Utf 8_wizard Adv (Day Two)

March 2006

x-Wizard – Advanced Training

DAY TWO – Model and Clutter Optimization

2

Class Agenda

• Model Optimization – A description of how to use x-Wizard’s model

optimization routines

• Clutter Optimization – – A description of how to use x-Wizard’s model


• Optimization Processes- – A description of how to use x-Wizard’s model


3

Class Agenda – Model Optimization

• Propagation Model Optimization– Overview– The Basic Routine

Numeric Analysis Basic Inputs

– Batch vs Single model optimization

• Optimization Results– Evaluating Results– Grouping Sites– Method of averaging– Applying average models

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• This section of the class is offered . . .

– To explain the model optimization routine

– To illustrate the differences between single mode and batch mode optimization

– To demonstrate how to create an average Propagation Model for use at sites that were not driven

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Goal of the PMO • Create the most accurate

model with a minimal amount of effort– Drive a limited number of sites

that represent an area 10% - 15% of the sites in a

given morphology

– Derive optimized parameters for each site that can be averaged You should not have a high

variability in results

– Apply average parameters to sites that were not driven

Model Optimization - Overview

Yellow sites were driven and OptimizedAverage Model applies at Blue sitesIs it worth the effort to squeeze an additional dB of accuracy?

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Unoptimized Prop. Model

Scatter Plot Mr-Pr

Histogram Mr-Pr

Optimized Prop. Model

Prediction

O

p

t

i

m

i

z

a

t

o

n

i

Prediction

Mean Error0St.Dev…large

Mean Error 0St.Dev…minimized

Model Optimization - Overview

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• Least-Squares Linear Regression RSL can be modeled as a straight line vs Log (Dist.)

The routine finds the straight line that minimizes the error between measured and predicted data points by

– Driving the Mean Error to zero (Measured-Predicted)

– Driving the Standard Deviation as low as possible

Industry accepted standard deviation is 6 to 10 dB

• Remember - The routine always optimizes model parameters and KED correction factors (Foose Factor or Polynomial)

Model Optimization – Basic Routine

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• Note that the routine is a numerical analysis and may produce results that do not make physical sense– Slopes better than free space, for example

• Unreasonable results are always traced back to:– Bad drive data– Peculiar drive test conditions

Model Optimization – Basic Routine

Rx is looking down on Tx;Can cause RSL to increase

far from the site.RSL increases far from site!Data indicates interference

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Propagation Model Optimization Analysis Requires

1. Site to be optimized

2. Drive data for the site– Associated data used if

available

3. Reference Grid– Read from site

4. Model to be optimized & Method

5. Filtering parameters

Model Optimization – Basic Inputs

1

2

3

4

5

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Notes on Optimization Method

– Hata Model will only allow 2-parameter optimization

Effective Antenna Height Multiplier Area Correction Factor

– The routine will also optimize the KED correction if Obstructed points are included

Foose Factor

OR Polynomial Correction


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Notes on Optimization Method (cont.)

– Lee Model will allow 2- or 3-parameter optimization

Intercept (always optimized) Slope (always optimized) Tx Height Correction Multiplier (only 3-parameter Opt.)

– 3-parameter optimization will provide better fit to data BUT . . .

Can lead to un-intuitive Slope & Intercept Can make it hard to average due to differences in optimized models

– Use 3-parameter optimization sparingly


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Single Site Optimization Analysis

1. Does not require associated measured data

– Associated data will be used if available

2. Will optimize the model assigned to the site

– Must set model prior to entering the dialog (can’t change in dialog)

3. Allows filtering by user defined angle

4. Must collect multiple site results to derive average model outside of x-Wizard

5. Used to refine optimizations performed with Batch Mode

Model Optimization – Single Site

1

2

3

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Single Site Optimization Analysis - Output– User can save Excel file before hitting Okay

– The routine provides:– Optimized parameters– Statistics on quality of optimization


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Notes on Statistical Output1. Obstructed Stats allow you to determine the accuracy of the KED

correction2. Non-Obstructed Stats allow you to determine the accuracy of the

Model parameters3. Total Stats allow you to determine the accuracy of the entire

optimization

– Each group has:– # of Points compared– Mean Delta between Measured Data and Opt’d Predictions– Std. Dev. of Delta between Measured Data and Opt’d Predictions


1

2

3

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Notes on Controls at the Bottom

– ‘Next’, ‘Previous’ are used in Batch mode– ‘Save to File’ allows you to save the data to an Excel file– ‘Apply to all TXs’ will assign the optimized model directly to the Sector

that was optimized– ‘Apply to all Cells’ will assign the Sector’s model directly to the Cell

site that was optimized


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Batch Mode Optimization Analysis

1. Select Mode– Tx Specific model optimizes

different models at different sites

– Selected Model optimizes one model using all sites regardless of what is assigned at site/sector

2. Requires associated measured data

– Available sites represents the sites with associated data

3. Selected Sites will be optimized– Double click Available site to add

to the Selected sites

Model Optimization – Batch Mode

1

2

3

Dbl.Click

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Batch Mode Output – Average Stats Dialog1. “Save Avg. Model” can save the average parameters to

Common Folder– The Avg. model can be used at sites not driven

2. Only sites with “Use” box checked are included in Avg.3. Average based on # of data points used at each site (more

later)


1

2

1

3

Avg. modelwritten to . . .

