Propagation Models & Scenarios - Hybrid Urban PropagationCNP

8/3/2019 Propagation Models & Scenarios - Hybrid Urban PropagationCNP

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Propagation Models & Scenarios:

Hybrid Urban Indoor

© 2007 by AWE Communications GmbH



Aug 2007 © by AWE Communications GmbH 2

• 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 PlanningHybrid Rural Urban Indoor Scenarios

Pixel and Vector Databases

Contents




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 Scenarios (1/2)

Propagation Models




Motley Keenan

COST 231 MW

Ray Tracing

Dominant Path

Knife Edge Diffraction

COST 231 WI

Ray Tracing

Dominant Path

Hata-Okumura

Two Ray

Knife Edge Diffraction

Dominant Path

Path Loss

Prediction Models

r < 200 mr < 2000 m

r > 200 m

r < 30 km

r > 2 kmRadius

3D building

3D indoor objects

2.5D building (vector)

Topography (pixel)

Topography

ClutterDatabase

Vector data Vector data

Raster dataRaster dataDatabase type

PicocellMicrocellMacrocell

Propagation Scenarios (2/2)

Propagation Models




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

Propagation Models

• Different types of cells require different propagation models

• Different databases for each propagation model

• Rural, urban, and indoor projects with different options

• Empirical and deterministic propagation models available

• CNP used to combine different propagation environments

Propagation Models


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Problem (Motivation)

Combined Scenarios (Urban Indoor)

Combined Network Planning (CNP): urban indoor

Penetration into buildings

with complex structure inside

Transmitter located inside buildings(micro BTS, Repeater, WLAN, …)

interfering with outdoor network

Modeling whole scenario in indoor

mode?

Computational demand too high

for large scenarios!



Problem (Motivation)

• Indoor penetration

If transmitter located outdoor it

should consider indoor walls

but two environments involved

(urban & indoor)

which propagation environmentshould 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?


Combined Network Planning (CNP): urban indoor




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 indoorenvironment occurs

• Multiple transition from indoor

outdoor indoor are possible to

include e.g. the indoor penetrationinto different buildings from an

indoor transmitter

• 3D Mode

Only needed if horizontal objects arelocated between prediction plane and

transmitter plane

Multiple prediction layers analyzed

Path finding over multiple floors

Highly accurate



• 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 whenusing 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) as CNP

database

• Indoor databases (with walls inside

buildings) can be imported into the CNPdatabase to substitute selected shapes

of buildings by their indoor structure

• The resulting database is saved as

indoor database and the project is also

handled as indoor propagation project

(incl. the (urban) shape of the

neighboring buildings)

CNP Database: urban indoor



• Rays in one environment reaching the shape of the indoor database are

stored and later traced in the other environment with the corresponding

propagation model

• Multiple transition from indoor outdoor indoor are possible to includee.g. the indoor penetration into different buildings from an indoor transmitter

• Transition COST 231 WI COST 231 MW is possible

• Transition Urban Dominant Paths Indoor Dominant Paths is possible

• Transition IRT Urban COST Indoor 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.


CNP Prediction: urban indoor



Propagation Models: Dominant Path Model

Determination of Paths

Analysis of types of walls in scenario

Generation of tree with walls

Searching best path through walls

Computation of path loss

1

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Transmitter

Layer 2

Layer 3

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Path length d

Path loss exponent p

individual interaction losses f( φ ,i) for each interaction i of all n interactions

Penetration loss t j for all m transmissions through walls

Gain due to waveguiding wk

at c pixels along the path

Gain g t of base station antenna

Power pt of transmitter

0 0 0

dBµV 1104.77 10 log ( , )m m

n m c

j k t t

i j k

d e p f i t w g pc

= = =

= − ⋅ ⋅ − ϕ − + + + ∑ ∑ ∑


Computation of field strength/path loss





Parameters for prediction (1/3)





Parameters for prediction (2/3) Large areas enable Adaptive Resolution Management

(resolution of result must be fine because of indoor structures, but for outdoor

areas larger resolution is sufficient) CNP propagation does not consider breakpoint

Definition of different path loss exponents for

LOS (line of sight)

OLOS (obstructed line of sight => no transmission through a wall)

NLOS (non line of sight => at least one transmission through a wall)

