Propagation urban

Propagation Models & Scenarios:
Urban

© 2012 by AWE Communications GmbH

www.awe-com.com

Contents

• Overview: Propagation Scenarios
- Rural and Suburban: Pixel Databases (Topography and Clutter)
- Urban: Vector databases (Buildings) and pixel databases (Topography)
- Indoor: Vector databases (Walls, Buildings)

• Wave Propagation Model Principles
- Multipath propagation
- Reflection
- Diffraction
- Scattering
- Antenna pattern

• Topography and Vector Data (buildings and/or vegetation)
- Map data
- Propagation models
- Evaluation with measurements

2012 © by AWE Communications GmbH 2

Propagation Scenarios

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 Scenarios
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


Wave 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


Topography and Vector Data

Databases: Vector Building Databases

• 3D vector oriented database
• Buildings as vertical cylinders
with polygonal ground-planes
• Uniform height above street-level
Example: New York
• Limitation to vertical walls and flat roofs
• Individual material properties of building surfaces
• Topography can be considered optionally

Consideration of Topography for Vector Scenarios
Topographical databases:
• Topography in pixel databases
• Resolutions of 20-30 m

Consideration in Prediction:
• Shift transmitter and receiver
• Shift buildings due to the topo
• Approximation of topo with triangles

Effects on results:
• Additional shadowing by hills
• Changing LOS-area of the transmitter
• No additional rays (scattering at topo)


Databases: Vector Building Databases

Special features

Courtyards and Towers

Vegetation areas
Vegetation areas are polygonal cylinders.
Rays get an additional attenuation (dB/m)
when passing the cylinder and receiver
pixels inside cylinder get an additional loss
Multiple Courtyards and Towers


Databases: Material Properties
Global catalogue for different construction materials (at various frequency bands)

(In WallMan via menu Edit  Materials  Import)

 User can add or modify materials


Local material database (in building database)
• only relevant for objects in this database
• independent of global material catalogue
(modification of global catalogue does not affect material properties of objects in database)

• can be updated with materials from global material catalogue

Settings of local material database
• individual material properties for different frequency bands
(always the properties of the frequency band closest to TX frequency is used)

• Material (incl. all properties) is assigned to objects (walls/buildings)
• Always all material properties must be defined even if they are not
required for the selected propagation model
• Individual colors can be assigned to the materials for better visualization


Properties of a material
• Properties affecting all propagation models
Transmission Loss (in dB)
• Properties affecting Ray Tracing & Dominant Path Model
Reflection Loss (in dB)
• Properties affecting Ray Tracing
• GTD/UTD related properties
• Relative Dielectricity
• Relative Permeability
• Conductance (in S/m)

• Empirical reflection/diffraction model
• Reflection Loss (in dB)
• Diffraction Loss Incident Min (in dB)
• Diffraction Loss Incident Max (in dB)
• Diffraction Loss Diffracted (in dB)


Propagation Models
• COST 231 Walfisch-Ikegami
• Homogenous parameters (street width, building
height,…) for whole area
• Individual determination of parameters according
to buildings in vertical plane between Tx and Rx

• Ray Tracing
• 3D Ray Tracing IRT (with preprocessing)
• 2x2D Ray Tracing IRT (horiz. and vertical plane)
• 3D Ray Tracing SRT (standard, no preprocessing)

• Dominant Path Model
• 3D path searching



Propagation Models: COST 231 Walfisch-Ikegami
• Model accepted by ITU-R

• Evaluating building profile between transmitter and
receiver (vertical plane)

• Consideration of additional losses due to building data

• Reasonable results for Tx above rooftops
For Tx below rooftops limited accuracy (no wave guiding)

• No multipath propagation considered

Transmitter Considered propagation path Receiver
Buildings considered for determination of parameters


WinProp: Vertical plane is analyzed for each predicted pixel individually!

Parameters of the model obtained from the buildings in the vertical plane

ht hr
h Roof w

b
d

• Height of transmitter hTX • Mean value of building heights hroof

• Height of receiver hRX • Mean value of widths of roads w
• Mean value of building separation b

Vertical profile with topography


Parameters of the model gained from the buildings in the vertical plane
d f
LOS: lb  42,6 dB  26  lg  20  lg
km MHz
l0  lrts  l msd l rts  lmsd  0
NLOS: lb 
l0 lrts  lmsd  0
f r
Free space loss l0 : l0  32,44 dB  20  lg  20  lg
MHz km
w f h  r
h
Rooftop loss lrts : lrts  16,9 dB  10  lg  10  lg  20  lg Roof
m MHz m
d f b
Over rooftop loss lmsd : lmsd  lbsh  k a  k d  lg  k f  lg  9  lg
km MHz m
 ht  Roof 
h
 18  lg1   ht hRoof
with lbsh   m 
0 ht  hRoof
Factors k a and k d Valid for: f MHz ................... 800 - 2000
Empir. Correction of antenna heights ht m ................................. 4 - 50
Faktor k f hr m ................................. 1 - 3
Adaption to different building
densities d m ........................... 20 - 5000


