An Efficient Grid-based RF Fingerprint Positioning ... · Mondal, R. U., J. Turkka, T. Ristaniemi,...
Transcript of 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
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.
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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
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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.
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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.
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RF Fingerprinting
RF fingerprinting consists of two main phases:
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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).
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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.
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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
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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
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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
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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
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Studied Scenarios
Two cellular network scenarios were developed having different inter-site distance between the macro and pico BSs.
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x axis [m]
y a
xis
[m
]
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x axis
y a
xis
Sparse Regular Macro Dense HetNet
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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
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Results and Discussion
Performance evaluation of SGL and OGL methods:
Scenario
Training
Data
(%)
RF Finger-
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%)
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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.
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Grid-cell Layout Optimization using MOGA
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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
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Studied Scenario
Dense urban network scenario (HetNet) was used with several macro and pico BSs.
Dense HetNet
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650 850 1050 1250 1450 1650 1850625
825
1025
1225
1425
1625
01
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x axis [m]
y a
xis
[m
]
A
C
B
D
Simulation Parameters
MOGA parameters used in this study:
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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
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Grid-cell Layout Optimization
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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.
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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.
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LTE Rel.10 Dynamic System Simulator
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