mmWave V2V Localization in MU-MIMO Hybrid Beamforming

Article

Lota, J., Ju, S., Kanhere, O., Rappaport, T. S. and Demosthenous, A. 2022. mmWave V2V Localization in MU-MIMO Hybrid Beamforming. IEEE Open Journal of Vehicular Technology. 3, pp. 210-220. https://doi.org/10.1109/OJVT.2022.3170522
AuthorsLota, J., Ju, S., Kanhere, O., Rappaport, T. S. and Demosthenous, A.
Abstract

Recent trends for vehicular localization in millimetre-wave (mmWave) channels include employing a combination of parameters such as angle of arrival (AOA), angle of departure (AOD), and time of arrival (TOA) of the transmitted/received signals. These parameters are challenging to estimate, which along with the scattering and random nature of mmWave channels, and vehicle mobility lead to errors in localization. To circumvent these challenges, this paper proposes mmWave vehicular localization employing difference of arrival for time and frequency, with multiuser (MU) multiple-input-multiple-output (MIMO) hybrid beamforming; rather than relying on AOD/AOA/TOA estimates. The vehicular localization can exploit the number of vehicles present, as an increase in a number of vehicles reduces the Cramr-Rao bound (CRB) of error estimation. At 10 dB signal-to-noise ratio (SNR) both spatial multiplexing and beamforming result in comparable localization errors. At lower SNR values, spatial multiplexing leads to larger errors compared to beamforming due to formation of spurious peaks in the cross ambiguity function. Accuracy of the estimated parameters is improved by employing an extended Kalman filter leading to a root mean square (RMS) localization error of approximately 6.3 meters.

JournalIEEE Open Journal of Vehicular Technology
Journal citation3, pp. 210-220
ISSN2644-1330
Year2022
PublisherIEEE
Accepted author manuscript
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Digital Object Identifier (DOI)https://doi.org/10.1109/OJVT.2022.3170522
Publication dates
Online27 Apr 2022
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Accepted19 Apr 2022
Deposited29 Apr 2022
Copyright holder© 2022 The Authors
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