Propagation Models: Ray Tracing
• Multipath propagation
• Dominant effects:
diffraction and reflection
• Up to 6 reflections and 2 diffractions are
determined as well as combinations
• Computation of the path loss with
Fresnel coefficients (for reflection) and
GTD/UTD model (for diffraction).
Alternative: Scalable empirical
reflection/diffraction model for
prediction of path loss along the ray
• Uncorrelated superposition of
contributions (rays)
• Either full 3D or 2x2D (horizontal and
vertical plane)
• Post-processing with Knife Edge
Diffraction model possible



Types of rays to be determined

• Different types of rays: direct,
reflected, diffracted, scattered
• Definition of max. number for
each interaction type
• Definition of total interaction
number
• Selection of Fresnel & GTD/UTD
or empirical interaction model
• Additional thresholds for
computation of paths




Direct Single
Reflection

Double Single
Reflection Diffraction




Triple Single

Reflection Reflection +
Single
Diffraction

Double Double
Diffraction Reflection +
Single
Diffraction



Propagation Models: Intelligent Ray Tracing (IRT)

Considerations to accelerate the time consuming process of path finding:

• Deterministic modelling generates
a large number of rays, but only few
of them deliver most of the power

• Visibility relations between walls and
edges are independent of transmitter
location

• Adjacent receiver pixels are reached
by rays with only slightly different paths

 Single pre-processing of the building database with determination of the
visibility relations between buildings reduces computation time


Pre-processing of the Building Database
• Subdivision of the walls into tiles
• Subdivision of the vertical and
horizontal edges into segments

min
• Subdivision of the prediction area
into receiving points (grid)

max

min
• stored information for each visibility relation: max
• angle between the elements
• distance between centres
• example: visibility between a tile and a
receiver pixel Tile Prediction Pixel
• projection of connecting straight lines Segment Center of Tile
into xy-plane and perpendicular plane Center of horiz. Segm.
Center of vert. Segm.
• 4 angles for each visibility relation


Prediction with Pre-processed Data
• Determination of all tiles, segments and receiving points, which are visible
from the transmitter
PREDICTION
• Computation of the angles
of incidence belonging to Direct ray
these visibility relations
1.interaction
PREPRO-
• Recursively processing of
CESSING
all visible elements incl.
consideration of the
2.interaction
angular conditions
• Tree structure is very
fast and efficient
3.interaction

transmitter receiving point tile / segment



Problem of Database Accuracy in Ray Tracing models

T

T

Ray Tracing
Building error


Propagation Models: Urban Dominant Path (UDP)
Typical Channel Impulse Response
 Dominant Path (single path)
 Determination of path with full 3D
One path
approach dominates

 Unlimited number of interactions
(changes of orientation)
 Parameters of path determined (e.g
length, number of interactions,
angles,….) and used to compute path loss
with semi-deterministic equations
Full 3D approach
 Optional consideration of wave guiding
possible (wave guiding factor, based on
reflection loss of walls)
 Short prediction time
 High accuracy


Propagation Models: Dominant Path Model
Determination of Paths
 Analysis of types of wedges in scenario
 Generation of tree with convex wedges
 Searching best path
 Computation of path loss
T

6 1
Layer 1 2 4 5

Layer 2 4 5 2 R 5 2 4
5 T 2
3
4 Layer 3 R 5 4 5 2 4 R 2

R Layer 4 R R

concave wedges convex wedges
1 3 6 2 4 5


Computation of Path Loss

 Path length l
 Path loss exponents before and after breakpoint p
 individual interaction losses f(φ,i) for each interaction i of all n
interactions
 Gain due to waveguiding Ω
 Gain gt of base station antenna

n
æ 4p ö
L 20 log 10 p log (l ) f ( , i) g

l÷
ø å t


= ç ÷+ + j +W+
i=0


Parameters for prediction (1/2)


Parameters for prediction (2/2)

 Acceleration for large areas
 Adaptive Resolution Management
 Path loss exponents before and after
breakpoint can be defined individually
TX
 Breakpoint distance/computation can be
adapted to the users needs
 Definition of different path loss exponents
for LOS (Line of Sight) and OLOS
(Obstructed Line of Sight)
 Interaction losses (at points where the Wave guiding factor
path changes its orientation) can be
defined
 Individual reflection loss assigned to
buildings influences wave guiding effect


Propagation Models: Preprocessing with WallMan
Single pre-processing of building database required only for IRT model

Project File Pre-processed
Pre-processing
Pre-processing Database Files
(*.pre) (Computation) (oib, ocb opb)

Database Extensions:
Original Binary
Database file *.odb Outdoor Data Binary
(*.odb)

*.ocb Outdoor COST Binary
Materials (electrical properties) can
still be modified after pre-processing. *.oib Outdoor IRT Binary
Re-assignment of materials to objects *.opb Outdoor Dom. Path Binary
is not possible after pre-processing.

