Joint User Node Positioning and Clock Offset Estimation in 5G Ultra-Dense Networks

Werner, Janis; Costa, Mário; Hakkarainen, Aki; Leppänen, Kari; Valkama, Mikko

doi:10.1109/glocom.2015.7417360

Cited by 34 publications

(49 citation statements)

References 17 publications

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“…In [38], the proposed GNSS/5G hybridization consists in adding 5G observations in the measurement vector of both single and PPP GNSS, again by exploiting the angular information from 5G. An accuracy in the order of 1 m or even below is expected for positioning supported by 5G networks; additionally, Werner et al [39] showed that positioning algorithms can run at the network side, ensuring a highly energy efficient approach from the UE's perspective.…”

Section: Hybridized Gnss For Train Positioningmentioning

confidence: 99%

Hybridized-GNSS Approaches to Train Positioning: Challenges and Open Issues on Uncertainty

Spinsante

Stallo

2020

Sensors

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In recent years, the development of advanced systems and applications has propelled the adoption of autonomous railway traffic and train positioning, with several ongoing initiatives and experimental testbeds aimed at proving the suitability and reliability of the Global Navigation Satellite System signals and services, in this specific application domain. To satisfy the strict safety and accuracy requirements aimed at assuring the position solution’s integrity, availability, accuracy and reliability, recent proposals suggest the hybridization of the Global Navigation Satellite System with other technologies. The integration with localization techniques that are expected to be available with the upcoming fifth generation mobile communication networks is among the most promising approaches. In this work, different approaches to the design of hybrid positioning solutions for the railway sector are examined, under the perspective of the uncertainty evaluation of the attained results and performance. In fact, the way the uncertainty associated to the positioning measurements performed by different studies is reported is often not consistent with the Guide to the Expression of Uncertainty in Measurement, and this makes it very difficult to fairly compare the different approaches in order to identify the best emerging solution. Under this perspective, the review provided by this work highlights a number of open issues that should drive future research activities in this field.

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Section: Hybridized Gnss For Train Positioningmentioning

confidence: 99%

Hybridized-GNSS Approaches to Train Positioning: Challenges and Open Issues on Uncertainty

Spinsante

Stallo

2020

Sensors

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“…Within the update cycle, standard deviation (STD) of speed in the state vector s l [i] is set to be σ sl = [0.01m/s, 0.01m/s, 0.01m/s] T with the speed modelled as Gaussian distributed with zero mean and the noise variance from measurement vector y l [i] obtained from DoA and ToA is σ el = 0.6m/s. For more details of the location tracking process employed here, please refer to reference [5]. The update process follows the equations from (25) to (29).…”

Section: Simulation and Analysismentioning

confidence: 99%

“…Fortunately, the extremely short wavelength of millimeter wave renders installation of a large number of array antennas at both UNs and ANs possible and the wide bandwidth can lead to highly accurate position measurements such as ToA (time of arrival) and TDoA (time difference of arrival). In [5], a joint DoA/ToA tracking method based on extended Kalman filtering (EKF) and a clock offset positioning method was proposed with an accuracy of about 0.1m. By combining DoA and RSS (received signal strength), a low-complexity localization system was presented in [6].…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Millimetre Wave Beam Tracking Based on Handset MEMS Sensors with Extended Kalman Filtering

Liu

2016

Radio Propagation and Technologies for 5G (2016)

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Due to the narrow beam in millimetre wave communication for future 5G networks, small device movements in the form of either self-rotation or displacement can result in serious power loss. In this paper, a three-dimensional (3-D) beam tracking method employing extended Kalman filtering (EKF) is proposed based on antenna arrays and the three smart phone sensors, which are gyroscope, accelerometer and magnetometer, embedded in the micro-electro-mechanical system (MEMS) inside the smart phone. The EKF-based location tracking is also incorporated into the design by combing the data from direction of arrival (DoA) and time of arrival (ToA) estimation results of the user node (UN), since accurate UN location information is also very important in the process of beam tracking to achieve beam alignment between the access node (AN) and the UN.

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“…Fortunately, the extremely short wavelength of millimetre wave renders installation of a large number of antenna arrays at both UNs and ANs possible, and the wide bandwidth can lead to highly accurate position measurements such as ToA (time of arrival) and TDoA (time DoA)dardari2015indoor. In [5], a joint DoA/ToA tracking method based on extended Kalman filtering (EKF) and a clock offset positioning method was proposed with an accuracy of about 0.1 m. By combining DoA and RSS (received signal strength), a low-complexity localisation system was presented in [6]. Furthermore, the angle information acquired from DoA is applied in direction lock loop for tracking purposes in [7].…”

Section: Introductionmentioning

confidence: 99%

Three‐dimensional millimetre‐wave beam tracking based on smart phone sensor measurements and direction of arrival/time of arrival estimation for 5G networks

Liu

2018

IET Microwaves, Antennas & Propagation

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Abstract:The narrow millimetre wave beam in future 5G networks is easily interrupted by the movement of mobile handsets including both location change and self-rotation. In this paper, a three-dimensional (3-D) beam tracking method is proposed to achieve beam alignment between the access node (AN) and the user node (UN). A gradient descent algorithm is employed for self-rotation tracking based on measurements obtained by the three smart phone sensors (gyroscope, accelerometer and magnetometer) embedded in the micro-electro-mechanical system (MEMS). An extended Kalman filtering (EKF) based location tracking algorithm is also incorporated into the design by combining the data from direction of arrival (DoA) and time of arrival (ToA) estimation results of the user node (UN), since accurate UN location information is also crucial in the beam tracking process. Moreover, an operation protocol is developed to coordinate the tracking process and tested in different scenarios.

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Joint User Node Positioning and Clock Offset Estimation in 5G Ultra-Dense Networks

Cited by 34 publications

References 17 publications

Hybridized-GNSS Approaches to Train Positioning: Challenges and Open Issues on Uncertainty

Hybridized-GNSS Approaches to Train Positioning: Challenges and Open Issues on Uncertainty

Three-Dimensional Millimetre Wave Beam Tracking Based on Handset MEMS Sensors with Extended Kalman Filtering

Three‐dimensional millimetre‐wave beam tracking based on smart phone sensor measurements and direction of arrival/time of arrival estimation for 5G networks

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