Multi-layer sparse bayesian learning for mmWave channel estimation
Multi-layer sparse bayesian learning for mmWave channel estimation
Millimeter wave (mmWave) communications has been considered one of the key techniques for the future generations of wireless systems due to the large mmWave bandwidth available. In mmWave systems, channel state information (CSI) is critical for the design of the precoder and combiner for operations respectively at transmitter and receiver. In this paper, we motivate to design the low-complexity and high-accuracy channel estimation methods for the mmWave systems employing orthogonal frequency division multiplexing (OFDM) signaling and hybrid transmitter/receiver beamforming. Specifically, a multi-layer sparse bayesian learning (SBL) channel estimator is proposed to both improve the performance of channel estimation and reduce the complexity of signal processing, when compared with a range of related channel estimators, including the orthogonal matching pursuit (OMP)-, approximate message passing (AMP)- and conventional SBL-assisted channel estimators. The proposed multi-layer SBL estimator is compared with these legacy channel estimators, when impacts from different perspectives are considered. Furthermore, the Bayesian Cramer-Rao Bound of channel estimation is analyzed and evaluated. Our studies and simulation results show that the proposed multi-layer SBL estimator is capable of achieving better performance than the benchmark estimators considered. Specifically, when compared with the traditional SBL estimator, the proposed multi-layer SBL estimator is capable of achieving a lower mean-square error (MSE), while simultaneously, requiring only about 1/10 of the computational complexity of the traditional SBL estimator.
Adaptive Codebook, Array signal processing, Channel Estimation, Channel estimation, Computational complexity, Estimation, Millimeter wave communication, Multi-layer Structure, OFDM, Radio frequency, Sparse Bayesian Learning, mmWave
1-14
Zhang, Yaoyuan
6b05d076-c3a9-4e38-90cb-bb89c5ccf265
El-Hajjar, Mohammed
3a829028-a427-4123-b885-2bab81a44b6f
Yang, Lie-Liang
ae425648-d9a3-4b7d-8abd-b3cfea375bc7
13 October 2023
Zhang, Yaoyuan
6b05d076-c3a9-4e38-90cb-bb89c5ccf265
El-Hajjar, Mohammed
3a829028-a427-4123-b885-2bab81a44b6f
Yang, Lie-Liang
ae425648-d9a3-4b7d-8abd-b3cfea375bc7
Zhang, Yaoyuan, El-Hajjar, Mohammed and Yang, Lie-Liang
(2023)
Multi-layer sparse bayesian learning for mmWave channel estimation.
IEEE Transactions on Vehicular Technology, .
(doi:10.1109/TVT.2023.3323677).
Abstract
Millimeter wave (mmWave) communications has been considered one of the key techniques for the future generations of wireless systems due to the large mmWave bandwidth available. In mmWave systems, channel state information (CSI) is critical for the design of the precoder and combiner for operations respectively at transmitter and receiver. In this paper, we motivate to design the low-complexity and high-accuracy channel estimation methods for the mmWave systems employing orthogonal frequency division multiplexing (OFDM) signaling and hybrid transmitter/receiver beamforming. Specifically, a multi-layer sparse bayesian learning (SBL) channel estimator is proposed to both improve the performance of channel estimation and reduce the complexity of signal processing, when compared with a range of related channel estimators, including the orthogonal matching pursuit (OMP)-, approximate message passing (AMP)- and conventional SBL-assisted channel estimators. The proposed multi-layer SBL estimator is compared with these legacy channel estimators, when impacts from different perspectives are considered. Furthermore, the Bayesian Cramer-Rao Bound of channel estimation is analyzed and evaluated. Our studies and simulation results show that the proposed multi-layer SBL estimator is capable of achieving better performance than the benchmark estimators considered. Specifically, when compared with the traditional SBL estimator, the proposed multi-layer SBL estimator is capable of achieving a lower mean-square error (MSE), while simultaneously, requiring only about 1/10 of the computational complexity of the traditional SBL estimator.
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More information
Accepted/In Press date: 9 October 2023
e-pub ahead of print date: 13 October 2023
Published date: 13 October 2023
Additional Information:
Publisher Copyright:
IEEE
Keywords:
Adaptive Codebook, Array signal processing, Channel Estimation, Channel estimation, Computational complexity, Estimation, Millimeter wave communication, Multi-layer Structure, OFDM, Radio frequency, Sparse Bayesian Learning, mmWave
Identifiers
Local EPrints ID: 483087
URI: http://eprints.soton.ac.uk/id/eprint/483087
ISSN: 0018-9545
PURE UUID: 1f70c5bd-53e1-475b-9eab-fe97f313e065
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Date deposited: 23 Oct 2023 16:39
Last modified: 18 Mar 2024 03:51
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Contributors
Author:
Yaoyuan Zhang
Author:
Mohammed El-Hajjar
Author:
Lie-Liang Yang
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