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Batch Mode Output – Average Stats Dialog (cont)Use the built-in ‘Stats’ Tool to analyze site optimization


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Notes on Controls at the Bottom

– ‘Next’, ‘Previous’ are used to view each site’s results separately– ‘Save to File’ allows you to save the data to an Excel file– ‘Apply to all TXs’ will assign the optimized models directly to the

Sectors that are checked ‘use’– ‘Apply to all Cells’ will assign the model from the FIRST Sector directly

to the Cell site that was optimized– This does not assign a 3-Sector average model to the Cell Level


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Not all Optimization tests will result in data that can be used.Before creating an Average Model, look at:

– Number of Data Points total should be > 350 pts.

– Standard Deviation should be < 8 to 10 dB (depending on morphology)

– Mean Error should be zero or close to it

– Check for same on Obstructed and Non-Obstructed If Obstructed points provide the most error, you may want to change the

KED correction method

Model Optimization – Evaluating Results

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Before creating an Average Model, check the KED Correction parameters:

– Foose Method Foose Factor – should be < 6 dB/obs Depending on conditions (very rugged), you may use Foose

Factors between 6 & 9 dB (rarely)

– Polynomial Method A should be between 0 and 1 B should be between + 10 dB


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Before creating an Average Model, check the model parameters:

– Lee Model Slope – should be between -25 and -45 dBm/dec Intercept – should be between -60 and -80 dBm

– Hata Model Effective Ant. Ht. Multiplier – between -5 dB to 10 dB Area Correction Factor

– negative for urban: -15 dB to 0 dB– positive for suburban/rural: 0 dB to 30 dB


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Use the built-in Evaluation Tool for problem sites. Look for:

1. Changes in slope– May indicate that data strayed into different morphology

2. Areas of consistent Error– May indicate a tree, in the near field, blocking the Tx at certain

angles

Optimized model consistentlyUnder-predicts signal. Why?

Drive Test data seems to showmultiple slopes. Why?


1 2

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An Average Model must be applied to sites that were not driven, BUT it is important that the groupings point to the appropriate model

There are several techniques:– Group by Area

This could be a geographic area or morphological type

– Group by ERP It may work out that test sites with similar ERP optimized to

approximately same model parameters

– Group by Tower Height Clutter may affect sites with similar heights in the same way

Model Optimization – Grouping Sites

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• Grouping by Area– Typical morphologies are hard to define

‘Suburb’ in one area may not work for ‘Suburb’ in a different area

– Using Geographic areas can make it easier If data defines models that fit geo-areas, draw regions on the

map and name regions according to model


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• Grouping by ERP– It is not often effective, but some optimizations show a

pattern between model parameters and ERP

• Grouping by Tower Height– The idea is that model parameters are affected by clutter– The lower the antenna, the more dramatic the affect on

slope


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– Morphology Do all the models in a given area match each other?

– ERP Is there a clear pattern that would allow you to assign an average

model to site with ERP between X watts and Y watts?

– Tower Height (RCL) Can you see a similarity in propagation parameters for sites

where antennas are mounted at about X feet?

– Slopes Is there a break in the slopes where it is clear one group of sites

belongs together?

– Grouping Sites Common Questions

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Model Optimization – Wt’d Avg.

• Parameters to average: One-mile intercept, P1mi

Slope, m Foose Factor, Ff

Standard deviation,

• Area Averaging is done using Number of samples (LAMs) per site, Ni

Number of test sites within a given morphology type, L

L

1ii

L

1iii

N

NXX

L

1ii

L

1iii

N

NXX

L

1ii

L

1iii

2

N

N

L

1ii

L

1iii

2

N

N

f1mi Fm,,PX

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• Two sites in suburban morphology

Optimized prop.parameters:

Site Magnolia HibiscusIntercept -30.42dBm -30.16dBm

Slope -62.96dB/dec -62.19dB/decFoose f. 7.15dB 6.32dB

6.87dB 6.81dB

N 457 898

Site Magnolia HibiscusIntercept -30.42dBm -30.16dBm

Slope -62.96dB/dec -62.19dB/decFoose f. 7.15dB 6.32dB

6.87dB 6.81dB

N 457 898


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dBm45.62

898457

89819.6245796.62mi1

P

dB83.6898457

89881.645787.6 22

• Weighted average method:

dB/dec25.30

898457

89816.3045742.30

m

dB60.6898457

89832.645715.7

fF


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• Optimization Results– Rules for accepting/rejecting data– Methods of averaging

Weighted average Group by Morphology/Tower Height/ERP

– Applying average models

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Assign Avg. Model to sites not driven (manual assignments illustrated)• Make Cell Level assignment for most cases

• This level is preferred because x-Wizard runs faster and takes up less disk space.

