Beam-based vehicular position estimation in 5G radio access

Xin, Zhang; Razavi, Sara Modarres; Gunnarsson, Fredrik; Larsson, Kjell; Manssour, Jawad; Na, Minsoo; Choi, Changsoon; Jo, Sung-Ho

doi:10.1109/wcnc.2018.8377434

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…Thus, several techniques for positioning using the signals and features of the 5G network have been investigated. Most of these techniques are based on classical positioning methods such as direction of departure (DoD) and timing advance (e.g., [1], [2]). The literature shows that these methods work well in line-of-sight (LOS) conditions but not in non-LOS (NLOS) conditions.…”

Section: Introductionmentioning

confidence: 99%

5G Positioning - A Machine Learning Approach

Malmstrom

Skog

Razavi

et al. 2019

2019 16th Workshop on Positioning, Navigation and Communications (WPNC)

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In urban environments, cellular network-based positioning of user equipment (UE) is a challenging task, especially in frequently occurring non-line-of-sight (NLOS) conditions. This paper investigates the use of two machine learning methodsneural networks and random forests -to estimate the position of UE in NLOS using best received reference signal beam power measurements. We evaluated the suggested positioning methods using data collected from a fifth-generation cellular network (5G) testbed provided by Ericsson. A statistical test to detect NLOS conditions with a probability of detection that is close to 90% is suggested. We show that knowledge of the antenna are crucial for accurate position estimation. In addition, our results show that even with a limited set of training data and one 5G transmission point, it is possible to position UE within 10 meters with 80% accuracy.

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Section: Introductionmentioning

confidence: 99%

5G Positioning - A Machine Learning Approach

Malmstrom

Skog

Razavi

et al. 2019

2019 16th Workshop on Positioning, Navigation and Communications (WPNC)

Self Cite

View full text Add to dashboard Cite

show abstract

“…Additionally, the machine-learning based positioning techniques for 5G networks is investigated considering line-of sight (LOS) and none-line-of-sight (NLOS) cases in [46]. In [47], the position estimation is performed by estimating AOA over beam tracking and the uplink timing alignment in 5G networks. In order to evaluate positioning methods, a 5G testbed is built and run on a test environment.…”

Section: Introductionmentioning

confidence: 99%

“…In order to evaluate positioning methods, a 5G testbed is built and run on a test environment. The measurement results are compared with the real-time GPS data [47]. As another relevant study, the authors developed a TDOA-based positioning method to localise the high speed trains in 5G new radio (NR) networks in [48].…”

Section: Introductionmentioning

confidence: 99%