WaveSight TM - see your net work! - Wavecallwavecall.com/downloads/WSTechnicalPresentationV8.pdf · · 2007-04-25and published [1] in 1968. Then the formula was given by Hata [2]

WaveSight WaveSight TM TM -- see your see your net work!net work!

Technical PresentationTechnical Presentation

Slide 2Prof. Dr. Volker Amelung / Dr. Karim Rizk Wavecall SA

Table of Contents I

I. Network Planning Concerns1. Calculating coverage and reducing interference

2. Rising numbers of subscribers in GSM networks

3. Additional Needs for GPRS

4. Set-up of UMTS networks

II. Propagation History1. Empirical models

2. Semi-empirical models

3. Deterministic models (Ray-Tracing)

III. Deterministic models: WaveSight1. Physical basics of WaveSight

2. Implemented Parameters and algorithms

3. Data Input

4. Simulation

5. Prediction Outcome (Accuracy and Speed)


Table of Contents II

IV. The added value of WaveSight for network planning

1. Reduced need for measurement and drive tests

2. Better knowledge of Coverage

3. Better Input / better results for frequency planning

4. Rising network quality / better opportunities for fine/tuning

5. Increased speed of network set-up

6. Reduction in the number of needed sites

V. Integration of WaveSight into Planning Tools1. Integrated Planning Tools2. Integration on Windows Systems3. Integration on Unix Systems4. License Protection


I. Network Planning Concerns

1. Calculating coverage and reducing interference




1. Calculating coverage

• In the early 90’s, the initial concern in term of planning was to ensure a coverage for all areas as fast as possible.

• After rolling-out their network, the operators had to face one problem : in Urban areas you can easily have shadow areas because of the characteristics of buildings.

• Finally these last years, the most important challenge became to face the important increase of subscribers, which caused problems of saturation in congested areas.



• The number of subscribers has continuously raised with an explosion in the year 2000, seeing more than 210,000,000 subscribers in Western Europe and an increase of more than 30%.

• Now as the amount of users has grown, it came close to the point that network capacities are saturated in some congested areas.

• Simultaneously, users have rising quality expectations and don´t accept dropped calls and bad connections.

• As the bandwidth allocated to operators is not infinite, the only way to raise network capacity is to add new microcells to increase the maximal amount of users in such areas.

• The important problem that is caused by adding microcells is that the frequency planning became more complex (a good one allows good frequency re-use)


3. Additional Needs for GPRS

• On GPRS (General Packet Radio Service) users use remaining free slots of Base Station for data transmission.

• The number of available slots for GRPS is scalable. It depends on the circuit load of the cell (number of voice channels)

The loading of GSM networks will raise seriously !! And so interference will raise as well.

• The received data throughput is directly related to the ratio receivedsignal/interference.

Bad coverage will result in poor data transmission and unacceptable Quality of Service

Badly planned GSM networks will have trouble with GPRS and may also experienceproblems with voice users.



• Planning of UMTS is a new challenge, as the technology is completely new, the problems in planning will be different

• Infrastructure costs will be a significant burden for all operators

• No frequency planning (frequency reuse = 1)

• Soft handover will be a feature

• There is a great uncertainty about the availability and attractivity of killer applications


4.1 Set-up of UMTS networks

• UMTS-Planning is done through network simulations.

• This simulation typically consists of spreading users, engaging different services like voice or data transmission, in the environment.

• The simulating software will then predict the behavior of the simulated network –e.g. uplink, downlink, power

• These characteristics are directly related to interference and so capacity

• Simulation is based on propagation (power path-loss prediction)

• Doing simulations on the basis of bad predictions of the coverage will give nonsense results


Simulation – User distribution

Cost-Hata WaveSight

Green: OK ; Black: inactive; Red: mobile power outage; Pink: BS power outage; Purple: overload (load factor)


II. Propagation History

1. Empirical models

2. Semi-empirical models

3. Deterministic models (ray-tracing)


1. Empirical Models

Okumura-Hata

This model has been developed from measurements made by Okumura inTokyoand published [1] in 1968. Then the formula was given by Hata [2] in 1980.

