an efficient grid-based rf fingerprint positioning ... · mondal, r. u., j. turkka, t. ristaniemi,...

An Efficient Grid-based RF Fingerprint

Positioning Algorithm for User

Location Estimation

Riaz Uddin Mondal PhD student

Department of Mathematical Information Technology,

University of Jyväskylä, Finland

riaz.u.mondal@student.jyu.fi

Supervisor: Professor Tapani Ristaniemi

Present State

Beginning of PhD study: May 2010.

ECTS Credits Obtained: 56 (4 more to complete 60 ECTS)

Conference Publications:

Mondal, R. U., J. Turkka, T. Ristaniemi, ‘An Efficient Grid-based RF Fingerprint Positioning Algorithm for User Location Estimation in Heterogeneous Small Cell Networks’ accepted in International Conference on Localization and GNSS, Helsinki, Finland, June 24-26, 2014.

Mondal, R. U., J. Turkka, T. Ristaniemi, Tero Henttonen, ‘Performance Evaluation of MDT Assisted LTE RF Fingerprint Framework’, The seventh International Conference on Mobile Computing and Ubiquitous Networking, Singapore Management University, Singapore, January 6-8, 2014.

Mondal, R. U., T. Ristaniemi, M. Doula, ‘Genetic Algorithm Optimized Memory Polynomial Digital Pre-distorter for RF Power Amplifiers’, International Conference on Wireless Communications & Signal Processing (WCSP), Hangzhou, China, October 24-26, 2013.

Mondal, R. U., J. Turkka, T. Ristaniemi, T. Henttonen, ‘Positioning in Heterogeneous Small Cell Networks using MDT RF Fingerprints’, First International Black Sea Conference on Communications and Networking, Batumi, Georgia, July 3-5, 2013.

2

Outline

Introduction to Research Problem

Minimization of Drive Tests

Conventional Grid-based RF Fingerprint Positioning

Overlying Grid Layout (OGL) based RF Fingerprinting

Research Methodology

Results and Discussion

Ongoing Research

Future Work

3

Research Problem

The goal of the study is to:

Improve positioning accuracy of user equipments (UEs)

using grid based RF fingerprinting in heterogeneous

small cell network (HetNet) and regular macro (RM)

network scenarios using Minimization of Drive Tests

(MDT) measurements.

Analyze the effectiveness of the proposed method in

comparison to the conventional RF fingerprint

positioning.

4

Minimization of Drive Tests

One of the biggest challenges of RF fingerprinting is the burden

of creating and maintaining the correlation database of the

training RF fingerprints.

MDT functionality is a LTE Release-10 feature which allows

operators to autonomously collect serving and neighboring base

station measurements (RSRP, RSRQ) from intra- and inter-

frequency bands together with time stamp and location

information.

MDT is a cost-efficient solution for cellular mobile operators to

gather and maintain big RF fingerprint training database.

5

RF Fingerprinting

RF fingerprinting consists of two main phases:

6

Grid Based RF Fingerprinting

A training signature consists of a set of MDT measurements

that belongs to a particular grid cell unit (GCU) having RSRP

values from same Base Stations(BS).

7

Grid Based RF Fingerprinting (cnt’d)

UE position is estimated to be the grid cell unit center

location of the best matched training signature with the

testing signature.

8

Grid based RF Fingerprinting based on

Multivariate Gaussian Distribution

A training signature t of ith grid-cell is:

where, is a vector of mean values and is the covariance

matrix of the MDT measurements of ith grid.

Kullback-Leibler Divergence (KLD) is used to find the best

matching training signature for a particular testing signature.

