3-D Position and Velocity Estimation in 5G mmWave CRAN with Lens Antenna Arrays

Jie Yang, Shi Jin, Yu Han, Michail Matthaiou, Yongxu Zhu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)
158 Downloads (Pure)


5G millimeter-wave (mmWave) cloud radio access networks (CRANs) provide new opportunities for accurate multilateration: large bandwidth, large antenna arrays, and increased densities of base stations allow for unparalleled delay and angular resolution. However, combining localization into communications and designing joint position and velocity estimation algorithms are challenging problems. This paper considers the joint estimation in three-dimensional (3-D) lens antenna array based mmWave CRAN architecture. We embed multilateration into communications and explain its benefits for the initial access and beam training stages. We propose a closed-form solution for the joint estimation problem by forming the pseudo-linear matrix representation and designing the weighted least squares estimator with hybrid measurements. The proposed method is proven asymptotically unbiased and confirmed by simulations to achieve the Cramer- Rao lower bound and attain the desired sub-decimeter level accuracy.
Original languageEnglish
Title of host publication90th IEEE Vehicular Technology Conference (VTC): Proceedings
Publisher IEEE
ISBN (Electronic)978-1-7281-1220-6
ISBN (Print)978-1-7281-1221-3
Publication statusPublished - 07 Nov 2019

Publication series

NameVehicular Technology Conference (VTC): Proceedings
ISSN (Electronic)2577-2465


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