• Make Tx Level assignments in special cases• One sector covers suburbs while the other two cover urban

Model Optimization – Applying Avg Model

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• The model can also be assigned using the Query/Edit Function

• Query on a common parameter (active state, for example)• Select Propagation Model under Data Fields and avg. model

<name> under Value• Select Use Cell Parameters under Data Fields and True under

Value

Model Optimization – Applying Avg Model

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Class Agenda – Clutter Optimization

• Review of Clutter– Overview– The Basic Routine– Analyzing the output

• Optimization Process– Overview– Area Tuning

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• This section of the class is offered . . .

– To explain the Clutter optimization routine

– To illustrate techniques used to optimize clutter

– To demonstrate how to create an average clutter adjustment file for use at sites that were not driven

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otherwise.i,0typeclutterinreceiverwhen1l

i,clutterthroughdistanceAggregated

i

i

types, clutter of NumberM

distance),(dB/adj.muliplierdistancelinearthroughPassc

),(dB/decademultiplierdistancelogthroughPassb

(dB),adjustmentclutterLocala

i

i

i

RX

TX

d3d4

d2

ClutterType 1

ClutterType 3

ClutterType 5

ClutterType 2

ClutterType 4

M

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1

log][

M

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1

log][

Clutter Optimization - Example

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Calculate the total RSL for the receiver that resides in a clutter type #5. Assume that RSLPM=-75dBm, DL=-5dBm, and a clutter adjustment file is given in a following table:

– Step#1 Find a clutter adjustment using the equation:

Clutter Type #

Distance [ft] li ai [dB] bi

[dB/decade]ci

[dB/Adj.dist.]

1 0 0 -2 -3 -0.22 50 0 -4 -3 -0.43 25 0 -6 -2 -0.64 50 0 -10 -4 -0.15 0 1 -4.5 -3 -0.4

Clutter Type #

Distance [ft] li ai [dB] bi

[dB/decade]ci

[dB/Adj.dist.]

1 0 0 -2 -3 -0.22 50 0 -4 -3 -0.43 25 0 -6 -2 -0.64 50 0 -10 -4 -0.15 0 1 -4.5 -3 -0.4

M

iiiiiii dcdbladBCA

1

log][

M

iiiiiii dcdbladBCA

1

log][

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– Step #2 Calculate the total RSL as:

dB

dBCA

42.115.429.344.119.2010

0*4.0

10

0log*)3(1*5.4

10

50*1.0

10

50log*)4(0*10

15

25*6.0

15

25log*)2(0*6

20

50*4.0

20

50log*)3(0*4

10

0*2.0

10

0log*)3(0*2

dBdBdBdBm

CADLRSLdBmRSL PM

42.9142.11575


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• The example in previous slide is performed for each bin in a prediction

• When optimizing, the routine – compares the measurements to the

predictions without clutter, then– Solves for the correct variables that will

bring the predictions and measurements as close as possible, then

– Repeats this for each measured data point

Tx

c c c c c

c c c

cc

c c c c c

c

c

c

c

c

cc c c

Clutter Optimization - Overview

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• How do you Optimize Clutter?– Optimize Clutter using the same drive test data you used for model

optimization.– Optimize Clutter by itself (i.e. use default, un-optimized model) OR– Optimize Clutter after Model Optimization

• Which variables should you Optimize?– Recommend that Local and at least one Pass Through variable be

optimized for best results

• Why Optimize Clutter?– Clutter optimization, in conjunction with Model Optimization, brings

out detail in the prediction– Even if you have old 7-class clutter, the affect on predictions can be

significant (especially for Permat)


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Before you start the clutter optimization:1. Make sure you have assigned appropriate Clutter height if you are

optimizing a Pass-Through variable

2. Make sure Clutter Adj. file is “zero’d out” before you start clutter optimization – the analysis gives you the difference from current

– Edit > Clutter Adjustment File OR Edit > Project under Basic Project Data

Clutter Optimization – Basics

1 2

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1. Requires associated measured data

– Available sites represents the sites with associated data

2. Selected Sites will be used to optimize clutter variables

– Double click Available site to add to the Selected sites

3. Optimize Local and/or Pass Through variables

• It is recommended that you include at least one Pass Through variables


Dbl.Click

Analysis > Clutter Optimization

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Notes on Pass-Through Clutter:1. It is wise to include Local and Pass-through Log for all clutter

optimizations

2. Optimize Pass-through Linear adjustments in urban or dense urban settings

– Linear pass-through variable can be problematic in trees or residential areas.


