3G Workshop May 2005 - Richard Edge.ppt

New Concepts for Optimization
Enhanced Approaches Using Measured Data

presenter Richard Edge


Contents
Developing a new concept - Veritune
Validating the Veritune concept
Developing a practical solution
Conclusions


Developing a
new concept
- Veritune

Is this really the best way of
doing things?

(Picture – Testing Golf Clubs by Heath-Robinson)


The importance of antenna optimization in
UMTS
If a cell causes too much
interference in a UMTS /
WCDMA network…
… frequency planning is no
longer an option…
…the only options are

power and antenna
optimisation.

Downtilt, azimuth and power
changes control are used to
control radio propagation

Antenna optimisation in
UMTS is both more frequent
and important than in GSM
networks.


A typical antenna optimization process
Changes identified by:

 Predicted data
 ‘Ranging’ spreadsheets
 Experience…
Antenna
changes
in field

KPI analysis

cost

Uncertain results
Time-consuming

Field
drive drive 03
Optimized!
Validation
02
01

Identify changes to
antenna coverage
time


How do we speed up the process?
Reducing the loops is key
to reducing time to revenue:
 Get an acceptable solution
as early as possible.

How do we improve the
certainty that our changes
will work?

Field
drive
Validation
Drive
Test drive 02
01

Time consuming

Antenna
changes
in field

KPI analysis

Relatively quick

Identify changes to
antenna coverage


Some principles for a new concept in
optimisation to reduce the number of loops
Improve prediction accuracy by simplifying
the problem (rather than increasing the
complexity of the model)
Wherever possible use real data (rather
than predicting measurable information)
Guide the user towards the solution (rather
than prescribing a solution automatically)
Present results in the same format as
measured data (rather than presenting a grid
of data)

(Picture – NASA Space Pen)


Review – how a planning tool predicts network
performance

Antenna Masking

Required inputs: Map database, antenna
performance and configuration

Interfering
Cells

Modified
Cell

Link Budget

Required inputs:
Equipment
performance &
configuration.

Path Loss

Required inputs: Map
database, model
diffraction/penetration/
reflection assumptions.

Interference Generation

Required inputs: Map database, model
diffraction/penetration/ reflection assumptions, antenna
performance and configuration, traffic geographical

distribution, building penetration, interferer link budget, traffic
modelling, power control, RRM modelling, mobile location in
buildings.


The planning tool prediction process
Traffic
Model
Map Data

Pathloss
Prediction

Propagation
Model
Antenna Masking
Model

Pilot EcNo

Site
Configuration
Channel Model
Traffic Map

Pilot
RSCP

RSSI
Service

EbNo
Served
Users


Applying the principles - Veritune
Antenna Masking

Required Inputs: Map database, antenna
performance and configuration

Interfering
Cells

Modified
Cell

Link Budget
Not Required.

Path Loss

Measure Signal Strength.

Interference Generation

Required Inputs: Map database, antenna performance and
configuration of changed interfering cells.

The Veritune prediction process
Measured
Pilot EcNo
Measured
Pilot RSCP
Antenna Masking
Model
Map Data
Site
Configuration

Pilot RSCP

Pilot EcNo


An improved antenna optimisation process
with Veritune
Optimized!
Field
Validation
drive drive 02
01

Optimisation loops are
taken out of the field onto
the desktop

cost

Antenna
changes
in field

KPI analysis

Less cycles
saves time,
reduces cost

time
Traditional process
With Actix Veritune

Actix Veritune
on desktop


Validating the Veritune concept


Validation approach
Actix Veritune

Before
Drive

Configuration
Changes

Traditiona
l Tuning

Synthetic
Drive
Comparison

After
Drive

• Validation was carried out using historically collected data.
• Synthetic drives calculated off ‘before’ measured drives
were compared to ‘after’ measured drives, and the error at
each data bin calculated.


Input measurements
Trials of 4 clusters (33 sector changes, 20 control sectors)
have been carried out across a variety of rural, suburban
and urban environments, in flat and hilly terrain.
A total of 8545 data bins were considered, approximately
427km driving.
No data filtering was carried out:
 Sector heights ranged between 12m and 48m
 Measurements both within and beyond the main lobe by
bearing were considered
 All ranges were considered up to 14km (including less than
500m)

The validation results
Standard deviations significantly improve upon typical
propagation model accuracies (benchmark 8dB – top of the below
graph).
The mean error was within 1dB of the control set for all clusters.
8
Standard Deviation (dB)

7
6
5
Control

4

Veritune

3
2
1
0
Rural/Suburban A

Rural/Suburban B

Urban

Hilly dense urban

4.8dB

without 2
outlying
sectors


How consistent are the results?
A majority of sectors exhibited consistent performance:
20

Number of Sectors

18
16

standard deviation

14

mean

12
10
8
6
4
2
0
0 to 1

1 to 2

2 to 3

3 to 4

4 to 5

5 to 6

6 to 7

7 to 8

8 to 9

9 to 10

Range (dB)

Outlying
sectors


Distribution of error with distance
Error Plot with Distance
30.00

20.00

Error (dB)

10.00

0.00

Rural

-10.00

-20.00

-30.00
0

2000

4000

6000

8000

Distance (m)

10000

12000

14000

Developing a practical solution


Implementing the principles in practice

1

2

3

Enabling 3 easy steps to fast, in-office optimization:

Step 1. Identify poorly performing areas
Step 2. Review guidance towards possible solution
Step 3. Simulate the effect of antenna changes