Propagation cnp

Propagation Models & Scenarios:

Hybrid Urban Indoor

© 2012 by AWE Communications GmbH

www.awe-com.com

http://www.awe-com.com/

2012 © by AWE Communications GmbH 2

Contents

• Overview: Propagation Scenarios

• Scenario: Rural and Suburban

Pixel Databases (Topography and Clutter)

• Scenario: Urban

Vector databases (Buildings) and pixel databases (Topography)

• Scenario: Indoor

Vector databases (Walls, Buildings)

• Combined Network Planning

Hybrid Rural Urban Indoor Scenarios

Pixel and Vector Databases


Propagation Models

Propagation Scenarios (1/2)

Different types of cells in a cellular network

• Macrocells

• Cell radius > 2 km

• Coverage

• Microcells

• Cell radius < 2 km

• Capacity (hot spots)

• Picocells

• Cell radius < 500 m

• Capacity (hot spots)


Propagation Models

Propagation Scenarios (2/2)

Macrocell

Microcell

Picocell

Database type

Raster data

Vector data

Raster data

Vector data

Database

Topography

Clutter

2.5D building (vector)

Topography (pixel)

3D building

3D indoor objects

Path Loss

Prediction Models

Hata-Okumura

Two Ray

Knife Edge Diffraction

Dominant Path

Knife Edge Diffraction

COST 231 WI

Ray Tracing

Dominant Path

Motley Keenan

COST 231 MW

Ray Tracing

Dominant Path

Radius

r < 30 km

r > 2 km

r < 2000 m

r > 200 m

r < 200 m


Propagation Models

Propagation Models

• Different types of environments require different propagation models

• Different databases for each propagation model

• Projects based on clutter/topographical data or vector/topographical data

• Empirical and deterministic propagation models available

• CNP used to combine different propagation environments

Types of databases

• Pixel databases (raster data)

• Topography, DEM (Digital Elevation Model)

• Clutter (land usage)

• Vector databases

• Urban Building databases (2.5D databases polygonal cylinders)

• Urban 3D databases (arbitrary roofs)

• Indoor 3D databases


Combined Scenarios (Urban Indoor)

Combined Network Planning (CNP): urban indoor

Motivation (1/2)

Modeling whole scenario in indoor

mode?

Computational demand too high

for large scenarios!

• Penetration into buildings

with complex structure inside

• Transmitters located inside buildings

(micro BTS, Repeater, WLAN, …)

interfering with outdoor network



Combined Network Planning (CNP): urban indoor

Motivation (2/2)

• Indoor penetration

If transmitter located outdoor indoor walls should be considered

but two environments involved (urban & indoor)

which propagation environment should be used?

• Radiation from indoor transmitters and interference with outdoor environment

If transmitter located indoor (e.g. repeater) the interference with the outdoor environment of interest

but two environments involved (urban & indoor)

which propagation environment should be used?



CNP Prediction: urban indoor

3D Mode

Multiple prediction layers analyzed

Path finding in 3D

Highly accurate

• Combination of urban and indoor

prediction

• Dynamic resolution of results:

Indoor higher resolution than urban

• Automatic adaptation of parameter

settings (path loss exponents,

interaction losses,..) if a transition

between urban and indoor

environment occurs

• Multiple transition from indoor

outdoor indoor are possible to

include e.g. the indoor penetration

into a different building from an

indoor transmitter



CNP Database: urban indoor

• Shape around indoor database (polygonal cylinder)

• Indoor database (with indoor walls and objects) is

imported into urban building database

• Shape of indoor database represents the building when

using the urban propagation model

• Rays are handled by using the Angular Power Delay

Profile (APDP) for the transition between the models

(includes field strength, delay time, angles of incidence)

Allows the prediction of

delay spread and impulse

response



CNP Database: urban indoor

• Urban database (polygonal cylinders) of the surrounding environment can be saved in indoor data format (i.e. as polygonal planar objects) for CNP

database

• Indoor databases (with walls inside buildings) can be imported into the urban database to substitute selected shapes of buildings by their indoor structure

• The resulting database is saved as urban database and the project is also handled as urban propagation project (incl. the (indoor walls of selected buildings)



CNP Prediction: urban indoor

• Rays in urban scenario reaching the shape of the indoor database are followed in the other environment with the corresponding propagation model

• Multiple transition from indoor outdoor indoor are possible to include e.g. the indoor penetration into a different building from an indoor transmitter

• Transition COST 231 WI COST 231 MW is possible

• Transition Urban Dominant Path Indoor Dominant Path is possible

• Transition IRT Urban COST 231 MW is possible

• Transition IRT Indoor IRT Urban is possible

• Handled in urban project

• If indoor walls at a building are detected the indoor coverage is computed with consideration of the indoor walls

• If transmitter is located inside building and if indoor walls of this building are available the CNP module is automatically activated



Examples CNP urban indoor

Indoor coverage

for outdoor transmitter



Examples CNP indoor urban

Outdoor coverage for indoor transmitter



Example urban indoor: Base Station on Top of Building

Indoor coverage for outdoor transmitter



Example indoor urban: WLAN AP inside Building

Outdoor coverage for indoor transmitter



Example: Indoor Urban

Omni-directional antenna in the highest floor of an office building

Computed with the Dominant Path Model


Combined Scenarios (Rural Urban Indoor)

Example: Rural (Topo) / Urban (Buildings) / Indoor (Walls)

Omni-directional antenna on a hill in the Hong Kong area


Combined Scenarios (Rural Urban Indoor)

Example: Rural (Topo) / Urban (Buildings) / Indoor (Walls)

Coverage inside a building (multiple floors) due to an

omni-directional antenna on a hill in the Hong Kong area



Evaluation with Measurements

Investigated Scenario:

I. Campus of University of Stuttgart, Germany


Scenario Information

Material concrete and glass

Total number of objects 1893

Number of walls 1004

Resolution 1.0 m

Transmitter height 40.0 m

Prediction height 17.0 m

Combined Scenarios: Evaluation

Scenario I: Campus of University of Stuttgart, Germany

Penetration Scenario!

3D view of database




Prediction with 3D Dominant Path Model

for transmitter 3

Prediction with 3D Dominant Path Model

for transmitter 4




Difference of prediction with DPM and measurement for transmitter 3

Difference of prediction with DPM and measurement for transmitter 4

Site

Statistical Results for Dominant Path Model

Mean Value [dB]

Std. Dev. [dB]

Comp. Time [s]

3

0.90

5.43

154

4

4.26

7.48

156

Remark: Standard PC with an AMD Athlon64 2800+ processor and 1024 MB of RAM


Summary

Features of WinProp Hybrid Urban Indoor Module

• Highly accurate propagation models

Empirical: Multi Wall

Deterministic (ray optical): 3D Ray Tracing, 3D Dominant Path

Arbitrary number of transitions (from indoor to urban and vice versa) within one path

Optionally calibration of 3D Dominant Path Model with measurements possible

• Building data

Models are based on 3D vector (CAD) data (indoor) and 2.5D vector building data (urban)

Consideration of material properties (also subdivisions like windows or doors)

• Antenna patterns

Either 2x2D patterns or 3D patterns

• Outputs

Predictions on multiple heights simultaneously

Signal level (path loss, power, field strength)

Delays (delay window, delay spread,…)

Channel impulse response

Angular profile (direction of arrival)


Further Information

Further information: www.awe-com.com

http://www.awe-com.com/

Propagation cnp

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