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The output of the routine comes in two forms:1. Tabular – list of optimized variables and statistics

2. Map Plot – the Delta between measured and predicted data AFTER the optimized clutter is applied

Clutter Optimization – Output

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Tabular Variables and Statistics1. “Save” will save the Clutter Adj. to Common Folder

– The new, optimized Clutter Adj. can be used at sites not driven2. The routine will ‘zero-out’ any positive pass-through variables and then

re-adjust matrix.– Positive pass-through variables will cause signal re-birth

1

2


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Note on Signal Re-Birth– If x-Wizard allowed the routine to keep positive pass through variables, there is a chance

that the signal would INCREASE as you went farther from the site.– The distance traveled through a clutter type will increase– If the variable is positive, it can become quite large.

– Example: – linear pass-through variable, C = +0.02 dB/ft; – distance passed through = .75 miles (3,960 ft)– With a positive pass-through variable, the signal regains –

+0.02 dB/ft * 3,960 ft = 79.2 dB!


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– Make note of the overall Mean and Std. Dev. Only # of Local Points are reported, BUT Total points used in optimization will include pass through ‘points’

– Local Adjustments may be positive– Do not over analyze the results

The variables are the result of a matrix operation The ultimate test is how well the adjustments predict certain areas

Clutter Optimization - Output

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Mapped Delta Plot1. Look for areas where the Delta is consistently high

– This may indicate a problem area (i.e. Open area clutter optimized from urban data)

2. It is helpful to Format > Band Colors so that the colors indicate an absolute value (i.e. Green has range of +/- 5dB)


a

b

Area ‘a’Mostly greens and yellowsindicate that the optimizationworked well in this area

Area ‘b’Mostly reds and orangesindicates that the optimizationdid not perform well in this area.

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The goal of any optimization is to balance accuracy with the effort/time required

• Things to consider:– How many sites will have to be driven?– How much data post-processing and analysis is

required?– How much money and time will it take?– What will be the resulting accuracy?– How easy will it be to assign model/clutter to a new site?


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Easiest methods produce mediocre accuracy1. Model Only – Use no clutter and tune only the model

Requires about 10% to 15% of sites be driven One step post-processing using the Batch Mode Expect 6 to 10 dB Std. Dev. but not much detail Depending on area, you can easily assign models to new sites

2. Clutter Only – Use a default model and tune only the clutter adjustments Requires about 10% to 15% of sites be driven One step post-processing Expect 7 to 10 dB Std. Dev. but no one clutter adjust fits all

areas well Depending on area, you can easily assign adjustments to new

sites


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Hardest methods produce superior accuracy1. Hyper-tuning – create site specific models and clutter

Requires you to drive test each and every site Must develop both tuned model and tuned clutter for each site Expect 3 to 6 dB Std. Dev. Strictly speaking, new sites must be driven.

– Prior to being built, you would have to derive an average from the surrounding sites.


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Area method represents a happy medium effort and accuracy

Network is divided into regions that represent a different RF environments

Requires about 10% to 15% of sites be driven per region Three step post-processing using the Batch Mode to tune the

model, optimize clutter and then re-optimize the model. Expect 4 to 8 dB Std. Dev. with some good detail Areas must be well defined so that you can easily assign models

clutter to new sites


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Clutter Optimization – Area Method

– x-Wizard handles Transition Areas through the Area Tuning Optimization technique

– Model Tuning provides overall accuracy– Clutter Optimization provides detail accuracy

Un-optimized predictionsshow large area of strongcoverage and interferingsignal in DT Philly

NJ SuburbDT Philly Transition

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Step One: Tune the Model w/o Clutter– Using Batch mode, optimize all sites that belong to a particular

morphology– Use the results to derive an average– Assign the exact optimized models to the sites that were driven

Provides better clutter tuning in the next step

• Band radii generally round• No shadowing from clutter• Note broad area ofinterfering signal in DT Philly


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Step Two: Clutter Optimization– Using the same sites and same data, tune the clutter using sites

with tuned models– Note that the avg. std. dev. may not improve much and may even

get slightly worse. You are looking to bring out detail in the predictions caused by clutter

This example used anAvg. Opt. Model &Opt. Clutter for Suburbson a site that was notused in the optimization


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Step Three: Re-Optimize Model with tuned Clutter– This step is required to ‘normalize’ the model to the tuned

clutter The average model derived at this stage is rarely much

different than the average model derived from Step One The avg. std. dev. may improve slightly at this stage,

however


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Utf 8_wizard Adv (Day Two)

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