• Advantage :

- Needs no building data

• Disadvantages :

- Needs expensive and time consuming calibration

- Rough prediction (circular shaped)

- the model is not appropriate for planning a sophisticated network


1.1 Empirical Models

• Shortcomings :

Despite this model has been developed especially for urban areas, the approach needs heavily calibration which is based on measurements and in some complex terrain like very dense cities it is very difficult to carry out.

[1] Y. Okumura, E. Ohmori, T. Kawano, K.Fukura: " Field strength and its variability in VHF and UHF land-mobile radio service", Review of the Electrical Communication Laboratory, Vol. 16 N°9-10, Sept. 68, pp 825-873.

[2] M. Hata: "Empirical formula for propagation loss in land mobile radio services", IEEE Transaction on Vehicular Technology, Vol.29 N°3, Aug. 80, pp 317-325.


Empirical Models

Expression given by Hata :L = 69.85 + 6.16 log10 (f) + 13.82 log10 (h) - (44.9 - 6.55 log10 (h)) log10

(d) - A - B - C – D

Where :f = frequency,d = distance transmitter-receiver, h = Base station heightA, B, C & D = relief loss, near obstacles loss, correction for mobile height (1.5m) other (rivers, wood, building density)


2. Semi-Empirical Models

Walfish-Ikegami

This model has been published for the first time by Bertoni and Walfish [4,5]. Then only the attenuation caused by buildings was implemented. Several ameliorations were added by different authors to improve the calculation formula. Ikegami has added the last reflection on the opposite wall.

• Advantage :

Takes into account terrain and building data

• Disadvantage :

Still needs expensive and time consuming calibrations


2.1 Semi-Empirical Models

• Shortcomings :

Despite this model takes the environment into account, it is still needed to calibrate it so the accuracy of the predictions is still not satisfying enough.

[4] J. Walfish, H.L. Bertoni: "A theoretical model of UHF propagation in urban area", IEEE Trans. on Ant. and Prop., Dec. 1988, pp1788-1796.

[5] H. L. Bertoni, J. Walfish: "A diffraction based theoretical model for predicting UHF path loss", IEEE Trans. on Veh. Tech., Vol. 37, 1988.


Semi-Empirical Models

Mobile

the reality

Base station

from terrain data base

w1 w2h

α

b

What can be computed by theory1 2 3 4 n-1 n

W

Reflected term(Ikegami)

Classical Fresnel diffraction

multi-screen effect frombase station to last diffraction =

Bertoni-Walfish formula :Pr = C * Pt / d

44


3. Deterministic Models

Since the early eighties, a lot of work has been carried out on physical models, which take into account all the three-dimensional environment. All of these models are only based on physical laws and use different techniques like ray-tracing or ray-launching.

• Advantages :- Does not need calibration (frequency, antennas, environment…)- More accurate- One model for all uses (macro small and micro cells).- Allows full channel information (Received power, direction of arrival, impulse

response, short term fading)

• Disadvantages :- Long computation time

• Shortcomings :Few: As the telecom industry is now looking for better accuracy in prediction results to improve the fine tuning of the network, the physical models look at this time the better alternative.


Deterministic Models

Problems involved

Reflector

Source

Virtual source

Receiver

Virtual source for each reflector

Source

Ray 2

Ray 1

Reflector

Construction imprecision

Reflector

Source

Virtual source

Receiver

Ray tracing method

Reflector

Source

Receiver

Ray launching method

Beam

Ray

Ray construction principle


Deterministic Models

Base station

Mobile


Deterministic models : WaveSightTM

1. Physical basics of WaveSight

2. Implemented parameters and algorithms

3. Data input

4. Simulation

5. Prediction outcome (accuracy and speed)


1. Physical basics of WaveSight

The propagation of waves is following different modes in the three-dimensional environment.

