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MONTE CARLO SIMULATION

FOR UMTS CAPACITY PLANNING

IN ONE - TELEKOM SLOVENIA GROUP

IGOR PUPALESKI

• ONE 3G Highlights

• ONE 3G Facts

• Monte Carlo method theory

• Monte Carlo simulation approach

• Conclusion and Future Developments

AGENDA

ONE 3G Highlights

• ONE is the convergent operator in Macedonia providing: Mobile,

Fixed, Broadband and DVB-T services

• First Mobile operator in Macedonia with 3G license

• First Mobile operator offering Mobile Broadband Internet -

HSDPA up to 7.2 Mbps

• Current 3G Population coverage is more than 81%

• 3G Coverage provided to more than 20 cities in Macedonia

• Covered main touristic places

• Constantly expanding the 3G coverage and capacity

ONE 3G Highlights – coverage map

ONE WADSL – coverage map

•Broadband Populat ion coverage approx. 93%•Covered more than 640 primary schools for internet kiosk project•Broadband internet offered to more than 1400 populated places

ONE 3G Facts - Data

2010 VS 2009 140% increase

2009 VS 2008 900% increase

Data growth

Data share

3G data

66%

2G data

34%

Total data share - Year 2008

3G data

92%

2G data

8%

Total data share - Year 2009

3G data

93%

2G data

7%

Total data share - Year 2010

ONE 3G Facts – Data & Voice & Subscribers

Data trend - 3G VS 2G Voice trend - 3G VS (2G + 3G)

3G subs

15%

2G subs

85%

Subscribers

“Pareto Rule”

3G subscribers (15%) are generating

85% of total data in the network

Monte Carlo Method Theory

• Introduced in the 1940s by physicists Enrico Fermi and Stanislaw Ulam , the

Monte Carlo methods are a class of computational algorithms that rely on

repeated random sampling to compute their results

• The technique is used by professionals in a wide range of fields such as

finance, project management, energy, research and development, insurance

and Telecommunications.

• The method consists in repeating an experience many times with different

randomly determined data in order to draw statistical conclusions; in mobile

network case, the users are deployed in the network with random position

• Simulate network regulation mechanisms for a user distribution and obtaining

network parameters

Monte Carlo

Monte Carlo Concept and Conclusion

1. The behavior of a network depends on many different aspects like power,

interference and services used

2. 3G radio network planning is performed through Monte Carlo simulations

trying to simulate a “real-life” network

3. Spreading the users on the environment and then compute the necessary

power for each user to fulfill the requirements

4. The power is calculated through steps adjusting the power according to the

power control algorithms and the simulation stops when the power converge

• For a given user and traffic distribution, the coverage predictions are used

to compute the offered service based on these simulations

• The results of one user and traffic distribution are gathered in a so-called

snapshot (a set of Mobile Stations in the network with random position)

Monte Carlo Simulation Parameters

Service configuration

Terminal configuration

Morphology configuration

Monte Carlo Simulation Approach

Input parameters:

3 sector UMTS sites, 65 beam width antenna, 3 meters over the roof,

BS power 43 dBm, pilot power 30 dBm, 400 users on 1Km2

Traffic repartition:

Voice = 40%

Video Call = 1%

R99 = 25%

HSDPA 3.6 = 30%

HSDPA 7.2 = 4%

Coverage map computed by Ray-Tracing propagation

Model for Urban environments

Coverage map with Cell Overlapping (-82 dBm threshold)

Monte Carlo Simulation Coverage Map Results

Superposition of 40 UMTS Simulation

Snapshots with 10% rejection, 6% due to

Low signal quality and 4% due to Overload

Superposition of 50 UMTS Simulation

Snapshots with 7% rejection, 5% due to

Low signal quality and 2% due to Overload

Overlapping sites removed to avoid interference

Snapshots with 2% rejection, 1% due to

Low signal quality and 1% due to Overload

In the UMTS simulations performed, an average of 400

users were spread in the area under investigations. On

average 160 users (~40%) are using the voice service,

(~25%) are using R99 (384kbps) connections and 120

(~30%) are on 3.6 Mbps.

For a UMTS radio network, more interference

means less capacity and less offered service

and/or at a lower quality

Conclusion and Future Developments

• Due to the high growth of 3G (data and voice) capacity optimization of the

UMTS network is needed

• Monte Carlo method is most suitable for UMTS capacity predictions

• Traffic optimization is needed in order to provide best services to our

customers

• Number of 3G terminals is constantly increasing and the customers are getting

more and more demanding

• ONE will permanently follow customer requirements in terms of coverage and

throughputs

• ONE is mobile broadband pioneer with leading contribution in technological

development of the country

THANK YOU

Q & A

igor.pupaleski@one.mk