Low-Complexity Channel Estimation and Localization with Random Beamspace Observations
Paper in proceeding, 2023

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.

Author

Fan Jiang

Halmstad University

Yu Ge

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Meifang Zhu

Lund University

Henk Wymeersch

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

F. Tufvesson

Lund University

IEEE International Conference on Communications

15503607 (ISSN)

Vol. 2023-May 5985-5990
9781538674628 (ISBN)

2023 IEEE International Conference on Communications, ICC 2023
Rome, Italy,

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Subject Categories

Communication Systems

Control Engineering

DOI

10.1109/ICC45041.2023.10278994

More information

Latest update

10/18/2024