Closed form KLD is given by:

, , , ,, , .i t i t i t i ts u Σ i

1 1 1

, , , , ,,

1|| ˆ ˆ ˆln

2i t i t i t u i t u

T

ii u tu t ud p p tr û Σ û Σ Σ I Σ Σu u

9

OGL RF Fingerprint Positioning

Training Phase Testing Phase

Training

Signatures

of OGL1

Training

Signatures

of OGL2

UE-wise Test Signature

Formation OGL1 Grid-

wise Group

of MDTs

OGL2 Grid-

wise Group

of MDTs

Combined Training Signatures

of OGL1 and OGL2

Group Training Signatures

According to BS ID Numbers

Select Training

Signatures having Same

BS ID as the Testing one

Calculate KLD values

and Chose Two Smallest

Training Signatures

Estimate UE Positions

According to KLD weights

10

OGL RF Fingerprint Positioning (cnt’d)

The weighted geometric center of the testing signature is

obtained from:

where,

Tr. sig. 1

Tr. sig. 2

Tr. sig. 3

Estimated

Position

Test

sig. 1

matched

matched

gl11

gl12

gl13

gl14

gl21

gl22

gl23

gl24

gl25

gl26

gl27

gl28

gl29

T[ , ] ( ) [ , ]i i i i ix y w x y

11

Research Methodology

Dynamic LTE Rel-10 compliant system simulator was used to simulate MDT measurement samples. In total, 1200 UEs were moving in the simulation scenario with mobility of 30 km/h. Average value of MDT measurement reporting length was 30 seconds and inverval was 1 report per second.

MDT RSRP modeling took into account the 3 GPP specifications:

-6 dB Ês/Iot criteria for UEs to detect BSs

Different pathloss models for different BSs types (Macro, Pico)

2D slow fading models with different standard deviations.

ITU Typical Urban fast fading

RSRP L3 filtering

Random UE measurement sampling error

12

Studied Scenarios

Two cellular network scenarios were developed having different inter-site distance between the macro and pico BSs.

650 850 1050 1250 1450 1650 1850625

825

1025

1225

1425

1625

01

23

4

56

7

8

1213

1415

16

1718

24

25

2627

28

2930

31

3233

34

35

3940

4142

43

4445

x axis [m]

y a

xis

[m

]

2800 4550 63002100

4200

5600

01

23

4

56

7

8

1213

1415

16

1718

2425

2627

28

2930

31

3233

34

35

3940

4142

43

4445

x axis

y a

xis

Sparse Regular Macro Dense HetNet

13

Results and Discussion

Analyzed training and testing signatures of SGL and OGL:

Grid-

cell

size

Scenario Training

Data (%)

Total no. of Training

Signatures (Absolute)

Analyzed Test

Signatures (%)

SGL OGL SGL OGL

10

-BY

-10

M

RM (ISD

1750M)

90% 16443 32758 83.19 84.86

10% 2044 4092 62.19 69.07

HSC (ISD

500M)

90% 48808 97687 71.66 73.39

10% 6319 12707 47.60 55.14

40

-BY

-40

M

RM (ISD

1750M)

90% 7090 14222 82.64 85.72

10% 1709 3401 64.02 73.80

HSC (ISD

500M)

90% 22079 44219 70.50 74.62

10%

5321 10677 47.74 61.43

14

Results and Discussion

Performance evaluation of SGL and OGL methods:

Scenario

Training

Data

(%)

RF Finger-

print

Algorithm

For 10-by-10 m Grid For 40-by-40 m Grid

68% PE

(m)

95% PE

(m)

68% PE

(m)

95% PE

(m)

RM (ISD 1750M)

90%

SGL Based

29.73

165.29

43.53

196.30

OGL Based

31.41

(+5.6%)

147.49

(-10.7%)

40.86

(-6.1%)

161.75

(-17.6%)

10%

SGL Based

72.00

228.80

72.48

225.45

OGL Based

63.96

(-11.1%)

206.05

(-9.9%)

65.03

(-10.2%)

203.70

(-9.6%)

HSC (ISD 500

M)

90%

SGL Based

21.12

58.08

33.73

76.43

OGL Based

19.45

(-7.9%)

50.94

(-12.2%)

27.57

(-18.2%)

64.87

(-15.1%)

10%

SGL Based

27.23

73.61

34.83

80.86

OGL Based

25.14

(-7.6%)

66.47

(-9.6%)

28.28

(-18.8%)

68.71

(-15.0%)

15

Results and Discussion

The proposed method can analyze more testing

signatures by analyzing more training signatures.

