Near-Field Beam Training: Joint Angle and Range Estimation With DFT Codebook

Prior works on near-field beam training have mostly assumed dedicated polar-domain codebook and on-grid range estimation, which, however, may suffer long training overhead, high codebook storage requirement, and degraded estimation accuracy. To address these issues, we propose in this paper new and...

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Published inIEEE transactions on wireless communications Vol. 23; no. 9; pp. 11890 - 11903
Main Authors Wu, Xun, You, Changsheng, Li, Jiapeng, Zhang, Yunpu
Format Journal Article
LanguageEnglish
Published New York IEEE 01.09.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Accuracy
Angles (geometry)
Antennas
Array signal processing
beam training
Beamforming
Discrete Fourier transforms
Estimates
Estimation
Extremely large-scale array (XL-array)
Far fields
Fourier transforms
Near fields
near-field communication
Pattern analysis
Signal to noise ratio
Training
Wireless communication
Online AccessGet full text
ISSN1536-1276
1558-2248
DOI10.1109/TWC.2024.3385749

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Abstract Prior works on near-field beam training have mostly assumed dedicated polar-domain codebook and on-grid range estimation, which, however, may suffer long training overhead, high codebook storage requirement, and degraded estimation accuracy. To address these issues, we propose in this paper new and efficient beam training schemes with off-grid range estimation by using conventional discrete Fourier transform (DFT) codebook. Specifically, we first analyze the received beam pattern at the user when far-field beamforming vectors are used for beam scanning, and show an interesting result that this beam pattern contains useful user angle and range information. Then, we propose two efficient schemes to jointly estimate the user angle and range with the DFT codebook. The first scheme estimates the user angle based on a defined angular support and resolves the user range by leveraging an approximated angular support width, while the second scheme estimates the user range by minimizing a power ratio mean square error (MSE) to improve the range estimation accuracy. Finally, numerical simulations show that our proposed schemes greatly reduce the near-field beam training overhead and improve the range estimation accuracy as compared to various benchmark schemes.
AbstractList Prior works on near-field beam training have mostly assumed dedicated polar-domain codebook and on-grid range estimation, which, however, may suffer long training overhead, high codebook storage requirement, and degraded estimation accuracy. To address these issues, we propose in this paper new and efficient beam training schemes with off-grid range estimation by using conventional discrete Fourier transform (DFT) codebook. Specifically, we first analyze the received beam pattern at the user when far-field beamforming vectors are used for beam scanning, and show an interesting result that this beam pattern contains useful user angle and range information. Then, we propose two efficient schemes to jointly estimate the user angle and range with the DFT codebook. The first scheme estimates the user angle based on a defined angular support and resolves the user range by leveraging an approximated angular support width, while the second scheme estimates the user range by minimizing a power ratio mean square error (MSE) to improve the range estimation accuracy. Finally, numerical simulations show that our proposed schemes greatly reduce the near-field beam training overhead and improve the range estimation accuracy as compared to various benchmark schemes.
Author Zhang, Yunpu
Li, Jiapeng
Wu, Xun
You, Changsheng
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crossref_primary_10_1109_OJCOMS_2024_3486172
crossref_primary_10_1109_ACCESS_2025_3531909
crossref_primary_10_1109_JSAC_2025_3536557
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Snippet Prior works on near-field beam training have mostly assumed dedicated polar-domain codebook and on-grid range estimation, which, however, may suffer long...
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SubjectTerms Accuracy
Angles (geometry)
Antennas
Array signal processing
beam training
Beamforming
Discrete Fourier transforms
Estimates
Estimation
Extremely large-scale array (XL-array)
Far fields
Fourier transforms
Near fields
near-field communication
Pattern analysis
Signal to noise ratio
Training
Wireless communication
Title Near-Field Beam Training: Joint Angle and Range Estimation With DFT Codebook
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Volume 23
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