These propagation modes are :

• Free space

• Diffraction

• Transmission

• Reflection

• Scattering


1.1 Physical basics of WaveSight

a. Propagation in free space

Antenna gainPr = P * G / 4pd2

Energyconcentration

on a sector by antenna

Free spacePr = C * Pt / 4pd2



b. Diffraction phenomenon

Obs

tacl

eSource

Wave roundsthe obstacle

Attenuation near

shadowboundary

Obs

tacl

e

Source

Mobile

Diffraction phenomenon Ray representation of diffraction(GTD, UTD)

In shadow area, the received power is proportional to wavelength



Reflection, scattering and transmission phenomenon

Source

Reflection

Scattering

Transmission

Building

Transmission and scattering have no significant impact. So they are not taken into account by WaveSight.


Approach: Horizontal Plane Propagation

ray0

Receiver

Antenna


Approach: Vertical Plane Propagation


Conclusion

WaveSight is an enhanced ray tracing algorithm

close to ray launching (block ray construction with

image source).

It is 2,5 D (separate quasi horizontal propagation of

vertical propagation) Takes into account :- 2 reflections (Fresnel formula)

- 2 diffractions by vertical wedges (building corners) : UTD

- 15 diffractions by horizontal wedges (roofs) : UTD

- penetration in buildings (constant path loss)


2. Implemented Parameters and Algorithms

• Terrain File

• Building Vector Data

• Frequency

• Receiver Height

• Transmitter Coordinates (x, y, z)

• Transmitter power

• Antenna Tilt

• Antenna Azimuth

• Antenna Radiation Pattern (horizontal and vertical)


3. Data Input

• Data needed is vector data.

WaveSight is taking ASCII files as input for geographical data in MSI Planet format (supported by almost all planning tools)

• Which resolution/accuracy is needed ?

Common Terrain resolution is 5m, but 25m is sufficient (given that the terrain is not too hilly)

Building accuracy +/- 2m


Actual terrain data of Paris

Actual terrain data :

(Paris, about 1km x 2km)


4. Simulation

• WaveSight’s interface is intuitive and easy-to-use. Running a prediction can be done in 5 steps :

• Zooming in to see the area you are looking for

• Defining the antenna position

• Setting-up the antenna and base-station parameters

• Start the prediction

• Analysing results on the map


WaveSight of ArcView

Building and Terrain Data of a part of the City of Bern in Switzerland

(Vector Format)



Zooming



Zooming II



Defining an Antenna Position



Setting AntennaAnd Prediction

Parameter



Antenna Height,Power, Azimuth,

Tilt, Antenna-Type,Prediction-Radius



Start of thePrediction



PredictionRunning



Prediction with Radius of 300m

(prediction time lessthan a minute on a PII

400 machine)



Shadow Area Shadow

Area

Shadow Area



Canyon-Effect of Streets




Prediction Outcome (Accuracy and Speed)

• After the simplicity of WaveSight – needing no calibration and being extremely easy to use – has been shown before, the two main questions to prove WaveSights value are directed towards accuracy and speed.

• The accuracy determines wether WaveSight can improve the input for planning and therefore allows higher quality in network setup and fine-tuning.

• The speed determines wether WaveSight has a practical use at all. So far the big obstacle to the practical use of ray-tracing were computation times up to 12 hours for a microcell.

• The following slides show, how WaveSight typically performs regarding on these critical decision parameters and prove that WaveSight delivers a unique combination of simplicity, accuracy and speed!


…tested In The Real World

• Over 80 sites and 1000 km of routes validated:

10 cities / 7 countries

8 operators

• World class high-tech reference:

Bell Labs

EPFL

KPN

• 7 years of development

• Different city types:

• Paris• Munich• Bern• Fribourg• Tampa• Manhattan• Rotterdam• The Hague• Brussels• Torino

Continually growing verification pool!


