Near-Field Channel Modeling and Parametric Estimation in Upper mmWave Band
Djelouat, Hamza; Das, Shrayan; Kyösti, Pekka; Juntti, Markku (2026-03-26)
IEEE
26.03.2026
H. Djelouat, S. Das, P. Kyösti and M. Juntti, "Near-Field Channel Modeling and Parametric Estimation in Upper mmWave Band," 2025 59th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA, 2025, pp. 1790-1794, doi: 10.1109/IEEECONF67917.2025.11443924
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© 2026 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists,or reuse of any copyrighted component of this work in other works.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202604232755
https://urn.fi/URN:NBN:fi:oulu-202604232755
Tiivistelmä
Abstract
Accurate mmWave channel modeling is crucial for reliable parametric channel estimation in extremely large multiple-input multiple-output (XL-MIMO) systems. Deploying XL-MIMO in high-frequency bands not only increases the number of antennas but fundamentally transforms channel propagation characteristics, placing user equipment (UE) in the radiative near-field of the base station. This paper presents a dual contribution: 1) we develop a comprehensive near-field channel model for the upper band mmWave communications that accurately captures spherical wave propagation unique to high-frequency systems; 2) we propose a novel two-stage parametric channel estimation framework that leverages the inherent sparse representation of the near-field channel. Our approach begins with a sparse Bayesian learning (SBL) algorithm for coarse complex coefficient estimation, followed by high-resolution off-grid refinement to precisely extract the angles of arrival and UE location coordinates.
Accurate mmWave channel modeling is crucial for reliable parametric channel estimation in extremely large multiple-input multiple-output (XL-MIMO) systems. Deploying XL-MIMO in high-frequency bands not only increases the number of antennas but fundamentally transforms channel propagation characteristics, placing user equipment (UE) in the radiative near-field of the base station. This paper presents a dual contribution: 1) we develop a comprehensive near-field channel model for the upper band mmWave communications that accurately captures spherical wave propagation unique to high-frequency systems; 2) we propose a novel two-stage parametric channel estimation framework that leverages the inherent sparse representation of the near-field channel. Our approach begins with a sparse Bayesian learning (SBL) algorithm for coarse complex coefficient estimation, followed by high-resolution off-grid refinement to precisely extract the angles of arrival and UE location coordinates.
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