The IC 1004 Urban Hannover Scenario 3D Pathloss ... · 5. Conclusion and Outlook . September 27,...

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The IC 1004 Urban Hannover Scenario – 3D Pathloss

Predictions and Realistic Traffic and Mobility Patterns

Dennis M. Rose September 27, 2013 COST IC1004 (Ghent, Belgium)

September 27, 2013 | Dennis M. Rose

The IC 1004 Urban Hannover Scenario | 2/24

Introduction and Motivation

Evolution of radio and core networks

Transition from coverage to capacity

More spectrum is available

Deployment of smaller cells or highly tilted

antennas

Changed usage of mobile terminals

Dominating data traffic

Approx. 80% of the mobile traffic is

delivered to indoor environments

Adapt to the situation

More complex network planning and

network architecture

Stronger variations in data traffic than in

legacy voice traffic

State of the art techniques to design and

simulate such networks realistically

Accurate predictions for site planning

Ray-optical models

Modelling of individual user behaviour

Realistic mobility pattern

Realistic traffic model

Problems

Huge overhead for researchers

No comparability of simulation results

Solution common simulation scenario

Flexible use for different tasks

Realistic network layout

Realistic model for mobility and data traffic



Outline

1. Introduction and Motivation

2. Environmental Data and Simulation Models

Geo Data

User Mobility Models

Propagation Models

Data Traffic Model

3. LTE Network in the “Hannover Scenario”

4. Scenario Data and File Format

Trace Files

Network File

Land-Use Map

Best-Server Map

5. Conclusion and Outlook



Easting [km]

No

rth

ing

[k

m]

0 4 8 12 16 200

4

8

12

16

20

24

Sealed surfaces

Roads

Railway track

Small building

Building

Commercial

Industrial

Wasteland

Forest

Green space

Water

Geo Data

2.5D Building

Information

Pathloss predictions

For Pedestrian Mobility

and Indoor User

Placements

Geographic Information

from OpenStreetMap

Road Data for

Vehicular and

Pedestrian User

Mobility

Land-Use Data

500 1000 1500 2000

500

1000

1500

2000

0

5

10

15

20

25




Static Indoor Users

Uniformly placed on indoor pixels

Vehicular Users (Simulation of Urban

MObility, SUMO)

Multi-lane streets

Lane changes

Traffic lights

Right of way rules

Braking and Acceleration

Pedestrian Users

Ways in close proximity to buildings

Shortcuts over greenspaces

Realistic crossing of roads

Movements start and end in buildings



Propagation Models

Ray Optical Outdoor Predictions

Considered types of rays

Direct path

Specular reflections up to the 2nd order

Diffuse scattering (Lambertian emitter)

Diffraction (Deygout)

Outdoor-to-Indoor Predictions

“Ground Outdoor-to-Indoor”

Indoor signal levels are derived from

signal levels at references points at the

building boundary

The reference points for an indoor pixel

are the foots of the dropped

perpendiculars onto the building

boundary

Geometrical indoor prediction for ARFCN=985, h In

=7.50 m , h Out

=1.00 m

-90

-80

-70

-60

-50

-40



Data Traffic Model – Object Sizes

Model of web traffic on application layer

Traffic object is a website, e-Mail, file download, …

Web traffic object sizes

Small objects: Signaling-like applications (WhatsApp, push e-Mail, notifications, …)

Medium sized objects: Interactive applications (Web sites, e-Mail transfers, …)

Large objects: File transfers (Downloads, videos, …)

Modelled object size distribution

Two separate distributions for uplink and downlink

(uplink about ½ the size of downlink)

Object sizes from 100 Byte to 10 GByte

Many small objects

(70% of the objects smaller than 10kByte)

Large objects contribute largest share

of the volume (60% of the volume

from objects larger than 1MB)



Data Traffic Model – Object Arrival Times

Large time scale

Trace files based on diurnal load curves

from literature

Users transfer about 8000 objects per day

Medium time scale

Session behaviour currently not modeled

(no correlation of object arrivals)

Small time scale

If required, TCP rate control has to be

implemented in the simulator



Data Traffic Model – TCP behaviour

TCP model required for

Evaluations including the buffer fill levels at UE or BS

Cell occupation analysis with high granularity

Approaches which change the delay of the mobile network

Evaluations of the user experience



Outline



Geo Data


Propagation Models

Data Traffic Model



Trace Files

Network File

Land-Use Map

Best-Server Map




LTE Network in the “Hannover Scenario”

Easting [km]

Nort

hin

g [

km

]

0 4 8 12 16 200

4

8

12

16

20

24

Easting [km]