Macrocell in Bern

Transmitter: Omni directional antenna, 632 m height, Frequency 1800 MHz

Building layout: Average building heights 614 m

Receivers: located within 1500 m from transmitter on a 20 km route

Computing time: 6 minutes on a Pentium II 266

Error with Measurements: Mean and standard deviation are 0 and 7.5 dB

-140

-120

-100

-80

-60

-40

-20

0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000Rx num ber

Pow

er [d

Bm

]

Measurements

WaveSight Predic tions

Measurements and buildings courtesy Swisscom and Istar respectively


Small Cell In Bern





Error with Measurements: Mean and standard deviation are .5 and 5.4 dB

-110

-90

-70

-50

-30

0 500 1000 1500 2000 2500 3000 3500 4000Rx number

Pow

er [d

Bm

]

Measurements

WaveSight Predictions



Microcell in Bern



Receivers: located within 1000 m from transmitter on a 2.5 km route


Error with Measurements: Mean and standard deviation are -1.5 and 8.2 dB

-110

-100

-90

-80

-70

-60

-50

-40

-30

0 500 1000 1500 2000 2500Receiver number

- Pat

h Lo

ss [d

B]

MeasurementsWaveSight Predictions



Small Cell in Munich


Building layout: Average building heights 16m



Error with Measurements: Mean and standard deviation are -0.2 and 6.4 dB

-160

-150

-140

-130

-120

-110

-100

-90

-80

-70

0 100 200 300 400 500 600 700 800 900 1000Receiver number

- Pat

h Lo

ss [d

B]


Measurements and buildings courtesy Mannesmann


Microcell in The Hague





Error with Measurements: Mean and standard deviation are 3.5 and 6.8 dB

-170

-160

-150

-140

-130

-120

-110

-100

-90

-80

-70

0 500 1000 1500 2000 2500Receiver number

- Pat

h Lo

ss [d

B]


Measurements and buildings courtesy KPN


Small Cell in The Hague






-160

-150

-140

-130

-120

-110

-100

-90

-80

-70

0 500 1000 1500 2000 2500Receiver number

- Pat

h Lo

ss [d

B]




Macrocell in The Hague






-160

-150

-140

-130

-120

-110

-100

-90

-80

-70

0 500 1000 1500 2000 2500Receiver number

- Pat

h Lo

ss [d

B]

MeasurementsWaveSight Prediction



Summarm of Accuracy

8.36.86.85.86.48.25.47.5

Standard Deviation (db)

2.54.13.5-1.5-0.2-1.50.50

Meanerror (db)

+40-3-3-3-11118

AntennaHeight (abovebuilding)

MacroSmallMicroSmallSmallMicroSmallMacroCell Size

The HagueTheHague

TheHague

MunichMunich

BernBernBern


Microcell in Paris

1099 sec. / 18,32 min.36 km2 (6000m x 6000m)

554 sec. / 9,23 min.16 km2 (4000m x 4000m)

153 sec. / 2,55 min.4 km2 (2000m x 2000m)

57 sec. / 0,95 min.1 km2 (1000m x 1000m)

29 sec. / 0,48 min.0,16 km2 (400m x 400m)

Calculation TimeArea

The calculations were performed with a 650 Mhz Pentium III PC with 192 MB RAM.


Macrocell in Torino

5802 sec. / 96,70 min64 km2 (8000m x 8999m)

951 sec. / 15,85 min.36 km2 (6000m x 6000m)

412 sec. / 6,87 min.16 km2 (4000m x 4000m)

246 sec. / 4,10 min.4 km2 (2000m x 2000m)

54 sec. / 0,90 min.1 km2 (1000m x 1000m)

25 sec. / 0,42 min.0,16 km2 (400m x 400m)

Calculation Time Area

The calculations were performed with a 650Mhz Pentium III PC with 192 MB RAM.


Microcell in Amsterdam

960 sec. / 16,0 min.16 km2 (4000m x 4000m)

315 sec. / 5,25 min.4 km2 (2000m x 2000m)

95 sec. / 1,58 min.1 km2 (1000m x 1000m)

17 sec. / 0,28 min.0,16 km2 (400m x 400m)




Microcell in Bern

196 sec. / 3,27 min.16 km2 (4000m x 4000m)

74 sec. / 1,23 min.4 km2 (2000m x 2000m)

34 sec. / 0,57 min.1 km2 (1000m x 1000m)

15 sec. / 0,25 min.0,16 km2 (400m x 400m)


The calculations were performed with a 650Mhz Pentium III PC with 192 MB RAM.


