Propagation Models & Scenarios Guide

Propagation Models & Scenarios:

Rural /
Suburban

© 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 Clutter Data
- 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 Clutter Data

Databases: Topographical Databases
Topographical database (DEM, Digital Elevation Model)
• Arbitrary resolution
Recommended: 20 - 30 m
• Elevation in meter
(converters available for feet,…)
• Interpolation of undefined
pixels possible
• Geodetic or UTM coordinates
• More than 200 coordinate
datum supported
• Display of additional
vector data layers
(e.g. streets, districts,….)

Example: Detroit, USA
• Index or single database files
(incl. multiple resolutions)
• Curvature of earth surface
considered (optionally)



Databases: Clutter (morpho, land usage) Databases
Clutter database (land usage)
• Individual class assigned to
each pixel
• Class ID with individual
properties
• Frequency dependent
attenuation
• Clutter heights
• Clutter clearance
• Electrical properties of ground
• Selection of prediction
submodels (Hata-Submodels)
• Class either defined by local
receiver coordinates or
Example: Detroit, USA
weighted along the path from
transmitter to receiver


Clutter database (land usage)

Example: Germany
• 12 classes
• 50m resolution



Properties defined for each clutter class • Name (and ID)
• Weight
(if dominant class along path
between Tx and Rx is determined)
• Color (on display)
• Height of objects in class
(for LOS and diffraction loss)
• Clearance of objects in class
(for LOS and diffraction loss)
• Selection of Hata submodel
• Dense Urban
• Medium Urban
• Suburban
• Open Area
• Definition of individual
electrical properties (losses,
ground properties) for
multiple frequency bands


Properties defined for each clutter class

• Fixed clutter heights and • Statistically distributed clutter
clearance radii heights and clearance radii


Properties defined for each frequency band
• Frequency band margins
• Additional loss (in dB) for all models except
Hata-Okumura
• Additional loss (in dB) for Hata-Okumura
depending on Hata sub model:
• Dense Urban
• Medium Urban
• Suburban
• Open Area
• Electrical properties of ground for selected
models (to determine reflection loss):
• Deterministic Two Ray
• 3D Scattering

Frequency band properties


Propagation Models
• Hata-Okumura
• 4 submodels (open/suburban/medium urban/dense urban)
• Akeyama Extension
• COST 207 for frequencies in the 2 GHz band
Hata-Okumura
• Two Ray Model
• Direct ray and ground reflected ray
• Either deterministic (with check of visibility and check
of reflection) or empirical (assuming always LOS)
• Knife Edge Diffraction
• Consideration of topography in vertical plane between
Tx and Rx (additionally to Hata or Two Ray Model) Knife Edge Diffraction
• ITU P.1546
• Interpolation from empirical field strength curves
• Dominant Path Model
• Full 3D path searching algorithm
• 2D/3D Ray Tracing Model
• Ray tracing algorithm in 3D or in vertical plane Dominant Path


Propagation Models: Hata-Okumura
• Four submodels
• open • medium urban
• suburban • dense urban

• Two different sub-model modes
• homogenous - same sub-model for whole area
• individual – model selection depending on clutter class at mobile station

• Akeyama Extension (close to Tx)
• COST 207 Extension (frequencies in 2 GHz band)

• Topography between Tx and Rx not considered
(e.g. shadowing due to hills, etc.)

• Frequency band between 150 and 2000 MHz



Propagation Models: Two-Ray & Knife-Edge Diffraction
• Computation of direct and ground reflected ray
• Additional diffraction loss in shadowed areas (frequency dependent)
• Topography between Tx and Rx considered
(e.g. shadowing due to hills, etc.)