Nort

hin

g [

km

]9 10 11

11

12

13

14

15

16



LTE Network in the “Hannover Scenario” – Prediction

Scenario LTE 1800

Planned

area [m]

E: 00000 … 20000 (20 km)

N: 00000 … 24000 (24 km)

480 km²

Scenario

area [m]

E: 08500 ... 11500 (3 km)

N: 11000 ... 16000 (5 km)

15 km²

RATs /

Layers

LTE (1 layer)

Macro cells LTE 1800: 195

TX power: 46 dBm

EIRP: 63.15 dBm

Micro cells None

Smaller cells None 6 8 10 12 14

10

12

14

16

18

Easting [km]

Nort

hin

g [

km

]

[dBm]-110

-100

-90

-80

-70

-60

-50

-40

-30

-20



Outline



Geo Data


Propagation Models

Data Traffic Model



Trace Files

Network File

Land-Use Map

Best-Server Map




File Format – CSV

Human readable CSV-Files

Lines with a fixed number of columns

(No header line)

Position data Time; User; X; Y; Z

0.23; 0; 256.13; 49.73; 1.5

0.23; 1; 24.47; 319.62; 1.5

0.23; 2; 140.57; 161.00; 1.5

Radio propagation data Time; User; Cell; RSRP; Cell; RSRP; …

0.23; 0; 13; -60.74; 12; -66.81; …

0.23; 1; 42; -63.02; 16; -69.67; …

0.23; 2; 74; -67.56; 17; -71.10; …



File Format – Simple Parsing (Java)

Simple Java Example

public static void main(String[] args) throws IOException {

final BufferedReader in =

new BufferedReader(new InputStreamReader(System.in));

String line = in.readLine();

while (line != null) {

final String[] fields = line.split(";");

// DO something

line = in.readLine();

}

in.close();

}



File Format – Simple Parsing (MATLAB)

Simple MATLAB Example

fChannel = fopen('Mobile_User_Trace_Channel.csv', 'r');

fChannel_filter = ['%f' repmat(';%f', 1, 41)];

while (ischar(tline))

fChannel_line = fgetl(fChannel);

fChannel_tokens = sscanf(fChannel_line, fChannel_filter);

% DO something

end

fclose(fChannel);



Trace Files

Mobility Type User ID Range

Vehicular Users 0 … 4619

Pedestrian Users 10000 … 15246

Static Indoor Users 20000 … 29999

Easting [km]

Nort

hin

g [

km

]

6 8 10 12 14

10

12

14

16

18



Network File

Description of Content Unit

Cell ID #

x-Coordinate m

y-Coordinate m

z-Coordinate (Height) m

Azimuth degree

Tilt degree

Transmit Power dBm

Antenna Type -

EIRP dBm

Bandwidth MHz

Centre Frequency MHz 6 8 10 12 14

10

12

14

16

18

Easting [km]

Nort

hin

g [

km

]

[dBm]-110

-100

-90

-80

-70

-60

-50

-40

-30

-20



Easting [km]

Nort

hin

g [

km

]

0 4 8 12 16 200

4

8

12

16

20

24

Sealed surfaces

Roads

Railway track

Small building

Building

Commercial

Industrial

Wasteland

Forest

Green space

Water

Land-Use Map / Best Server Map

6 8 10 12 14

10

12

14

16

18

Easting [km]

Nort

hin

g [

km

]

[#]0

20

40

60

80

100

120

140

160

180



Outline



Geo Data


Propagation Models

Data Traffic Model



Trace Files

Network File

Land-Use Map

Best-Server Map




Conclusion

First version of the IC 1004 Urban Hannover Scenario is ready!

http://www.ifn.ing.tu-bs.de/sim-scenario

State of the art techniques to design and simulate networks realistically

Accurate predictions for site planning

Ray-optical models

Modelling of individual user behaviour

Realistic mobility pattern

Realistic traffic model

Serves as common simulation scenario

Flexible use for different tasks

Realistic network layout

Realistic model for mobility and data traffic








Outlook

First of all, please provide us with your feedback

Mobility model improvements

Provisioning of indoor mobility traces

Only SUMO users are yet interacting with each

other

HetNets scenario extensions

Dense pico/femto cell deployments

Analyse the performance of such networks in a

more realistic way

Base scenario in the FP7 STREP Project

„SEMAFOUR“

Self-Management for Unified Heterogeneous Radio

Access Networks

September 2012 - August 2015



Thank you for

your attention!

Dennis M. Rose

[email protected]

The IC 1004 Urban Hannover Scenario 3D Pathloss ... · 5. Conclusion and Outlook . September 27,...

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