Macrocell in Paris

915 sec. / 15,25 min.36 km2 (6000m x 6000m)

492 sec. / 8,20 min.16 km2 (4000m x 4000m)

201 sec. / 3,35 min.4 km2 (2000m x 2000m)

73 sec. / 1,22 min.1 km2 (1000m x 1000m)

27 sec. / 0,45 min.0,16 km2 (400m x 400m)




Macrocell in Paris

990 sec. / 16,50 min.36 km2 (6000m x 6000m)

750 sec. / 12,50 min.16 km2 (4000m x 4000m)

355 sec. / 5,92 min.4 km2 (2000m x 2000m)

105 sec. / 1,75 min.1 km2 (1000m x 1000m)

30 sec. / 0,50 min.0,16 km2 (400m x 400m)




Summary of Speed

--96.70-64 km2

--15.85 min18.32 min36 km2

3.27 min16.0 min6.87 min9.23 min16 km2

1.23 min5.25 min4.10 min2.55 min4 km2

0.57 min1.58 min0.90 min0.95 min1 km2

0.25 min0.28 min0.42 min0.48 min0.16 km2

Microcell in Bern

Microcell in Amsterdam

Macrocell in Torino

Microcell in Paris

Area of Calculation


IV. The added value of WaveSight for network planning

1. Reduced need for measurement and drive tests

2. Better knowledge of Coverage

3. Better Input / better results for frequency planning

4. Rising network quality / better opportunities for fine/tuning

5. Increased speed of network set-up

6. Reduction in the number of needed sites


Reduced need for measurement and drive tests

• WaveSight is a deterministic model. The input consists of a rough picture of the physical environment and the calculation process is based on a picture of the physical behavior of the rays (UTD, Ray-Tracing, Maxwells-Theory of Rays).

• Because all signficant geographic information is taken into account in the calculation process, the model needs NO TUNING.

• Therefore a lot of money, time and energy which is going into drive testing can be saved by using WaveSight.


Better knowledge of Coverage

It is clearly visible that the coverage in the empirical prediction is only very crudely estimated (red area), whereas the WaveSight prediction takes the physical characteristics of the city into account (buildings, streets, terrain) to compute a precise map. The canyon effect of streets is visible.

Okumura-Hata Model (macro)+ Pseudo-Ray Tracer (micro)

WaveSight Model


Coverage by signal level

Cost-Hata WaveSight

Coverage by signal >=-75 dBm

>=-81 dBm

>=-85 dBm

>=-93 dBm

>=-96 dBm

>=-102 dBm

Measurements


Better Input / better results for frequency planning

• To show the impact of WaveSights predictions on frequency planning the Wavecall team has performed a study – based on the GSM-technology (download: www.wavecall.com/papers).

• The main objective of this case study was to show that using a sophisticated prediction model reduces cost and time in frequency planning.

• ILSA, a frequency-planning tool from Aircom, was used to compare frequency planning obtained by using classical propagation models and the ray-tracing model WaveSight.

• The tests presented were performed on a 4.5 km2 area comprising 17 sites (36 cells) actually used in the Bouygues Telecom network of Paris.

• This study demonstrates that using WaveSight can reduce the number of carriers needed to provide the same network quality from 47 carriers to 40. This is significant not only because it can reduce the cost of fine-tuning the network, but also because extra carriers can be used to increase traffic capacity.

• By using, WaveSight, the network interference area can be reduced by 80%.

http://www.wavecall.com/papersm


Rising network quality / better opportunities for fine/tuning

Changing a minor parameter can have astonishing impact on the coverage.

The single change made is the antenna’s height.

Initial coverage (left), Antenna 2m higher (above)


Best Server (-102dBm)

Cost-Hata WaveSight


Overlapping (- 80dBm)

Cost-Hata WaveSight

Intercell interference => noise increase => reduced capacity


Soft Handover – No handoff area

Cost-Hata WaveSight


Simulation – User distribution

Cost-Hata WaveSight

Green: OK ; Black: inactive; Red: mobile power outage; Pink: BS power outage; Purple: overload (load factor)


Simulation

• 232 active users

• Simulation with empirical prediction: 4 rejections (1.7 %) , due to load factor

• Simulation with deterministic prediction: 47 rejections (20.3%), 5.2 % due to power, 4.3 % due to pilot problem, 10.8% due to load factor. 18% of speech users, 22% of 64kbps users, 28% of 144kbps users.