• Possible evaluation of Fresnel zone


• Superposition of clutter heights to terrain profile
• Propagation model considers topography and clutter heights
Topo Profile

individual height for each clutter class
Clutter Profile

Forest Open Buildings Skyscr. Buildings Street Forest Open Forest Buildings
Superposition


• Variation of obstacles even in same clutter class
• Heights of each class can be statistically distributed (individual parameters)
Topo Profile

Individual statistical distribution of height for each clutter class
Clutter Profile

Forest Open Buildings Skyscr. Buildings Street Forest Open Forest Buildings
Superposition


• Clearance impacts the propagation (for each class defined individually)
BS MS 1 MS 2
Without Clearance

BS MS 1 MS 2
With Clearance

Clearance Buildings: 2 Grid 2 Grid 0.5 Grid
Clearance Forest: 0.5 Grid

Examples

Prediction in Baden-Württemberg (10000 km²) with Two-Ray plus Knife-Edge Diffraction model
pt0=57 dBm, f=2200 MHz, ht=67 m, omni antenna



Propagation Models: ITU P.1546
• For terrestrial radio circuits over land paths, sea paths
and/or mixed land-sea paths
• interpolation/extrapolation from empirically derived field strength curves as
functions of distance, antenna height, frequency and percentage of time
• includes corrections of the results obtained from interpolation/extrapolation to
account for terrain clearance and terminal clutter obstructions


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 field strength/path loss
 Path length d
 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 wk
at c pixels along the path

 Gain gt of base station antenna
 Power pt of transmitter

n
dBμV æd
e 104.77 10 p log f ( , i) g p


÷ å
ø t t

= - ⋅ ç ÷- j + +

⋅
m m i=0


Examples

181 km

Prediction of the Grand Canyon (16900 km², 2.6 Megapixel) with Rural Dominant Path Model
pt0=40 dBm, f=948 MHz, ht=25 m, omni antenna


Examples

Prediction of an area in Switzerland (63 km², 632000 Pixel) with Rural Dominant Path Model
pt0=10 Watt, f=948 MHz, ht=25 m, omni antenna


Examples

Prediction of a high mountain
(‘Matterhorn’) in Switzerland with
Rural Dominant Path Model
pt0=10 Watt, f=948 MHz,
ht=25 m, omni antenna


Propagation Models: Ray Tracing
Usage of Digital Surface Models
• includes buildings, vegetation, and roads, as well as
natural terrain features
• Conversion of topography from pixel to vector format

• Consideration of land usage in vector format

Additional obstacles
in vector format




• Multipath propagation considered
• Dominant effects: diffraction, reflection
and shadowing
• Ray with multiple reflections and
diffractions are determined (incl.
different combinations)
• Angle tolerance for reflections to
emulate scattering
• Electrical properties of ground can be
defined for each clutter class individually
• Either full 3D or 2D in vertical plane
• Uncorrelated or correlated superposition
of contributions (rays)
• Optional post-processing with Knife
Edge Diffraction model possible


Determination of Paths

• The Ray Tracing computes all rays for each
receiver point individually and guarantees the
consideration of each ray as well as a constant
resolution.
• For the computation of the rays, not only the
free space loss has to be considered but also
the loss due to the reflections and (multiple)
diffraction. This is either done using a physical
deterministic model or using an empirical
model.


Examples


Results

Channel Impulse Response Angular Profile


Results

Spatial Chanel Impulse Response (3D)


Rural Evaluation

Evaluation with Measurements

I. Area around Grab/Murrhardt, Germany

II. Area around Ludwigsburg, Germany

III. Hjorring, Denmark

IV. Jerslev, Denmark

V. Ravnstrup, Denmark


Rural Evaluation

Scenario I: Area around Grab/Murrhardt, Germany

Scenario Information

Topo. difference 394 m

Resolution 50.0 m

91.0 m, 43.8 dBm,
Transmitter
1259.05 MHz

Prediction height 1.5 m 3D view of the database (z-axis scaled with factor 5)


Rural Evaluation


Prediction with Hata-Okumura Model Prediction with Rural Dominant Path
with Knife-Edge-Diffraction Extension Model


Rural Evaluation


Difference of prediction with Hata- Difference of prediction with Rural
Okumura Model with Knife-Edge- Dominant Path Model and
Diffraction Extension and measurement measurement (cut-out)
(cut-out)