Propagation Models: Comparison

COST 231 Walfisch-Ikegami Ray Tracing (3D IRT) Dominant Path (3D)

Computation time: < 1 min Computation time: 3 min Computation time: < 1 min
Preprocessing time: < 1 min Preprocessing time: 30 min Preprocessing time: < 1 min

Not very accurate High accuracy in region of Tx High accuracy everywhere
Limited accuracy far away


Propagation Models: Indoor Penetration

Constant Level Model Exponential Decrease Model Variable Decrease Model
Considers defined Considers defined Considers defined
transmission loss transmission loss transmission loss

Homogeneous indoor level Additional exponential Additional exponential
decrease towards the decrease towards the interior
Subtracting defined interior with attenuation rate with definable attenuation
transmission loss from depending on building rate (default 0.6 dB/m)
average level at outer walls depth (~ 0.1 dB/m)

Propagation Models: Prediction of LOS States
 LOS: Line of sight between Tx and Rx
 OLOS: Obstructed line of sight between Tx and Rx (only indoor)
 NLOS: No line of sight between Tx and Rx
 LOS-V: Line of sight regarding the buildings, but shadowing due to vegetation
 NLOS-V: NLOS due to buildings and additional shadowing by vegetation



Sample Large Urban Scenario incl. Topography

Prediction of Hong Kong (334 km², 1.5 megapixel, 22030 buildings, comp. time: 15 min)
(transmit power: 40 dBm, GSM 900, directional antenna at 40 m height)



Sample Urban Scenario

2D view

Prediction of Manhattan (9 km x 18 km, 15758 buildings, comp. time: 6 min)


Urban Evaluation

Evaluation with Measurement Data

Wave Propagation Models considering

 Topography and Clutter Data

 Topography and Vector Data


Urban Evaluation

Evaluation with Measurements

Investigated Scenarios:

I. Helsinki, Finland

II. Hong Kong, China

III. Monaco, Monte Carlo

IV. Munich, Germany

V. Ilmenau, Germany

VI. Amsterdam, Netherlands


Urban Evaluation

Scenario I: Helsinki, Finland

Scenario Information

Number of buildings 1651

Topo. difference none (flat terrain)

Resolution 5m

Site 1 4.0 m, 2.5 Watt, 900 MHz
3D view of database Transmitter
Site 2 41.5 m, 10 Watt, 2.1 GHz

Prediction heights 1.6 m, 2.5 m


Urban Evaluation


Predictions for transmitter location 2

Prediction with COST 231 Prediction with 3D Ray Prediction with Urban
Walfisch-Ikegami Tracing Dominant Path


Urban Evaluation


Differences for transmitter location 2

Difference of prediction Difference of prediction Difference of prediction
with COST 231 Walfisch- with 3D Ray Tracing and with Urban Dominant
Ikegami and measurements Path and measurements
measurements


Urban Evaluation


Statistical evaluations for all transmitters

Statistical Results

Empirical Model
Deterministic Model
(e.g. COST 231 Walfisch-
Site (e.g. 3D Ray Tracing or Urban Dominant Path)
Ikegami)

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

2 -9.38 9.40 2 -1.04…1.94 5.92…6.30 20…32

3 -5.84 8.35 2 -3.60…4.31 5.53…7.81 18.. 32

Avg -7.61 8.88 2 -0.83...1.64 5.73...7.06 19.. 32

A standard PC with an AMD Athlon64 2800+ processor and 1024 MB of RAM
was used to determine the computation times


Urban Evaluation

Scenario II: Hong Kong, China



Topo. difference 482 m

Resolution 10 m

Site 1 33.0 m, 28.5 dBm, 948 MHz
Transmitter
3D view of database with topography Site 2 94.0 m, 24.9 dBm, 948 MHz

Prediction height 1.5 m


Urban Evaluation



Prediction with COST 231
Walfisch-Ikegami

Prediction with Urban Dominant Path

Prediction with 3D Ray
Tracing


Urban Evaluation


Differences for transmitter location 1

Difference of prediction with COST
231 Walfisch-Ikegami and
measurements

Difference of prediction with Urban
Dominant Path and measurements

Difference of prediction with 3D
Ray Tracing and measurements


Urban Evaluation


Statistical evaluations for all transmitters

Statistical Results

Empirical Model
Deterministic Model
(e.g. COST 231 Walfisch-
Site (e.g. 3D Ray Tracing or Urban Dominant Path)
Ikegami)

