model tuning report
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Model Tuning Report
NOKIA RNP Team
PT.Nokia Solutions and Networks, April 2015
Revision History
Date Revision No. Description Author
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Contents
1 Introduction ........................................................................................................... 6
1.1 Model Tuning Process ............................................................................................. 6
1.2 Model Tuning Objectives ......................................................................................... 6
1.3 Digital Map .............................................................................................................. 6
1.4 Propagation Model Definition ................................................................................... 8
1.4.1 Propagation environments ....................................................................................... 8
1.4.2 Standard Propagation Model ................................................................................... 8
2 Site Selection and Route Planning Process ........................................................ 10
2.1 RF Measurement ..................................................................................................... 10
2.2 Site Selection .......................................................................................................... 10
2.3 Site survey .............................................................................................................. 12
2.4 CW Test route ......................................................................................................... 12
3 Model Calibration .................................................................................................. 13
3.1 Introduce ................................................................................................................. 13
3.2 Model for Dense Urban ........................................................................................... 13
3.2.1 Analysis CW Test Data for Dense Urban ................................................................. 13
3.2.2 Model Tuning Result for Dense Urban ..................................................................... 23
3.3 Model for Urban ....................................................................................................... 24
3.3.1 Analysis CW Test Data for Urban ............................................................................ 24
3.3.2 Model Tuning Result for Urban ................................................................................ 34
3.4 Model for Sub Urban ............................................................................................... 35
3.4.1 Analysis CW Test Data for Sub Urban ..................................................................... 35
3.4.2 Model Tuning Result for Sub Urban ......................................................................... 43
3.5 Model for Rural ........................................................................................................ 44
3.5.1 Analysis CW Test Data for Rural ............................................................................. 44
3.5.2 Model Tuning Result for Rural ................................................................................. 50
4. LTE Propagation Model of Jakarta ................................................................................. 51
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Figures
Figure 1-1 clutter plot of Jakarta ................................................................................................... 7
Figure 2-1 Rooftop antenna clearance ....................................................................................... 11
Figure 3-1 CW Signal Strength plot Dense Urban ...................................................................... 13
Figure 3-2 signal Strength vs 10 log (d) before filter UOB .......................................................... 14
Figure 3-3 signal Strength vs 10 log (d) before filter Mandiri ...................................................... 15
Figure 3-4 signal Strength vs 10 log (d) before filter PPHUI ....................................................... 16
Figure 3-5 signal Strength vs 10 log (d) before filter Resto Paregu ............................................ 17
Figure 3-6 Dense Urban DT Data process ................................................................................. 18
Figure 3-7 Signal strength vs log (d) after filter UOB .................................................................. 19
Figure 3-8 Signal strength vs log (d) after filter Mandiri .............................................................. 20
Figure 3-9 Signal strength vs log (d) after filter PPHUI ............................................................... 21
Figure 3-10`Signal strength vs log (d) after filter Resto Paregu .................................................. 22
Figure 3-11 CW Signal Strength plot Urban ............................................................................... 24
Figure 3-12 signal Strength vs 10 log (d) before filter Salon JLO ................................................ 25
Figure 3-13 signal Strength vs 10 log (d) before filter Kemang Selatan ...................................... 26
Figure 3-14 signal Strength vs 10 log (d) before filter Cementaid ............................................... 27
Figure 3-15 signal Strength vs 10 log (d) before filter Ruko Artha KEdoya ................................. 28
Figure 3-16 Urban DT Data process .......................................................................................... 29
Figure 3-17 Signal strength vs log (d) after filter Salon JLO ....................................................... 30
Figure 3-18 Signal strength vs log (d) after filter Kemang Selatan .............................................. 31
