Propagation cnp

Propagation Models & Scenarios:
Hybrid Urban  Indoor

© 2012 by AWE Communications GmbH

www.awe-com.com

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

2012 © by AWE Communications GmbH 2

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

Vector data
Database type Raster data Vector data
Raster data

Topography 2.5D building (vector) 3D building
Database
Clutter Topography (pixel) 3D indoor objects

Hata-Okumura Knife Edge Diffraction Motley Keenan
Path Loss Two Ray COST 231 WI COST 231 MW
Prediction Models Knife Edge Diffraction Ray Tracing Ray Tracing
Dominant Path Dominant Path Dominant Path

r < 30 km r < 2000 m
Radius r < 200 m
r > 2 km 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)
• Penetration into buildings
with complex structure inside

• Transmitters located inside buildings
(micro BTS, Repeater, WLAN, …)
interfering with outdoor network

Modeling whole scenario in indoor
mode?
Computational demand too high
for large scenarios!



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

• 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
3D Mode
into a different building from an
 Multiple prediction layers analyzed indoor transmitter
 Path finding in 3D
 Highly accurate


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


Combined Scenarios: Evaluation

Scenario I: Campus of University of Stuttgart, Germany

Penetration
Scenario! Scenario Information
Material concrete and glass
Total number of objects 1893
Number of walls 1004
Resolution 1.0 m
Transmitter height 40.0 m
3D view of database Prediction height 17.0 m




Prediction with 3D Prediction with 3D
Dominant Path Model Dominant Path Model
for transmitter 3 for transmitter 4




Difference of prediction with DPM and Difference of prediction with DPM and
measurement for transmitter 3 measurement for transmitter 4

Statistical Results for Dominant Path Model
Site
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

Propagation cnp

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