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

1 -12.81 20.13 5 0.72…4.91 6.08 …7.56 10…127

2 1.34 9.02 5 -2.30…5.63 7.74… 7.79 16…80

Avg -5.74 14.58 5 -0.79...5.27 6.94 ...7.65 13...104



Urban Evaluation

Scenario III: Monaco, Monte Carlo



3D view of database Resolution 10 m

Transmitter 17.0 m, 31.0 dBm, 2.2 GHz



Urban Evaluation





Urban Evaluation


Differences for measurement route 50

measurements


Urban Evaluation


Statistical evaluations for all measurements routes

Statistical Results

Empirical Model Deterministic Model
Route (e.g. COST 231 Walfisch-Ikegami) (e.g. 3D Ray Tracing or Urban Dominant Path)

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

50 -18.71 5.74 -4.73…-2.94 3.92…4.36

52 -20.12 8.09 3 -1.94…0.08 4.97…6.17 15…141
-0.60…-0.23
58 -25.28 9.04 4.09…4.87

Avg -21.37 7.62 3 -2.30...-1.15 4.73 15...141



Urban Evaluation

Scenario IV: Munich, Germany



Resolution 10 m

Transmitter 13.0 m, 10.0 Watt, 947 MHz
3D view of database with topography


Urban Evaluation





Urban Evaluation


Differences for measurement route 0

measurements


Urban Evaluation


Statistical evaluation for all measurement routes

Statistical Results

Deterministic Model
Empirical Model
(e.g. 3D Ray Tracing or Urban Dominant
Route (e.g. COST 231 Walfisch-Ikegami)
Path)

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

0 -10.98 6.38 -5.26…2.80 7.13…7.17

1 -13.80 7.07 -2.01…1.34 6.20…6.73
5 14...20

2 -14.70 7.43 -3.15…0.31 7.94…8.04

Avg -13.16 6.96 5 -3.47...1.48 7.09...7.31 14...20



Urban Evaluation

Scenario V: Ilmenau, Germany
 Trajectory in Urban Marco Cell (COST reference scenario)
 Tx height: 26.5 m
 Tx frequency: 2.53 GHz
 Tx power: 46 dBm
 Receiver: high resolution 3D channel sounder (RUSK, Medav GmbH)
 Receiver moving with constant speed along trajectory (~ 54/123 m)
 Rx height: 1.9 m


Urban Evaluation
Rx Power:
(Route 41a-42) [dBm] Mean Std. Dev.
Measured -62.38 2.24
Simulated -62.47 2.06
Difference 0.09 0.70

Delay Spread:
(Route 41a-42) [ns] Mean Std. Dev.
Measured 195.33 17.11
Simulated 208.79 37.46

MIMO Capacity (2x2):
(Route 41a-42) [bit/s/Hz] Mean Std. Dev.
Measured 6.31 0.13
Simulated 6.48 0.21


Urban Evaluation
Rx Power:
(Route 10b-9b) [dBm] Mean Std. Dev.

Delay Spread:
(Route 10b-9b) [ns] Mean Std. Dev.
Measured 173.36 75.54

MIMO Capacity (2x2):
(Route 10b-9b) [bit/s/Hz] Mean Std. Dev.
Measured 6.14 0.19
Simulated 6.26 0.26


Urban Evaluation

Scenario VI: Amsterdam, Netherlands
 Trajectory in Urban Marco Cell
 Tx height: 29 m
 Tx frequency: 2.25 GHz
 Tx power: 43 dBm
 Receiver: high resolution 3D-Channel Sounder (TU Eindhoven)
 Receiver moving with constant speed along trajectory (~ 420 m)
 Rx height: 3.5 m

Bridge / Tunnel
(not considered in simulation)

Urban Evaluation
Rx Power:
[dBm] Mean Std. Dev.

Delay Spread:
[ns] Mean Std. Dev.
Measured 222.36 106.91
Simulated 216.07 130.23
Difference -6.29 109.63

Angular Spread (Rx):
[°] Mean Std. Dev.
Measured 52.05 21.15

Bridge / Tunnel Difference -2.25 24.99
(not considered in simulation)


Summary
Features of WinProp Urban Module
• Highly accurate propagation models
Empirical: COST 231 Walfisch-Ikegami
Deterministic (ray optical): 3D Dominant Path, 3D Ray Tracing, 2x2D Ray Tracing
Optionally calibration of 3D Dominant Path Model with measurements possible – but not
required as the model is pre-calibrated

• Building data
Models are based on 2.5D vector data of buildings
Consideration of material properties (also vegetation objects can be defined)
Consideration of topography (pixel databases)

• Antenna patterns
Either 2x2D patterns or 3D patterns

• Outputs
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 urban

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