Effective service area - Speech

Cost-Hata WaveSight

100% of area 90.3% of area


Effective service area - 64kbps

Cost-Hata WaveSight

100% of area 87.1 % of area


Service area - 144 kbps

Cost-Hata WaveSight

99.3 % of area 83.1 % of area


DL Service area 64kbps – different Eb/N0 threshold

Cost-Hata WaveSight

64kbps Service area (Eb/Nt maxi >= 10 dB

Eb/Nt maxi >= 8 dB

Eb/Nt maxi >= 5 dB

Eb/Nt maxi >= 1 dB


Conclusion & future

• UMTS simulations and planning are based on the path loss prediction => if it’s wrong the results are nonsense.

• Ray tracing models are also able to deliver other interesting feature for UMTS: impulse response, delay spread, direction of arrivals.


Increased speed of network set-up

• In the actual situation at the UMTS-frontier speed is desperately needed.

• For acquiring their licenses operators had to invest tremendous amounts of money, raising their debts to almost intolerable limits.

• Every day of delaying the start of their new networks and making money with their licenses can be said to cost millions of € in interest rates.

• Because WaveSight is easy to use, calculates accurate predictions extremely fast and needs no calibration to (non-existing) measurements it can significantly speed-up the planning phase and help to shorten the time to start the technical and commercial operation of the network –saving millions of € in interest rates.


Reduction in the number of needed sites

• In the planning phase, a propagation fading margin istaken in the link budget in order to raise the probability of good coverage.

• This margin is directly related to the standard deviationerror of the propagation prediction model.

• The more accurate the propagation model is, the lowerthe standard deviation is and the lower the needed margin is.

• A gain in the link budget results in a lower needed basestation density.

• An improvement of 1dB in the link budget corresponds to 12% reduction in the number of needed sites. 2dB corresponds to a 23 % reduction and 3dB to 32%. (based on a propagation path loss exponent of 3.52) 1

1 (ref WCDMA for UMTS, H.Holma A.Toskala)


Integration of WaveSight into Planning Tools

1. Integrated Planning Tools

2. Integration on Windows Systems

3. Integration on Unix Systems

4. License Protection


Integrated Planning Tools

Integration Completed :

• Enterprise – Aircom (Partnership with one-stop-shop and support)

• Totem – Nokia (Partnership with one-stop-shop and support in negotiation)

• Atoll – Forsk (Partnership with one-stop-shop and support)

• Odyssey – Logica (Partnership with one-stop-shop and support)

• Ellipse – CRIL

• Planet – MSI

Integration work in progress:

• Astrix – Teleplan (Partnership with one-stop-shop and support)

• Celplanner Suite – Celplan (Partnership with one-stop-shop and support in negotiation)

• Wizard – Safco/Agilent (Partnership with one-stop-shop and support in negotiation)

Companies with a positive stance towards integration• Quantum – Quotient

• Decibel Planner – Northwood


Integration on Windows Systems

Planning Tool

WaveSight Interface

Calc Array()

Create input files for WaveSight

Files.txt

1 2

Call

Winsight.exeDLL Library

w2c_wsal.dll

Read

Generates Results


Integration on Unix Systems

Planning Tool

WaveSight Interface

Calc Array()

Create input files for WaveSight

Files.txt

1 2

Call

WS.exeRead

Generates Results


License Protection

The licensing is made in 2 different ways :

1. Completely integrated into the planning tools, with whom we have a partnership agreement (protection with the dongle solution)

2. In the other case, with a node-locked license where a serial key is generated in link with the D-drive serial number (NT Workstation) or the Host-ID (Unix Workstation)

Documents

WaveSight TM - see your net work! - Wavecallwavecall.com/downloads/WSTechnicalPresentationV8.pdf · · 2007-04-25and published [1] in 1968. Then the formula was given by Hata [2]