Figure 3-19 Signal strength vs log (d) after filter Cementaid ....................................................... 32
Figure 3-20 Signal strength vs log (d) after filter Ruko Artha Kedoya ......................................... 33
Figure 3-21 CW Signal Strength plot Sub Urban ........................................................................ 35
Figure 3-22 signal Strength vs 10 log (d) before filter Primagama .............................................. 36
Figure 3-23 signal Strength vs 10 log (d) before filter Bina Asih ................................................. 37
Figure 3-24 signal Strength vs 10 log (d) before filter Perkutut ................................................... 38
Figure 3-25 Sub Urban DT Data process ................................................................................... 39
Figure 3-26 Signal strength vs log (d) after filter Primagama ...................................................... 40
Figure 3-27 Signal strength vs log (d) after filter Bina Asih ......................................................... 41
Figure 3-28 Signal strength vs log (d) after filter Perkutut ........................................................... 42
Figure 3-29 CW Signal Strength plot Rural ................................................................................ 44
Figure 3-30 signal Strength vs 10 log (d) before Rangon Jaya ................................................... 45
Figure 3-31 signal Strength vs 10 log (d) before filter Sukatani .................................................. 46
Figure 3-32 Rural DT Data process ............................................................................................ 47
Figure 3-33 Signal strength vs log (d) after filter Rangon Jaya ................................................... 48
Figure 3-34 Signal strength vs log (d) after filter Sukatani .......................................................... 49
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Tables
Table 1-1 Clutter Classification scheme ....................................................................................... 8
Table 2-1 List of selected CW test for Jakarta ............................................................................ 11
Table 3-1Test Point count of each site Dense urban .................................................................. 14
Table 3-2Test Point count of each site Urban ............................................................................. 24
Table 3-3Test Point count of each site Sub Urban ..................................................................... 35
Table 3-4Test Point count of each site Rural .............................................................................. 44
Table 4-1 Final Propagation Model tuning result of Jakarta ........................................................ 51
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1 Introduction
Propagation model Tuning is a crucial procedure early in network Deployment as it enables
accurate predictions of coverage and interference. The objective of this document is to describe
the CW drive campaign and the propagation model tuning procedure for smartfren FDD 850
project in Jakarta of Indonesia. Here we do CW test and propagation model tuning base on FDD
850 MHz, and for LTE propagation model was calculated depend on tuned Standard Propagation
Model.
Anite Nemo Scanner and Coyote used to data process and Atoll (version : 3.2.1.7090) for model
tuning Maps & Clutters
1.1 Model Tuning Process
The model tuning process including the following activities:
Define models required to simulate coverage of different physical environments
Site survey to find suitable sites for each environment
Clearance Scanning
Equipment and testing
CW test and data collection
Assessment and preparation data
Model tuning for each environment
Validate the model
1.2 Model Tuning Objectives
The propagation models included in this investigation refers to dense urban, urban, sub urban,
and rural area.
The following were used as objectives throughout the process:
Mean error between -1 and 1 dB in global calculation each clutter
Standard Deviation error
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Digital Terrain Map
All measurement data was resolved using a 20 m resolution Digital Terrain Map. The vintage of
the map used was Indonesia: West Java, Map 20m, January 15th 2015 from Computa Maps
Company. Universal transverse Mercator co-ordinate system with zone 48s and datum WGS84
is used
Clutter Database
The clutter data classes used for the Jakarta model tuning campaign are listed in was
Indonesia: West Java, Map 20m, January 15th 2015 from Computa Maps Company, with the
clutter distribution shown in figure1.1. The 20 m resolution map was used to represent the land
cover.
Figure 1-1 clutter plot of Jakarta
This data formed a part of the procured maps. The following table shows various classifications
that were defined.
Code Name
Default Values
1 Sea
2 inland water
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3 Wetland
4 Barren
5 low vergetation
6 sparse forest
7 Forest
8 Village
9 residential with trees
10 residential with few trees
11 dense residential
12 Urban
13 dense urban
14 high buildings
15 building blocks
16 comercial/industrial
17 Airport
18 open in urban Table 1-1 Clutter Classification scheme
1.4 Propagation Model Definition
We use standard propagation model in Atoll for FDD LTE 850 MHz network. The Standard
Propagation Model (SPM) is based on the cost -231 formulas and is suited for predictions in the
850 to 3500 MHz band over long distances (from one to 20 km). It is the best suited to UMTS
and LTE radio technologies
1.4.1 Propagation environments
In Jakarta, the main propagation environment is a mix of dense urban, urban,sub urban and rural
clutter. Considering the planning issues (also height and clutter data of digital map), it is
acceptable to develop three models for the whole city, dense urban, urban, sub urban and rural.
