TY - JOUR
T1 - MmWave V2V Localization in MU-MIMO Hybrid Beamforming
AU - Lota, Jaswinder
AU - Ju, Shihao
AU - Kanhere, Ojas
AU - Rappaport, Theodore S.
AU - Demosthenous, Andreas
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2022
Y1 - 2022
N2 - 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 the number of vehicles reduces the Cramér-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.
AB - 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 the number of vehicles reduces the Cramér-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.
KW - Hybrid beamforming
KW - MU-MIMO
KW - V2V
KW - localization
KW - mmWave
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U2 - 10.1109/OJVT.2022.3170522
DO - 10.1109/OJVT.2022.3170522
M3 - Article
AN - SCOPUS:85129680306
SN - 2644-1330
VL - 3
SP - 210
EP - 220
JO - IEEE Open Journal of Vehicular Technology
JF - IEEE Open Journal of Vehicular Technology
ER -