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Low-Complexity Channel Estimation and Localization with Random Beamspace Observations

Jiang, Fan ; Ge, Yu ; Zhu, Meifang LU ; Wymeersch, Henk and Tufvesson, Fredrik LU orcid (2023) 2023 IEEE International Conference on Communications, ICC 2023 In IEEE International Conference on Communications 2023-May. p.5985-5990
Abstract

We investigate the problem of low-complexity, high-dimensional channel estimation with beamspace observations, for the purpose of localization. Existing work on beamspace ESPRIT (estimation of signal parameters via rotational invariance technique) approaches requires either a shift-invariance structure of the transformation matrix, or a full-column rank condition. We extend these beamspace ESPRIT methods to a case when neither of these conditions is satisfied, by exploiting the full-row rank of the transformation matrix. We first develop a tensor decomposition-based approach, and further design a matrix-based ESPRIT method to achieve auto-pairing of the channel parameters, with reduced complexity. Numerical simulations show that the... (More)

We investigate the problem of low-complexity, high-dimensional channel estimation with beamspace observations, for the purpose of localization. Existing work on beamspace ESPRIT (estimation of signal parameters via rotational invariance technique) approaches requires either a shift-invariance structure of the transformation matrix, or a full-column rank condition. We extend these beamspace ESPRIT methods to a case when neither of these conditions is satisfied, by exploiting the full-row rank of the transformation matrix. We first develop a tensor decomposition-based approach, and further design a matrix-based ESPRIT method to achieve auto-pairing of the channel parameters, with reduced complexity. Numerical simulations show that the proposed methods work in the challenging scenario, and the matrix-based ESPRIT approach achieves better performance than the tensor ESPRIT method.

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author
; ; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
ICC 2023 - IEEE International Conference on Communications : Sustainable Communications for Renaissance - Sustainable Communications for Renaissance
series title
IEEE International Conference on Communications
editor
Zorzi, Michele ; Tao, Meixia and Saad, Walid
volume
2023-May
pages
6 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
2023 IEEE International Conference on Communications, ICC 2023
conference location
Rome, Italy
conference dates
2023-05-28 - 2023-06-01
external identifiers
  • scopus:85178280972
ISSN
1550-3607
ISBN
9781538674628
DOI
10.1109/ICC45041.2023.10278994
language
English
LU publication?
yes
additional info
Publisher Copyright: © 2023 IEEE.
id
c15b3cf3-2885-4e66-a2ed-e3bc71cce419
date added to LUP
2024-10-24 12:28:30
date last changed
2025-06-06 08:27:54
@inproceedings{c15b3cf3-2885-4e66-a2ed-e3bc71cce419,
  abstract     = {{<p>We investigate the problem of low-complexity, high-dimensional channel estimation with beamspace observations, for the purpose of localization. Existing work on beamspace ESPRIT (estimation of signal parameters via rotational invariance technique) approaches requires either a shift-invariance structure of the transformation matrix, or a full-column rank condition. We extend these beamspace ESPRIT methods to a case when neither of these conditions is satisfied, by exploiting the full-row rank of the transformation matrix. We first develop a tensor decomposition-based approach, and further design a matrix-based ESPRIT method to achieve auto-pairing of the channel parameters, with reduced complexity. Numerical simulations show that the proposed methods work in the challenging scenario, and the matrix-based ESPRIT approach achieves better performance than the tensor ESPRIT method.</p>}},
  author       = {{Jiang, Fan and Ge, Yu and Zhu, Meifang and Wymeersch, Henk and Tufvesson, Fredrik}},
  booktitle    = {{ICC 2023 - IEEE International Conference on Communications : Sustainable Communications for Renaissance}},
  editor       = {{Zorzi, Michele and Tao, Meixia and Saad, Walid}},
  isbn         = {{9781538674628}},
  issn         = {{1550-3607}},
  language     = {{eng}},
  pages        = {{5985--5990}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  series       = {{IEEE International Conference on Communications}},
  title        = {{Low-Complexity Channel Estimation and Localization with Random Beamspace Observations}},
  url          = {{http://dx.doi.org/10.1109/ICC45041.2023.10278994}},
  doi          = {{10.1109/ICC45041.2023.10278994}},
  volume       = {{2023-May}},
  year         = {{2023}},
}