The propagation environment is mostly characterised as residential and commercial buildings
throughout the whole city.
1.4.2 Standard Propagation Model
Below is the Standard Propagation Model used in Atoll.
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It consists of parts:
- General parameter
- The basic path loss model
- Calculation of the base station effective antenna height
- Diffraction Clutter corrections
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2 Site Selection and Route Planning Process
2.1 RF Measurement
In order to calibrate the propagation model, a comparison was carried out between the predicted
propagation and actual measured data. This measured data was collected by way of a series of
Continuous Wave (CW) propagation surveys. In these drive test a calibrated test transmitter was
set up at a base station located and received signal strength measurements were made with
along a predetermined drive route
The accuracy of the model is directly related to the validity and accuracy of the CW data
2.2 Site Selection
To ensure validity of the calibration process it was essential that site was selected carefully and
that various parameters required in the calibration process were verified. The site morphology is
also a major factor in determine the extent of survey regarding direction around the site.
Site selection factors include:
Test site measured were representative of typical BTS sites, considering issues such as
the general environment and antenna height surrounding clutter characteristics etc.
They were located in and around the area where the prediction model is to be used so as
to capture a good representation of data in that propagation location thus ensuring a
valid model for that propagation classification.
Rooftop sites were chosen with flat roofs and power outlets (possibly sites with BTS
equipment on the roof) so that test masts and equipment could be installed.
.selected sites height was representative of the relative radiation height of the network.
As this drive campaign was concerned with the calibration of macro cell model, micro
cellular sites or umbrella sites were not be taken in to account.
Selected sites coverage was chosen to minimise anomalous local propagation
phenomena, such as near obstacles shadowing, canyon effect, measurement faults,
etc.
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It was ensured that minimum height of the transmit antenna was such that the 3 dB
vertical beam width was free of obstruction from the building. This can be seen in:
H=d tan()
Where =0.5 * (3 dB vertical beam width) + safety margin
A margin of 10 degrees was included
Figure 2-1 Rooftop antenna clearance
List of selected CW Test sites for Jakarta model is shown as the following table
Site Name Morphologies Longitude Latitude Antenna Height
EIRP (dBm)
Antenna Power (dBi)
Antenna type
ZTE_0219 UOB Dense urban 106.823 -
6.19796 38
53 11 K 736347
ZTE_0215 MANDIRI Dense urban 106.8152 -6.225 24 53
11 K 736347
ZTE_0213 PPHUI Dense urban 106.8328 -6.222 25 53
11 K 736347
ZTE_0206 RESTORAN PAREGU Dense urban 106.83 -6.1874 32 47
11 K 736347
ZTE_0022 SALON J LO Urban 106.9009 -6.1575 23 53
11 K 736347
ZTE_0349 CEMENTAID Urban 106.905 -6.2035 30 47
11 K 736347
ZTE_0564 RUKO ARTHA KEDOYA
Urban 106.759 -
6.17509 23
53 11 K 736347
ZTE_0083 KEMANG SELATAN Urban 106.816 -6.275 42 47
11 K 736347
ZTE_3378 BINA ASIH Sub Urban 106.9577 -6.3047 42 53
11 K 736347
ZTE_0116 PRIMAGAMA PAMULANG
Sub Urban 106.729 -6.3424 25 47
11 K 736347
ZTE_4272 PEKUTUT TANGERANG
Sub Urban 106.6224 -6.141 29 47
11 K 736347
ZTE_2057 SUKATANI Rural 107.179 -6.1679 60 53
11 K 736347
ZTE_2029 RANGON JAYA Rural 107.366 -
6.28468 55
53 11 K 736347
Table 2-1 List of selected CW test for Jakarta
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2.3 Site survey
Factor for planning surveys are followings:
Drive test must first be planned according to the limitations observed through the site survey.
