Propagation urban

Propagation Models & Scenarios:

Urban

© 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 Vector Data (buildings and/or vegetation)

- 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

Knife Edge Diffraction

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 Vector Data

Databases: Vector Building Databases

• 3D vector oriented database

• Buildings as vertical cylinders with polygonal ground-planes

• Uniform height above street-level

• Limitation to vertical walls and flat roofs

• Individual material properties of building surfaces

• Topography can be considered optionally

Example: New York



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

Multiple 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



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


t h



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

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

h Roof

w h r

• Height of transmitter hTX

• Height of receiver hRX

b d

• Mean value of building heights hroof

• 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

LOS: lb

42,6 dB d

26 lg km

20 lg f

MHz

l0 lrts lmsd

lrts lmsd 0

NLOS: lb

l l l 0 0 rts msd

f r Free space loss l

0 : l

0 32,44 dB 20 lg

w

20 lg MHz km

f

hRoof hr

Rooftop loss lrts : lrts 16,9 dB 10 lg 10 lg m

d

20 lg MHz m

f b Over rooftop loss lmsd : lmsd lbsh k a k d lg k

km f

lg 9 lg MHz m

18 lg1

ht hRoof

with lbsh

m 0

ht hRoof

ht hRoof

Factors k a and k d

Empir. Correction of antenna heights

Faktor k f Adaption to different building densities

Valid for: f MHz ................... 800 - 2000

ht m ................................. 4 - 50

hr m ................................. 1 - 3

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

Reflection

Single

Diffraction




Triple

Reflection

Double

Diffraction

Single

Reflection +

Single

Diffraction

Double

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

• Subdivision of the prediction area into receiving points (grid)

• stored information for each visibility relation:

• angle between the elements

• distance between centres

• example: visibility between a tile and a receiver pixel

Tile

max

min

min

max

Prediction Pixel

• projection of connecting straight lines into xy-plane and perpendicular plane

• 4 angles for each visibility relation

Segment Center of Tile

Center of horiz. Segm.

Center of vert. Segm.




Prediction with Pre-processed Data

• Determination of all tiles, segments and receiving points, which are visible from the transmitter

• Computation of the angles of incidence belonging to these visibility relations

• Recursively processing of all visible elements incl. consideration of the angular conditions

• Tree structure is very fast and efficient

Direct ray

1.interaction

2.interaction

3.interaction

PREDICTION

PREPRO- CESSING

transmitter receiving point tile / segment



Problem of Database Accuracy in Ray Tracing models

Ray Tracing

T

T

Building error



Propagation Models: Urban Dominant Path (UDP)

Dominant Path (single path)

Determination of path with full 3D approach

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

Optional consideration of wave guiding possible (wave guiding factor, based on reflection loss of walls)

Short prediction time

High accuracy

Typical Channel Impulse Response

One path dominates

Full 3D approach


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 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

æ 4pö

L 20 log

10 p log (l ) n

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

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

path changes its orientation) can be

defined

Individual reflection loss assigned to

buildings influences wave guiding effect

TX

Wave guiding factor



Propagation Models: Preprocessing with WallMan

Single pre-processing of building database required only for IRT model

Project File

Pre-processing

(*.pre)

Pre-processing

(Computation)

Pre-processed

Database Files

(oib, ocb opb)

Original Binary

Database file

(*.odb)

Materials (electrical properties) can still be modified after pre-processing.

Re-assignment of materials to objects is not possible after pre-processing.

Database Extensions:

*.odb Outdoor Data Binary

*.ocb Outdoor COST Binary

*.oib Outdoor IRT Binary

*.opb Outdoor Dom. Path Binary



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

Limited accuracy far away

High accuracy everywhere



Propagation Models: Indoor Penetration

Constant Level Model Exponential Decrease Model Variable Decrease Model

Considers defined

transmission loss

Homogeneous indoor level

Subtracting defined

transmission loss from

average level at outer walls

Considers defined

transmission loss

Additional exponential

decrease towards the

interior with attenuation rate

depending on building

depth (~ 0.1 dB/m)

Considers defined

transmission loss

Additional exponential

decrease towards the interior

with definable attenuation

rate (default 0.6 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



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 Information

Number of buildings 1651

Topo. difference none (flat terrain)

