learning-based channel estimation for massive MIMO-OFDM systems. / Al-Salihi, Hayder Qahtan Kshash ; Nakhai, Mohammad Reza ; Le, Tuan Anh.
2017 European Conference on Networks and Communications (EuCNC): EuCNC 2017 Travel Grant Support. Oulu, Finland : Institute of Electrical and Electronics Engineers Inc., 2017. 7980703.

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

TY - CHAP

T1 - Enhanced sparse Bayesian learning-based channel estimation for massive MIMO-OFDM systems

AU - Al-Salihi, Hayder Qahtan Kshash

AU - Nakhai, Mohammad Reza

AU - Le, Tuan Anh

PY - 2017/6/15

Y1 - 2017/6/15

N2 - Pilot contamination limits the potential benefits of massive multiple input multiple output (MIMO) systems. To mitigate pilot contamination, in this paper, an efficient channel estimation approach is proposed for massive MIMO systems, using sparse Bayesian learning (SBL) namely coupled hierarchical Gaussian framework where the sparsity of each coefficient is controlled by its own hyperparameter and the hyperparameters of its immediate neighbours. The simulation results show that the proposed method can reconstruct original channel coefficients more effectively compared to the conventional channel estimators in terms of channel estimation accuracy in the presence of pilot contamination.

AB - Pilot contamination limits the potential benefits of massive multiple input multiple output (MIMO) systems. To mitigate pilot contamination, in this paper, an efficient channel estimation approach is proposed for massive MIMO systems, using sparse Bayesian learning (SBL) namely coupled hierarchical Gaussian framework where the sparsity of each coefficient is controlled by its own hyperparameter and the hyperparameters of its immediate neighbours. The simulation results show that the proposed method can reconstruct original channel coefficients more effectively compared to the conventional channel estimators in terms of channel estimation accuracy in the presence of pilot contamination.

KW - channel estimation

KW - massive MIMO

KW - Sparse Bayesian learning

UR - http://www.scopus.com/inward/record.url?scp=85039934190&partnerID=8YFLogxK

U2 - 10.1109/EuCNC.2017.7980703

DO - 10.1109/EuCNC.2017.7980703

M3 - Conference paper

AN - SCOPUS:85039934190

BT - 2017 European Conference on Networks and Communications (EuCNC)

PB - Institute of Electrical and Electronics Engineers Inc.

CY - Oulu, Finland

T2 - 2017 European Conference on Networks and Communications, EuCNC 2017

Y2 - 12 June 2017 through 15 June 2017

ER -

Enhanced sparse Bayesian learning-based channel estimation for massive MIMO-OFDM systems

Abstract

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this