It is important to collect a statistically significant amount of data to model diffraction. A
good balance between LoS and NLoS should be attempted.
The data should be evenly distributed with respect to distance from the transmitter.
Distance should also be taken into account on a per clutter type basis. Through using
various test site locations this is practically implemented.
When planning drive test routes it is of great importance to ensure that the drive goes
through the clutter type in mind, since consecutive roads may be classified as different
clutter types. If in sufficient data measurements are collected in particular morphology
class in the calibration toward other classes may occur.
Using partially the same routes from different sites is beneficial since the different location
of the test antenna will provide different data with respect to the distance.
The extent of the survey is dictated on the amount of clutter types and required bins along
with the actual purposes of the survey data. In cases where the data collected will be
used for analyzing interference between sites survey may tend to reach long distances
away from the site (up to 20 km) with the actual route exceeding 100 km.
There should be sufficient data collected within each clutter category to ensure accurate
modelling.
2.4 CW Test route
The following factors should be considered when planning a route:
The route should be planned according to limitations noted at the survey stage. If the
antenna blocked in any direction then the route should avoid the area affected by this
blocking
The accuracy of the model calibration is dependent upon the amount of data collected, so
the route should cover as much road as timescale permit
The data for each clutter type should be evenly distributed with respect to log (distance)
between the measurement equipment and transmitter
Using partially the same routes for different surveys is beneficial since the different
location of the test antenna will provide different data with respect to distance and
effective antenna height.
The extent of the survey is dictated on the number of clutter types and required
measurements along with the actual purposes of the survey data and the frequency being
used.
The route should incorporate a variety of different terrain variations
Both line of sight and non line of sight points should be covered
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3 Model Calibration
3.1 Introduce
For output accurate propagation model for each clutter environment, the model calibration uses
few sites to do propagation model tuning in each tuning. According to importance of each
environment, choose different site count to do propagation model tuning, there are 4 sites
separately in Dense urban, 3 sites in urban and sub urban, and 2 sites in rural. Analyses CW test
data and output model tuning result in different environment.
The propagation models requires the definition of some general parameters, such as:
Frequency 874.7 MHz
Mobile antenna height : 2 m
3.2 Model for Dense Urban
3.2.1 Analysis CW Test Data for Dense Urban
Import the CW Test data into Atoll software, The CW signal strength of test data is following as:
Figure 3-1 CW Signal Strength plot Dense Urban
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Analysis test point distribution in different clutter. Confirm test points weight in different clutter,
according to percentage, ensure which points should be reversed, which should be removed.
Site name Number of Bin
ZTE_0219 UOB 8.883
ZTE_0215 MANDIRI 4.991
ZTE_0213 PPHUI 12.180
ZTE_0206 RESTO PAREGU 15.492
Table 3-1Test Point count of each site Dense urban
The relation between distance and signal strength
Figure 3-2 signal Strength vs 10 log (d) before filter UOB
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Figure 3-3 signal Strength vs 10 log (d) before filter Mandiri
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Figure 3-4 signal Strength vs 10 log (d) before filter PPHUI
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Figure 3-5 signal Strength vs 10 log (d) before filter Resto Paregu
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Figure 3-6 Dense Urban DT Data process
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Figure 3-7 Signal strength vs log (d) after filter UOB
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Figure 3-8 Signal strength vs log (d) after filter Mandiri
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Figure 3-9 Signal strength vs log (d) after filter PPHUI
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Figure 3-10`Signal strength vs log (d) after filter Resto Paregu
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3.2.2 Model Tuning Result for Dense Urban
According to test point distribution, the relation between distance and signal and other factors,
filter out same unreasonable data points, reserved reasonable data points, to do model tuning,
output model tuning result for dense urban
The result for dense urban models are listed below:
Result
Parameter Initial Final
K1 (LoS) 65.4 12.13
K1(NLoS) 40 6
K2 (LoS) Log(D) 65.4 42.28
K2(NLoS) Log(D) 40 42.28
K3 Log (HTx) -30 -19.68
K4 Diffraction 0 0.47
K5 Log (D) * log (HTx) 0 0
K6 0 0
K7 -5 0
The statistic for Dense Urban model
Statistic
Initial Final
Mean Error -19.82 -0.4
Standard deviation 9.72 7.78
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3.3 Model for Urban
3.3.1 Analysis CW Test Data for Urban
Import the CW Test data into Atoll software, The CW signal strength of test data is following as:
Figure 3-11 CW Signal Strength plot Urban
Analysis test point distribution in different clutter. Confirm test points weight in different clutter,
according to percentage, ensure which points should be reversed, which should be removed.
