Influence of Positioning Error on X-Map Estimation Michaela - - PowerPoint PPT Presentation

FP7 ICT-SOCRATES

Influence of Positioning Error

• n X-Map Estimation

Michaela Neuland, TUBS

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Outline

• Motivation
• X-map estimation approach
• Simulation scenario
• Comparison of different X-maps
• Position error modelling
• Conclusion and next steps

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• Drive/walk tests are used for radio network planning and optimisation today

– expensive – cover only a limited part of the network – capture only a snapshot in time

⇒ use mobiles as probes for the service quality

• X-map estimation function

– continuously monitors the network – estimates the spatial characteristics of the network, e.g., coverage or throughput – connects the UE measurements to an estimated geographic position – may use other sources of information, e.g. prediction data

• X-map is a geographic map with overlay performance information depending on

– the positioning accuracy – the UE measurement accuracy – the number of measurements taken

Motivation

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X-map estimation approach

UE Location and Measurement Unit (LMU) Propagation Model approach 1: approach 2: X-map Prediction Data UE/RAN Measurement, Time, Position Measurement, Time, Location Data RAN Measurement Unit (RMU) Propagation Model Calibration Bin Update UE1 UEn X-Map Estimation

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+ very accurate

• RSRP values only for those pixels which are covered by a UE

X-maps based on approach 1

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(x,y) RSRP (x,y) RSRP (x,y) RSRP (x,y) RSRP (x,y) RSRP (x,y) RSRP (x,y) RSRP (x,y) RSRP RSRP RSRP RSRP RSRP RSRP RSRP RSRP RSRP

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X-maps based on approach 2

(x,y) RSRP (x,y) RSRP (x,y) RSRP (x,y) RSRP (x,y) RSRP (x,y) RSRP (x,y) RSRP (x,y) RSRP RSRP RSRP RSRP RSRP RSRP RSRP RSRP RSRP

building natural areas water- ways streets railway

diff diff diff diff diff diff diff diff corr corr corr corr corr

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+ RSRP value for every pixel in the X-map

• less accurate

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• city area of 1.5 km x 1.5 km in Germany
• 20 mobile users traces derived with the

help of SUMO

• network information available (site

location, sector orientation, tilt)

• realistic path loss predictions at 2.6 GHz

– used for determining 30 strongest cells

for each user position

– reference for determining accuracy of the

X-maps

• satellite orbits for a specific day and time

Simulation scenario

Source: Google Earth 5.0

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Comparison of different X-maps (real position)

approach 1 approach 2

approach 1 approach 2 µ σ µ σ 0.0 0.2 2.1 6.6

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• For LTE, three different localisation methods are foreseen

– Assisted Global Positioning System (A-GPS) – Observed Time Difference of Arrival (OTDOA) – Enhanced cell ID positioning method

• Model for the minimum mean square position error based on the Cramér-

Rao lower bound found in the literature*

• This model is based on the

– geometry of eNodeBs / satellites and the UE – number of measured signals – standard deviation of the measurement error

Position Error Modelling

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* C. Fritsche and A. Klein, “Cramér-Rao Lower Bounds for Hybrid Localization of Mobile Terminals”, 5th Workshop on Positioning, Navigation and Communication (WPNC ‘08), March 2008

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• For OTDOA measurements from at least 3 eNodeBs are necessary
• To improve hearability

– special positioning reference signals (PRS) with frequency reuse of 6 – low interference subframes

⇒ modelled by excluding the 6 strongest cells from interference calculation

Position error modelling

• 1

1 2 3 4 500 1000 1500 2000 2500 3000

• 1

1 2 3 4 5 6 500 1000 1500 2000

histogram number of visible satellites number of hearable eNodeBs number of hearable eNodeBs

2 4 6 8 10 12 200 400 600 800 1000 1200

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GPS OTDOA OTDOA6

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Position error modelling

• 1

1 2 3 4 500 1000 1500 2000 2500 3000

• 1

1 2 3 4 5 6 500 1000 1500 2000 20 40 60 80 100 0.2 0.4 0.6 0.8 1

histogram

GPS OTDOA

cumulative distribution function GPS OTDOA6

OTDOA6

position error in m number of visible satellites number of hearable eNodeBs number of hearable eNodeBs

GPS OTDOA6 GPS+OTDOA6 valid positions 77.1 % 66.4 % 90.6 % mean error 8.3 m 16.3 m 8.8 m standard deviation 6.7 m 31.4 m 6.7 m

2 4 6 8 10 12 200 400 600 800 1000 1200

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Comparison of X-maps with different positioning methods

approach 1 approach 2 µ σ µ σ GPS 0.1 2.3 2.6 6.6 GPS+OTDOA6 0.0 2.3 2.9 6.7 OTDOA6 0.0 4.6 4.6 6.7

GPS GPS+OTDOA6 OTDOA6

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• Two approaches for creating X-maps have been introduced
• Influence of the positioning error on the accuracy of the X-maps has been

analysed

– approach 1 strongly depends on the accuracy of the positioning method – approach 2 is almost independent of the used positioning method

• Next steps:

– consider other factors which have an influence on the accuracy of the X-maps – UE inaccuracies – number of measurements taken – consider real measurement data

Conclusion and next steps

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Thank you!

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