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User Positioning in mmW 5G Networks Using Beam-RSRP Measurements and Kalman Filtering

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User Positioning in mmW 5G Networks Using Beam-RSRP Measurements and Kalman Filtering. / Rastorgueva-Foi, Elizaveta; Costa, Mário; Koivisto, Mike; Leppänen, Kari; Valkama, Mikko.

2018 21st International Conference on Information Fusion, FUSION 2018. IEEE, 2018. p. 1150-1156 8455289.

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Harvard

Rastorgueva-Foi, E, Costa, M, Koivisto, M, Leppänen, K & Valkama, M 2018, User Positioning in mmW 5G Networks Using Beam-RSRP Measurements and Kalman Filtering. in 2018 21st International Conference on Information Fusion, FUSION 2018., 8455289, IEEE, pp. 1150-1156, International conference on information fusion, 10/07/18. https://doi.org/10.23919/ICIF.2018.8455289

APA

Rastorgueva-Foi, E., Costa, M., Koivisto, M., Leppänen, K., & Valkama, M. (2018). User Positioning in mmW 5G Networks Using Beam-RSRP Measurements and Kalman Filtering. In 2018 21st International Conference on Information Fusion, FUSION 2018 (pp. 1150-1156). [8455289] IEEE. https://doi.org/10.23919/ICIF.2018.8455289

Vancouver

Rastorgueva-Foi E, Costa M, Koivisto M, Leppänen K, Valkama M. User Positioning in mmW 5G Networks Using Beam-RSRP Measurements and Kalman Filtering. In 2018 21st International Conference on Information Fusion, FUSION 2018. IEEE. 2018. p. 1150-1156. 8455289 https://doi.org/10.23919/ICIF.2018.8455289

Author

Rastorgueva-Foi, Elizaveta ; Costa, Mário ; Koivisto, Mike ; Leppänen, Kari ; Valkama, Mikko. / User Positioning in mmW 5G Networks Using Beam-RSRP Measurements and Kalman Filtering. 2018 21st International Conference on Information Fusion, FUSION 2018. IEEE, 2018. pp. 1150-1156

Bibtex - Download

@inproceedings{df96684d50e74635b9a84b6201f50cea,
title = "User Positioning in mmW 5G Networks Using Beam-RSRP Measurements and Kalman Filtering",
abstract = "In this paper, we exploit the 3D-beamforming features of multiantenna equipment employed in fifth generation (5G) networks, operating in the millimeter wave (mmW) band, for accurate positioning and tracking of users. We consider sequential estimation of users' positions, and propose a two-stage extended Kalman filter (EKF) that is based on reference signal received power (RSRP) measurements. In particular, beamformed downlink (DL) reference signals (RSs) are transmitted by multiple base stations (BSs) and measured by user equipments (UEs) employing receive beamforming. The so-obtained beam-RSRP (BRSRP) measurements are reported to the BSs where the corresponding directions of departure (DoDs) are sequentially estimated by a novel EKF. Such angle estimates from multiple BSs are subsequently fused on a central entity into 3D position estimates of UEs by means of another (second-stage) EKF. The proposed positioning scheme is scalable since the computational burden is shared among different network entities, namely transmission/reception points (TRPs) and 5G-NR Node B (gNB), and may be accomplished with the signalling currently specified for 5G. We assess the performance of the proposed algorithm on a realistic outdoor 5G deployment with a detailed ray tracing propagation model based on the METIS Madrid map. Numerical results with a system operating at 39 GHz show that sub-meter 3D positioning accuracy is achievable in future mmW 5G networks.",
keywords = "5G networks, beamforming, direction-of-departure, extended Kalman filter, line-of-sight, localization, location-awareness, positioning, RSRP, tracking",
author = "Elizaveta Rastorgueva-Foi and M{\'a}rio Costa and Mike Koivisto and Kari Lepp{\"a}nen and Mikko Valkama",
year = "2018",
month = "9",
day = "5",
doi = "10.23919/ICIF.2018.8455289",
language = "English",
isbn = "978-1-5386-4330-3",
pages = "1150--1156",
booktitle = "2018 21st International Conference on Information Fusion, FUSION 2018",
publisher = "IEEE",

}

RIS (suitable for import to EndNote) - Download

TY - GEN

T1 - User Positioning in mmW 5G Networks Using Beam-RSRP Measurements and Kalman Filtering

AU - Rastorgueva-Foi, Elizaveta

AU - Costa, Mário

AU - Koivisto, Mike

AU - Leppänen, Kari

AU - Valkama, Mikko

PY - 2018/9/5

Y1 - 2018/9/5

N2 - In this paper, we exploit the 3D-beamforming features of multiantenna equipment employed in fifth generation (5G) networks, operating in the millimeter wave (mmW) band, for accurate positioning and tracking of users. We consider sequential estimation of users' positions, and propose a two-stage extended Kalman filter (EKF) that is based on reference signal received power (RSRP) measurements. In particular, beamformed downlink (DL) reference signals (RSs) are transmitted by multiple base stations (BSs) and measured by user equipments (UEs) employing receive beamforming. The so-obtained beam-RSRP (BRSRP) measurements are reported to the BSs where the corresponding directions of departure (DoDs) are sequentially estimated by a novel EKF. Such angle estimates from multiple BSs are subsequently fused on a central entity into 3D position estimates of UEs by means of another (second-stage) EKF. The proposed positioning scheme is scalable since the computational burden is shared among different network entities, namely transmission/reception points (TRPs) and 5G-NR Node B (gNB), and may be accomplished with the signalling currently specified for 5G. We assess the performance of the proposed algorithm on a realistic outdoor 5G deployment with a detailed ray tracing propagation model based on the METIS Madrid map. Numerical results with a system operating at 39 GHz show that sub-meter 3D positioning accuracy is achievable in future mmW 5G networks.

AB - In this paper, we exploit the 3D-beamforming features of multiantenna equipment employed in fifth generation (5G) networks, operating in the millimeter wave (mmW) band, for accurate positioning and tracking of users. We consider sequential estimation of users' positions, and propose a two-stage extended Kalman filter (EKF) that is based on reference signal received power (RSRP) measurements. In particular, beamformed downlink (DL) reference signals (RSs) are transmitted by multiple base stations (BSs) and measured by user equipments (UEs) employing receive beamforming. The so-obtained beam-RSRP (BRSRP) measurements are reported to the BSs where the corresponding directions of departure (DoDs) are sequentially estimated by a novel EKF. Such angle estimates from multiple BSs are subsequently fused on a central entity into 3D position estimates of UEs by means of another (second-stage) EKF. The proposed positioning scheme is scalable since the computational burden is shared among different network entities, namely transmission/reception points (TRPs) and 5G-NR Node B (gNB), and may be accomplished with the signalling currently specified for 5G. We assess the performance of the proposed algorithm on a realistic outdoor 5G deployment with a detailed ray tracing propagation model based on the METIS Madrid map. Numerical results with a system operating at 39 GHz show that sub-meter 3D positioning accuracy is achievable in future mmW 5G networks.

KW - 5G networks

KW - beamforming

KW - direction-of-departure

KW - extended Kalman filter

KW - line-of-sight

KW - localization

KW - location-awareness

KW - positioning

KW - RSRP

KW - tracking

U2 - 10.23919/ICIF.2018.8455289

DO - 10.23919/ICIF.2018.8455289

M3 - Conference contribution

SN - 978-1-5386-4330-3

SP - 1150

EP - 1156

BT - 2018 21st International Conference on Information Fusion, FUSION 2018

PB - IEEE

ER -