Site name Number of Bin
ZTE_0022 SALON JLO 7.801
ZTE_0083 KEMANGSELATAN 4.822
ZTE_0349 CEMENTAID 2.941
ZTE_0564 RUKOARTHAKEDOYA 5.433
Table 3-2Test Point count of each site Urban
The relation between distance and signal strength
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Figure 3-12 signal Strength vs 10 log (d) before filter Salon JLO
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Figure 3-13 signal Strength vs 10 log (d) before filter Kemang Selatan
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Figure 3-14 signal Strength vs 10 log (d) before filter Cementaid
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Figure 3-15 signal Strength vs 10 log (d) before filter Ruko Artha KEdoya
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Figure 3-16 Urban DT Data process
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Figure 3-17 Signal strength vs log (d) after filter Salon JLO
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Figure 3-18 Signal strength vs log (d) after filter Kemang Selatan
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Figure 3-19 Signal strength vs log (d) after filter Cementaid
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Figure 3-20 Signal strength vs log (d) after filter Ruko Artha Kedoya
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3.3.2 Model Tuning Result for Urban
According to test point distribution, the relation between distance and signal and other factors,
filter out same unreasonable data points, reserved reasonable data points, to do model tuning,
output model tuning result for Urban
The result for Urban models are listed below:
Result
Parameter Initial Final
K1 (LoS) 65.4 12.13
K1(NLoS) 40 28.44
K2 (LoS) Log(D) 65.4 56.61
K2(NLoS) Log(D) 40 48.49
K3 Log (HTx) -30 -20
K4 Diffraction 0 0.3
K5 Log (D) * log (HTx) 0 -10
K6 0 0
K7 -5 0
The statistic for Urban model
Statistic
Initial Final
Mean Error -20.42 -0.88
Standard deviation 10.91 5.96
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3.4 Model for Sub Urban
3.4.1 Analysis CW Test Data for Sub Urban
Import the CW Test data into Atoll software, The CW signal strength of test data is following as:
Figure 3-21 CW Signal Strength plot Sub Urban
Analysis test point distribution in different clutter. Confirm test points weight in different clutter,
according to percentage, ensure which points should be reversed, which should be removed.
