Low-Complexity Channel Estimation and Localization with Random Beamspace Observations
Paper i 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.

Författare

Fan Jiang

Högskolan i Halmstad

Yu Ge

Chalmers, Elektroteknik, Kommunikation, Antenner och Optiska Nätverk

Meifang Zhu

Lunds universitet

Henk Wymeersch

Chalmers, Elektroteknik, Kommunikation, Antenner och Optiska Nätverk

F. Tufvesson

Lunds universitet

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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Ämneskategorier

Kommunikationssystem

Reglerteknik

DOI

10.1109/ICC45041.2023.10278994

Mer information

Senast uppdaterat

2024-10-18