2D and 3D mode

2D only if transmitter and receiver are in the same floor

3D must be used for all hybrid scenarios (TX on neighboring building) and

if multi floor buildings are analyzed

Vertical distance between prediction

layers must be defined Prediction at all layers + TX layer





Parameters for prediction (3/3)

Interaction losses (at points where the path

changes its orientation) can be defined depending

on the number of interaction in the propagationpath (e.g. higher losses could be assigned to the

first interactions)

Interaction losses can be defined individually for

vertical and horizontal interactions (e.g. to distinguishbetween rooftop and corner diffraction)

Interaction losses are defined independent of material

properties of buildings (calibration is more convenient)

Interaction losses can be weighted with angle in propagation path (0° leads to noloss and 90° to medium loss and 180° to max. loss)

Interaction losses can be defined individually for indoor walls and for urban building

shapes

Individual reflection loss assigned to walls influences waveguiding effect




Propagation Models: Preprocessing with WallMan

Single preprocessing of building database required once for each database


Project FilePreprocessing

(*.pre)

Preprocessing

(Computation)

Preprocessed

Database Files

(oib, ocb opb)

Original Binary

Database file

(*.odb)

Database Extensions:

*.odb Outdoor Data Binary

*.ocb Outdoor COST Binary

*.oib Outdoor IRT Binary

*.opb Outdoor Dom. Path Binary

Materials (electrical properties) canstill be modified after preprocessing.

Re-assignment of materials to objectsis not possible after preprocessing.

C b d S ( b d )



Indoor Coverage

Outdoor Transmitter

(1500 MHz)


Example: Urban Indoor

C bi d S i (U b I d )



Antenna (2.1 GHz, 40 dBm) located on top of building

Prediction in neighbor building on 4 layers inside the building


Example: Urban Indoor: UMTS Base Station on Neighbor Building





Example: Urban Indoor: GSM Base Station on Top of Building

f = 948 MHz

Omnidirectional antenna





Antenna (948 MHz) located in dense urban area

Prediction inside building on 7 layers

Example: Urban Indoor: GSM coverage in multi floor office building






Outdoor Coverage, Indoor Transmitter (948 MHz)


Example: Indoor Urban






Example: Indoor Urban: WLAN Access Point (Inside Building)

f = 2400 MHz

Omnidirectional antenna





Antenna (2.4 GHz) located inside multi floor building

Prediction on 4 layers inside building

Example: Indoor Urban: WLAN Access Point (Inside Building)







Omnidirectional GSM antenna in the highest floor of an office building,Computed with the Dominant Path Model in CNP mode







Omnidirectional GSM antenna in the highest floor of an office building,Computed with the Dominant Path Model in CNP mode


Combined Scenarios (Rural Urban Indoor)





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

Omnidirectional antenna on ahill in the Hong Kong area






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

Coverage inside a building (multiple floors) due to anomnidirectional antenna on a hill in the Hong Kong area





Investigated Scenario:

I. Campus of University of Stuttgart, Germany

Evaluation with Measurements


Combined Scenarios: Evaluation




1.0 mResolution

17.0 mPrediction height

40.0 mTransmitter height

concrete and glassMaterial

1893Total number of objects

1004Number of walls

Scenario Information

3D view of database

Scenario I: Campus of University of Stuttgart, Germany

PenetrationScenario!






Prediction with 3DIndoor Dominant Path

Model for transmitter 3

Prediction with 3DIndoor Dominant Path

Model for transmitter 4

For the predictions inthis scenario, the 3Dmode was used.







Difference of prediction with IDP andmeasurement for transmitter 3

Difference of prediction with IDP andmeasurement for transmitter 4

1567.484.264

1545.430.903

Comp. Time [s] Std. Dev. [dB] Mean Value [dB]

Statistical Results for Indoor Dominant PathSite


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


Combined Scenarios: Summary




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 pathOptionally 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)

Combined Scenarios: Summary



AWE CommunicationsOtto-Lilienthal-Str. 36

D-71034 Boeblingen

Germany

Phone: +49 7031 71497 - 19

Fax: +49 7031 71497 – 12

[email protected]

www.awe-communications.com

Propagation Models & Scenarios - Hybrid Urban PropagationCNP

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