University of Oulu

M. Umar Aminu, M. Codreanu and M. Juntti, "Bayesian Learning Based Millimeter-Wave Sparse Channel Estimation with Hybrid Antenna Array," 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Kalamata, 2018, pp. 1-5. doi: 10.1109/SPAWC.2018.8445972

Bayesian learning based millimeter-wave sparse channel estimation with hybrid antenna array

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Author: Aminu, Mubarak Umar1; Codreanu, Marian1; Juntti, Markku1
Organizations: 1Centre for Wireless Communications, University of Oulu P.O. Box 4500, 90014 University of Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.4 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe202002246195
Language: English
Published: Available from Curran Associates, 2018
Publish Date: 2020-02-24
Description:

Abstract

We consider the problem of millimeter-wave (mmWave) channel estimation with a hybrid digital-analog two-stage beamforming structure. A radio frequency (RF) chain excites a dedicated set of antenna subarrays. To compensate for the severe path loss, known training signals are beamformed and swept to scan the angular space. Since the mmWave channels typically exhibit sparsity, the channel response can usually be expressed as a linear combination of a small number of scattering clusters. Thereby the number of angles of arrival (AoAs) and angles of departure (AoDs) with significant signal components is limited, and compressive sensing techniques can be leveraged for estimating the channel. In this paper, we investigate two sparse recovery algorithms: a Bayesian and non-Bayesian one. In the Bayesian approach, we invoke the sparse Bayesian learning (SBL) framework, which relies on a 2-layer hierarchical prior model for channel. A highly efficient and fast iterative Bayesian inference method is then applied to the proposed model. The non-Bayesian approach is a LASSO-based approach, where we devise a low complexity solution by adopting alternating directions method of multipliers (ADMM) technique to solve the problem. The efficacy of the proposed algorithms is demonstrated using numerical examples. The Bayesian approach shows improved estimation performance in relation to the non-Bayesian approach.

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Series: IEEE International Workshop on Signal Processing Advances in Wireless Communications
ISSN: 2325-3789
ISSN-L: 2325-3789
ISBN: 978-1-5386-3512-4
ISBN Print: 978-1-5386-3513-1
Pages: 1 - 5
Article number: 8445972
DOI: 10.1109/SPAWC.2018.8445972
OADOI: https://oadoi.org/10.1109/SPAWC.2018.8445972
Host publication: 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)
Conference: IEEE International Workshop on Signal Processing Advances in Wireless Communications
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
Subjects:
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