Resolution 5 m

Transmitter Site 1 4.0 m, 2.5 Watt, 900 MHz

Site 2 41.5 m, 10 Watt, 2.1 GHz

Prediction heights 1.6 m, 2.5 m

Scenario I: Helsinki, Finland

3D view of database


Urban Evaluation


Predictions for transmitter location 2

Prediction with COST 231 Walfisch-Ikegami

Prediction with 3D Ray Tracing

Prediction with Urban Dominant Path


Urban Evaluation


Differences for transmitter location 2

Difference of prediction with COST 231 Walfisch-

Ikegami and measurements

Difference of prediction with 3D Ray Tracing and

measurements

Difference of prediction with Urban Dominant

Path and measurements


Urban Evaluation


Statistical evaluations for all transmitters

Site

Statistical Results

Empirical Model (e.g. COST 231 Walfisch-

Ikegami)

Deterministic Model (e.g. 3D Ray Tracing or Urban Dominant Path)

Mean Value [dB]

Std. Dev. [dB]

Comp. Time [s]

Mean Value [dB]

Std. Dev. [dB]

Comp. Time [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



Topo. difference 482 m

Resolution 10 m

Transmitter Site 1 33.0 m, 28.5 dBm, 948 MHz

Site 2 94.0 m, 24.9 dBm, 948 MHz

Prediction height 1.5 m

Scenario II: Hong Kong, China

3D view of database with topography


Urban Evaluation







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

Site

Statistical Results

Empirical Model (e.g. COST 231 Walfisch-

Ikegami)


Mean Value [dB]

Std. Dev. [dB]

Comp. Time [s]

Mean Value [dB]

Std. Dev. [dB]

Comp. Time [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




Resolution 10 m

Transmitter 17.0 m, 31.0 dBm, 2.2 GHz


Scenario III: Monaco, Monte Carlo

3D view of database


Urban Evaluation







Urban Evaluation


Differences for measurement route 50




measurements




Urban Evaluation


Statistical evaluations for all measurements routes

Route

Statistical Results

Empirical Model (e.g. COST 231 Walfisch-Ikegami)


Mean Value [dB]

Std. Dev. [dB]

Comp. Time

[s]

Mean Value [dB]

Std. Dev. [dB]

Comp. Time

[s]

50

-18.71

5.74

3

-4.73…-2.94

3.92…4.36

15…141

52

-20.12

8.09

-1.94…0.08

4.97…6.17

58

-25.28

9.04 -0.60…-0.23

4.09…4.87

Avg

-21.37

7.62

3

-2.30...-1.15

4.73

15...141



Urban Evaluation




Resolution 10 m

Transmitter 13.0 m, 10.0 Watt, 947 MHz


Scenario IV: Munich, Germany

3D view of database with topography


Urban Evaluation







Urban Evaluation


Differences for measurement route 0




measurements




Urban Evaluation


Statistical evaluation for all measurement routes

Route

Statistical Results

Empirical Model (e.g. COST 231 Walfisch-Ikegami)

Deterministic Model (e.g. 3D Ray Tracing or Urban Dominant

Path)

Mean Value

[dB]

Std. Dev. [dB]

Comp. Time

[s]

Mean Value

[dB]

Std. Dev. [dB]

Comp. Time

[s]

0 -10.98 6.38

5

-5.26…2.80 7.13…7.17

14...20

1

-13.80

7.07

-2.01…1.34

6.20…6.73

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

[dBm] Mean Std. Dev.

Measured -62.38 2.24

Simulated -62.47 2.06

Difference 0.09 0.70

[ns] Mean Std. Dev.

Measured 195.33 17.11

Simulated 208.79 37.46


[bit/s/Hz] Mean Std. Dev.

Measured 6.31 0.13

Simulated 6.48 0.21


Rx Power: (Route 41a-42)

Delay Spread: (Route 41a-42)

MIMO Capacity (2x2): (Route 41a-42)


Urban Evaluation





[ns] Mean Std. Dev.

Measured 173.36 75.54



[bit/s/Hz] Mean Std. Dev.

Measured 6.14 0.19

Simulated 6.26 0.26


Rx Power: (Route 10b-9b)

Delay Spread: (Route 10b-9b)

MIMO Capacity (2x2): (Route 10b-9b)

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

2012

Bridge / Tunnel (not considered in simulation)

© by AWE Communications GmbH 57

Urban Evaluation


[°] Mean Std. Dev.

Measured 52.05 21.15


Difference -2.25 24.99

Rx Power:





Delay Spread:

[ns] Mean Std. Dev.

Measured 222.36 106.91

Simulated 216.07 130.23

Difference -6.29 109.63

Angular Spread (Rx):

Bridge / Tunnel (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

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

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