propagation rural

Propagation Models & Scenarios:

Rural /

Suburban

© 2012 by AWE Communications GmbH

www.awe-com.com

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

Contents

2012 © by AWE Communications GmbH 2

• 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


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

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


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


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)

Example: Detroit, USA

• 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,….)

• Index or single database files (incl. multiple resolutions)

• Curvature of earth surface considered (optionally)



Databases: Clutter (morpho, land usage) Databases

Clutter database (land usage)

Example: Detroit, USA

• 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 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 clearance radii

• Statistically distributed clutter 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

• 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)

• 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

Hata-Okumura


Dominant Path



Propagation Models: Hata-Okumura

• Four submodels

• open

• suburban

• medium urban

• 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


Superp

osi

tion

Clu

tter

Pro

file

To

po P

rofile



• Superposition of clutter heights to terrain profile

• Propagation model considers topography and clutter heights

individual height for each clutter class

Forest Open Buildings Skyscr. Buildings Street Forest Open Forest Buildings


Superp

osi

tion

Clu

tter

Pro

file

Topo P

rofile



• Variation of obstacles even in same clutter class

• Heights of each class can be statistically distributed (individual parameters)

Individual statistical distribution of height for each clutter class

Forest Open Buildings Skyscr. Buildings Street Forest Open Forest Buildings


With C

leara

nce

W

ithout

Cle

ara

nce


Propagation Models: Two-Ray & Knife-Edge Diffraction • Clearance impacts the propagation (for each class defined individually)

BS MS 1 MS 2

BS MS 1 MS 2

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


6 1


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

Layer 1

2 4 5

5 T 2

3

4

R

Layer 2

Layer 3 Layer 4

4 5 R 5 4

R

2 R 5 5 2

2 4 4 R 2 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

e 104.77 dBμV

10 p æ d

log

n

f ( , i) g p


÷ø å 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

http://www.awe-communications.com/Propagation/Rural/RayOptical/images/topo_vector_large.jpg

http://www.awe-communications.com/Propagation/Rural/RayOptical/images/topo_vector_large.jpg




• 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 Information

Topo. difference

394 m

Resolution

50.0 m

Transmitter 91.0 m, 43.8 dBm,

1259.05 MHz

Prediction height

1.5 m

Scenario I: Area around Grab/Murrhardt, Germany

3D view of the database (z-axis scaled with factor 5)


Rural Evaluation


Prediction with Hata-Okumura Model with Knife-Edge-Diffraction Extension

Prediction with Rural Dominant Path Model


Rural Evaluation


Difference of prediction with Hata- Okumura Model with Knife-Edge-

Diffraction Extension and measurement (cut-out)

Difference of prediction with Rural Dominant Path Model and measurement (cut-out)


Rural Evaluation


Scenario

Statistical Results

Hata-Okumura Model with Knife-Edge-Diffraction

Extension

Rural Dominant Path

Mean Value [dB]

Std. Dev. [dB]

Comp. Time [s]

Mean Value [dB]

Std. Dev. [dB]

Comp. Time [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


Topo. difference 205 m

Resolution

50.0 m

Transmitter 41.0 m, 49.0 dBm,

438.92 MHz

Prediction height

1.5 m

Scenario II: Area around Ludwigsburg, Germany

3D view of the database (z- axis scaled with factor 5)


Rural Evaluation





Rural Evaluation



Diffraction Extension and measurement (cut-out)

Difference of prediction with Rural Dominant Path Model and measurement (cut-out)


Rural Evaluation


Scenario

Statistical Results


Extension

Rural Dominant Path

Mean Value [dB]

Std. Dev. [dB]

Comp. Time [s]

Mean Value [dB]

Std. Dev. [dB]

Comp. Time [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

Transmitter 12.0 m, 40 dBm,

970 MHz

Prediction height

3.0 m

Terrainprofile of database

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


Rural Evaluation





Rural Evaluation



Diffraction Extension and measurement

Difference of prediction with Rural Dominant Path Model and measurement


Rural Evaluation


Scenario

Statistical Results


Extension

Rural Dominant Path

Mean Value [dB]

Std. Dev. [dB]

Comp. Time [s]

Mean Value [dB]

Std. Dev. [dB]

Comp. Time [s]

Hjorring

0.13

10.04

< 1

2.05

7.85

< 1



Rural Evaluation

Scenario IV: Jerslev, Denmark


Topo. difference

20.9 m

Resolution

50.0 m


970 MHz

Prediction height

3.0 m


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


Rural Evaluation





Rural Evaluation


Scenario

Statistical Results


Extension

Rural Dominant Path

Mean Value [dB]

Std. Dev. [dB]

Comp. Time [s]

Mean Value [dB]

Std. Dev. [dB]

Comp. Time [s]

Jerslev

-5.36

6.42

< 1

-0.68

6.77

< 1



Rural Evaluation

Scenario V: Ravnstrup, Denmark


Topo. difference

45.4 m

Resolution

50.0 m


970 MHz

Prediction height

3.0 m


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


Rural Evaluation





Rural Evaluation


Scenario

Statistical Results


Extension

Rural Dominant Path

Mean Value [dB]

Std. Dev. [dB]

Comp. Time [s]

Mean Value [dB]

Std. Dev. [dB]

Comp. Time [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

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

propagation rural

Technology

clutter data databases

clutter urban

clutter morpho

propagation models scenarios

vector databases buildings

vector databases walls

propagation model projects

cluttertopographical