The performance evaluation indicates that the

proposed OGL method can provide a maximum of

18.8% improvement in positioning accuracy as

compared to that of the conventional SGL method.

16

Grid-cell Layout Optimization using MOGA

17

Start

Generate Initial population of Chromosoms

Genetic Operations: Crossover, Mutation

Calculate Multi-objective Functions

Calculate Ranking and Perform Selection

Stop Criteria

Stop

No

Yes

Fitness Function of Multi-objective GA

Create validation signatures UE-wise

Create grid-cell layout according to a chromosom

Group of MDT samples grid-wise and form training signatures

Select training signatures having same BS IDs as that of a

validating signature

Calculation of KLD values and select the grid-cell that

corresponds to the smallest training signature KLD

Calculate 68 and 95 percentiles of positioning error

18

Studied Scenario

Dense urban network scenario (HetNet) was used with several macro and pico BSs.

Dense HetNet

19

650 850 1050 1250 1450 1650 1850625

825

1025

1225

1425

1625

01

23

4

56

7

8

1213

1415

16

1718

24

25

2627

28

2930

31

3233

34

35

3940

4142

43

4445

x axis [m]

y a

xis

[m

]

A

C

B

D

Simulation Parameters

MOGA parameters used in this study:

20

Parameter Value

Selection type Tournament

Crossover function Scattered with cross-over fraction: 0.5

Mutation function Constraint dependent

Stopping criteria 200 generations or Spread of Pareto solutions less than

tolerance: 0.0001

Fitness (objective)

functions

68 percentile value of PE, 95 percentile value of PE

Simulation

parameters

For Area A: Total samples : 9224, Training samples: 914

(about 10%), Validation samples = 4176 (about 45%),

Test_samples = 4134 (about 45%), Chromosom length = 60,

Population size = 60, variables = 10 to 30

Results

Simulation

Number

Closest to Ideal Point Square Grid-

cell (SGC): 10m, 20m and 40m

Grid-cells respectively

PE and Analysed Test

Samples(ATS) with GA Optimized

Grid-cell (GAOGC) Units

(with test signatures)

Total

Generations

1st

simulation

10%

Training

Data

68 Perct. 95 Perct. ATS (%) 68 Perct. 95 Perct. ATS (%)

104

35.18 92.79 28.89 36.94 85.15 28.59

2nd

simulation

30%

Training

Data

68 Perct. 95 Perct. ATS in

(%) 68 Perct. 95 Perct.

ATS in

(%)

157

25.92 66.28 51.53 26.71 64.21 55.06

3rd

simulation

50%

Training

Data

68 Perct. 95 Perct. ATS in

(%) 68 Perct. 95 Perct.

ATS in

(%)

200

24.12 60.19 60.82 24.19 55.68 62.71

21

Grid-cell Layout Optimization

22

Future Work

Collection of LTE and WLAN measurements with Samsung

Galaxy S3 LTE device with Anite’s Nemo Handy-A software in

September 2014 has be completed.

Time stamp, GPS location, list of detected LTE Cell

ID/RSRP/RSRQ, list of detected WLAN MAC/RSSI/Quality

measurements were recorded.

Measurements were done on LTE 800MHz, 1800MHz and

2600MHz bands.

More than 100 km of measurements by foot, bicycle and car on

target area of 0.33 sqm.

Main purpose is to bring out a suitable method regarding hybrid

positioning architecture based on generalized MDT.

23

Future Work 24

Future Work 25

Acknowledgements

The present work was carried out within the framework of

European Celtic-Plus project SHARING (Self-organized

Heterogeneous Advanced Radio Networks Generation).

I am grateful to my fellow researcher Jussi Turkka for his

contribution and continuous encouragement throughout the

study.

26

LTE Rel.10 Dynamic System Simulator

27