Site name Number of Bin
ZTE_0116 PRIMAGAMA 4.137
ZTE_3378 BINA ASIH 3.227
ZTE_4272 PERKUTUT 6.503
Table 3-3Test Point count of each site Sub Urban
The relation between distance and signal strength
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Figure 3-22 signal Strength vs 10 log (d) before filter Primagama
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Figure 3-23 signal Strength vs 10 log (d) before filter Bina Asih
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Figure 3-24 signal Strength vs 10 log (d) before filter Perkutut
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Figure 3-25 Sub Urban DT Data process
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Figure 3-26 Signal strength vs log (d) after filter Primagama
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Figure 3-27 Signal strength vs log (d) after filter Bina Asih
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Figure 3-28 Signal strength vs log (d) after filter Perkutut
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3.4.2 Model Tuning Result for Sub Urban
According to test point distribution, the relation between distance and signal and other factors,
filter out same unreasonable data points, reserved reasonable data points, to do model tuning,
output model tuning result for Sub Urban
The result for Sub Urban models are listed below:
Result
Parameter Initial Final
K1 (LoS) 65.4 12.13
K1(NLoS) 40 10.1
K2 (LoS) Log(D) 65.4 34.68
K2(NLoS) Log(D) 40 34.68
K3 Log (HTx) -30 1.47
K4 Diffraction 0 0.3
K5 Log (D) * log (HTx) 0 -2.98
K6 0 0
K7 -5 0
The statistic for Sub Urban model
Statistic
Initial Final
Mean Error -29.83 -0.98
Standard deviation 7.15 6.19
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3.5 Model for Rural
3.5.1 Analysis CW Test Data for Rural
Import the CW Test data into Atoll software, The CW signal strength of test data is following as:
Figure 3-29 CW Signal Strength plot Rural
Analysis test point distribution in different clutter. Confirm test points weight in different clutter,
according to percentage, ensure which points should be reversed, which should be removed.
Site name Number of Bin
ZTE_2029 RANGON JAYA 8.430
ZTE_2057 SUKA TANI 4.999
Table 3-4Test Point count of each site Rural
The relation between distance and signal strength
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Figure 3-30 signal Strength vs 10 log (d) before Rangon Jaya
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Figure 3-31 signal Strength vs 10 log (d) before filter Sukatani
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Figure 3-32 Rural DT Data process
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Figure 3-33 Signal strength vs log (d) after filter Rangon Jaya
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Figure 3-34 Signal strength vs log (d) after filter Sukatani
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3.5.2 Model Tuning Result for Rural
According to test point distribution, the relation between distance and signal and other factors,
filter out same unreasonable data points, reserved reasonable data points, to do model tuning,
output model tuning result for Rural
The result for Rural models are listed below:
Result
Parameter Initial Final
K1 (LoS) 65.4 12.13
K1(NLoS) 40 12.13
K2 (LoS) Log(D) 65.4 52.54
K2(NLoS) Log(D) 40 52.54
K3 Log (HTx) -30 -20
K4 Diffraction 0 0.28
K5 Log (D) * log (HTx) 0 -10
K6 0 0
K7 -5 0
The statistic for Rural model
Statistic
Initial Final
Mean Error -24.75 0.27
Standard deviation 9.91 7.83
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4. LTE Propagation Model of Jakarta
The Jakarta of each model for 850 is listed in the table below
K (850 MHz) Dense Urban Urban Sub Urban Rural
K1 12.13 12.13 12.13 12.13
K2 38.63 56.61 34.68 52.54
K3 -19.68 -20 1.47 -20
K4 0.47 0.3 0.3 0.28
K5 0 -10 -2.93 -10
K6 0 0 0 0
K7 0 0 0 0
Table 4-0-1 Final Propagation Model tuning result of Jakarta
Appendix 1 : Equipment Description
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Equipment for CW measurement are listed in table below
SMARTFREN NOKIA
No System Equipment Freq. Brand Type Remark850 MHz HP hp 8371 b
2300 MHz HP-Agilent HP - AGILENT 8921A
850 MHz BVS BVS Power=40/46 dBm
2300 MHz Minicircuit ZHL 100W 242+ Power=37/50 dBm
850 MHz Kathrein K736347 Gain= 11 dBi
2300 MHz FTRF OA232410-NF Gain= 10 dBi
850 MHz
2300 MHz
5 Spectrum Analyzer Agilent E4407B
6 Site Master Anritsu S 331 L
7 Power Meter Agilent Agilent
8 Feeder Cable 7/8 60 Meters
9 Jumper
10 Roll Meter 100 Meters
Transmitter
Receiver
1 CW Generator
2 Power Amplifier
3 Omni Antena
Accesoris
4 ReceiverAnite Scanner
/Coyote
FSR1 /Dual Modular
Receiver
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