You, Y and Zhang, L orcid.org/0000-0002-4535-3200 (Cover date: February 2022) Exploiting Angular Spread in Channel Estimation of Millimeter Wave MIMO System. IET Communications, 16 (3). pp. 195-205. ISSN 1751-8628
Abstract
Millimeter wave (mmWave) frequency spectrum can mitigate severe spectrum shortage caused by the explosive growth of mobile data demand. To overcome the high propagation loss of mmWave signals, massive multi-input multi-output (MIMO) and hybrid architecture are employed. As in microwave communication systems, channel state information (CSI) is essential to fully achieve the advantages of mmWave communication. However, due to the massive number of antennas and hybrid architecture, the CSI acquisition is challenging. The sparsity of mmWave channel can be utilized to reduce the training overhead. In addition to sparsity, real-world measurements in dense urban propagation environments reveal that the mmWave channel may spread in form of cluster of paths over the angular domains, namely the angular spread. In this paper, it is utilized to formulate the channel estimation as a block-sparse signal recovery problem. The block orthogonal matching pursuit (BOMP) is used to validate the model. Then, block fast Bayesian matching pursuit (BFBMP) algorithm is proposed to solve the above problem. Compared with other existing channel estimation methods, simulation results show that the angular spread feature and the proposed BFBMP can considerably improve the CSI estimation with less complexity.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 The Authors. IET Communications published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Institute of Communication & Power Networks (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 07 Jan 2022 14:00 |
Last Modified: | 15 Feb 2024 02:43 |
Published Version: | https://ietresearch.onlinelibrary.wiley.com/doi/fu... |
Status: | Published |
Publisher: | Institution of Engineering and Technology |
Identification Number: | 10.1049/cmu2.12329 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:182174 |
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