Rural Evaluation


Statistical Results

Hata-Okumura Model with
Knife-Edge-Diffraction Rural Dominant Path
Scenario Extension
Mean Comp. Mean Comp.
Std. Dev. Std. Dev.
Value Time Value Time
[dB] [dB]
[dB] [s] [dB] [s]
Grab/Murrhardt 18.01 9.26 3 5.79 8.95 62

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


Rural Evaluation

Scenario II: Area around Ludwigsburg, Germany


Topo. difference 205 m
3D view of the database (z-
Resolution 50.0 m axis scaled with factor 5)
41.0 m, 49.0 dBm,
Transmitter
438.92 MHz

Prediction height 1.5 m


Rural Evaluation


Prediction with Hata-Okumura Model Prediction with Rural Dominant Path
with Knife-Edge-Diffraction Extension Model


Rural Evaluation


Okumura Model with Knife-Edge- Dominant Path Model and
Diffraction Extension and measurement (cut-out)
measurement (cut-out)


Rural Evaluation


Statistical Results

Scenario Extension
Std. Dev. Std. Dev.
[dB] [dB]
[dB] [s] [dB] [s]
Ludwigsburg -1.54 8.31 3 -9.76 7.39 9



Rural Evaluation

Scenario III: Hjorring, Denmark


Topo. difference 28.0 m

Resolution 50.0 m

12.0 m, 40 dBm,
Transmitter
970 MHz


Terrainprofile
of database

3D view of terrainprofile
of database (z-axis
stretched with facor 10)


Rural Evaluation


Prediction with Hata-Okumura Model with Prediction with Rural Dominant Path Model
Knife-Edge-Diffraction Extension


Rural Evaluation


Difference of prediction with Hata- Difference of prediction with Rural Dominant
Okumura Model with Knife-Edge- Path Model and measurement
Diffraction Extension and measurement


Rural Evaluation


Statistical Results

Scenario Extension
Std. Dev. Std. Dev.
[dB] [dB]
[dB] [s] [dB] [s]
Hjorring 0.13 10.04 <1 2.05 7.85 <1



Rural Evaluation

Scenario IV: Jerslev, Denmark



Resolution 50.0 m

12.0 m, 40 dBm,
Transmitter
970 MHz


Terrainprofile
of database

3D view of terrain profile
of database (z-axis
stretched with factor 10)


Rural Evaluation




Rural Evaluation


Difference of prediction with Hata- Difference of prediction with Rural Dominant
Okumura Model with Knife-Edge- Path Model and measurement


Rural Evaluation


Statistical Results

Scenario Extension
Std. Dev. Std. Dev.
[dB] [dB]
[dB] [s] [dB] [s]
Jerslev -5.36 6.42 <1 -0.68 6.77 <1



Rural Evaluation

Scenario V: Ravnstrup, Denmark



Resolution 50.0 m

12.0 m, 40 dBm,
Transmitter
970 MHz


Terrainprofile
of database

3D view of terrain profile
of database (z-axis
stretched with factor 10)


Rural Evaluation




Rural Evaluation


Okumura Model with Knife-Edge- Dominant Path Model and measurement


Rural Evaluation


Statistical Results

Scenario Extension
Std. Dev. Std. Dev.
[dB] [dB]
[dB] [s] [dB] [s]
Ravnstrup 0.48 6.60 <1 1.86 6.56 <1



Summary
Features of WinProp Rural Module
• Highly accurate propagation models for various scenarios
Empirical: Hata-Okumura, ITU P.1546, …
Semi-Empirical: Two-Ray plus Knife-Edge diffraction, …
Deterministic (ray optical): 3D Dominant Path, 3D Ray Tracing, 2x2D Ray Tracing
Optionally calibration of models with measurements possible – but not required as the
models are pre-calibrated

• Topography and Clutter or Vector Data
Obstacles described by clutter or vector data
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)
Channel impulse response, angular profile (direction of arrival)


Further Information

Further information: www.awe-com.com

Propagation Models & Scenarios Guide

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