AircomPlugIn Overview

www.awe-communications.com

WinProp Multi Scenario Model

for Aircom Enterprise

July 2011 (c) by AWE Communications 2

Outline

Company

About the WinProp plug-in

Configuration of the WinProp plug-in

Advantages of the WinProp plug-in

Customers


Company

AWE Communications was founded in 1998

Fully independent of any external investors (all shares owned by employees of the company)

Long term vision

Spin-Off from University of Stuttgart, Germany

Strong scientific background

Engaged in international, European, and national research projects (in the field of wave propagation)

Specialized on wave propagation models

Located in southern Germany (Region of Stuttgart)

Development of customer specific software packages in the area of wave propagation

Institute of RF Technology. University of Stuttgart

Offices of the R&D team of AWE Communications


Urban

Rural / Suburban

Satellite

Indoor

Tunnel

Time-Variant

2000 2001 2002 2003 2004 2005 2006 2007 2008

CNP (Urban Indoor)

Continuous expansion of supported environments

Range from small scale indoor to rural and satellites

Propagation models for each environment already sold to customers

Still significant growing since 2003

Company


One propagation model for any type of scenario: macro-cell, mini-cell, pico-cell

Support of all air interfaces: 2G, 2.5G, 3G, HSPA, LTE, WiMAX

Frequency range: 300 MHz to 30 GHz

Very short prediction times

Accurate results (std. dev. less than 7 dB compared to measurements)

Support of all kinds of map data: Pixel (topography, clutter, buildings) and vector data (buildings, vegetation objects)

Prediction of indoor coverage based on indoor walls (if available in vector building database)

Auto tuning with measurements


Multi Scenario Model: Key facts


Multi Scenario Model: Features

Semi-deterministic approach

Computation of dominant path with full 3D approach

Unlimited number of interactions for each path

Consideration of wave guiding in street canyons

Penetration into buildings and computation of coverage inside buildings based on semi-deterministic approach

Accuracy similar to ray tracing

Computation time similar to empirical models

Auto calibration with measurements



Realistic wave guiding in street canyons


Consideration of 3D vector building data

Prediction with topography and 3D vector buildings


Computation of indoor coverage


Prediction of coverage inside buildings based on indoor walls

Each wall can have individual material properties

Transmitter inside buildingDeterministic indoor coverage with urban vector data and indoor vector data of selected buildings

Transmitter outside buildingDeterministic indoor coverage with urban vector data and indoor vector data of selected buildings

* Possible if a WinProp vector database is used for predictions



Fixed heights and clearance (separation)

Statistically distributed heights and clearance: More realistic approach

Consideration of clutter data

Clearance

Urban

Vegetation

Residential

Height

⇒ Std. dev. of heights can be defined

⇒ Ratio of “open” clearance can be defined


Additional outputs for post processing


Visualization of results in WinProp ProMan standalone tool

- Delay spread, angular spread

- Power delay profile, angular profile

- Propagation paths


Automatic calibration of model/clutter


Calibration of model parameters based on measurement data

Calibration of clutter losses also supported

Separate tool available for selection of calibration method and management of calibration files


Antenna height: 10 m



Vegetation modeling in a city center

Consideration of vegetation objects


* Possible if a WinProp vector database is used for predictions

Vegetation blocks lead to higher attenuations if rays pass the objects

Arbitrary blocks possible (woods, fields, etc.)


Highly accurate results


Comparison between predictions and CW measurement data

Std. Dev. below 7 dB

Mean Value between -3 and +3 dB

Scenarios analyzed:

Canada: Toronto

China: Hong Kong

Finland: Helsinki

Germany: Cologne, Munich, Stuttgart

Indonesia: Jakarta

Ireland: Dublin

Italy: Lucca, Pisa

Japan: Tokyo

Monaco: Monte Carlo

Poland: Warsaw

Spain: Barcelona

Turkey: Istanbul

Uruguay: Montevideo

USA: New York City, Schaumburg


Short prediction times


Prediction time

about 25 secabout 50 sec3 km

about 15 secabout 20 sec2 km

-about 1 sec500 m

2 sec5 sec 1 km

Vector buildings Clutter dataCell radius

Scenario: Resolution 10 m, dense urban areaPC: 2 GHz CPU, 2 GB RAM


Map Data


Selection of map data which will be considered during prediction

Mapinfo vector data directly supported

Considered mapdata duringprediction

Consideration of clutter data

Configuration of vector data


Land usage

Clutter/land usage parameters depending on frequency

Different approaches for determination of clutter attenuations

Definition of frequency bands

Definition of clutter parameters

List with all clutterclasses



Model Settings 1

Model parameters which influence the wave propagation computation

Definition of pathloss exponentsand break point

factor

Interaction lossesdue to

diffractions, transmissions and vegetation objects



Model Settings 2

Further parameters for additional results


Additional outputsfor post processing

Indoor coverageanalysis based on

indoor walls


Customers

Research Center

..and many more.


Further information

www.awe-communications.com

AircomPlugIn Overview

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