Montecarlo 3 g

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An Introduction to3G Monte-Carlo simulations

within ProMan

responsible editor:

Hermann BuddendickAWE Communications GmbH

Otto-Lilienthal-Str. 36D-71034 Bblingen

Phone: +49 70 31 71 49 7 - 16Fax: +49 70 31 71 49 7 - 12

[email protected]

Issue Date Changes

V1.0 Feb. 2006 First version of document


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Monte Carlo UMTS system simulator 1

by AWE Communications GmbH February 2006

1 Motivation

System simulations are required for the network planning process on order to come to an costeffective investment in air interface network infrastructure. This is achieved by reducing the

number of Node B (and sites) to a minimum, still fulfilling capacity and service quality constraints.System simulations help to evaluate mobile network performance in different environments withdifferent configurations or network layouts. Performance indicators of interest are networkcapacity and service availability. There are mainly three approaches for the simulation of UMTSnetworks: dynamic simulations, semi-dynamic simulations (Monte Carlo simulator) and staticsimulations.

Dynamic System SimulationsOne way to model the network behavior is to use dynamic system simulations with quite realisticmodels for most aspects and effects (power control, soft handover, mobility, ...), and to simulatethe time-variant behavior of the network. Detailed results can be obtained by using this method,but it is very time consuming. It is mainly used to validate or optimize small parts of the networkor to perform parameter studies to tune the network settings.

Static Simulation / Analytic ApproachTo speed up the required simulation time and to enable the simulation of larger areas theconsideration of individual mobiles and discrete mobile distributions has to be given up, and thenetwork capacity is to be predicted with analytical methods. Therefore, in a second approach(static or analytical) the mobile terminals are considered to be distributed continuously over thesimulation area, i.e. for each pixel of the simulation area a kind of fractional mobile is assumed,and the complete simulation area has to be scanned only once. With this static/analytic approachthe capacity of a given UMTS FDD network layout can be estimated based on the propagationconditions in a very fast way. This is very useful for a first rough network planning with a fewiterations necessary to find a proper network layout that fulfills your needs.

Monte-Carlo SimulationsAnother option for network simulations is to use Monte-Carlo (MC) simulations with less detailedmodels (compared to the dynamic approach explained above): time variant effects are notconsidered but many (independent) snapshots with random mobile distributions are evaluated.These simulations are faster than dynamic simulations but to get statistically reliable results manysnapshots have to be carried out and the simulation time depends on the number of snapshotsand on the number of mobile stations. Practical examples show that the size of the area that canbe considered is limited to a few hundred cells.

The Monte Carlo (MC) method consists in repeating an experience many times with differentrandomly determined data in order to draw statistical conclusions. It can be applied for mobilenetworks simulation. In this case the repeated experience is called a snapshot and represents aset of Mobile Stations in the network with random position, state and parameters. The resultsgiven by a high number of snapshots are considered to be representative of all the possible statesof the network. The aim of a Monte Carlo simulator is to provide an analysis tool that allows thefast and accurate evaluation of the performance of a UMTS network during the planning andoptimization phases.


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OF (value) meaning

1 perfect orthogonality (1 path channel)

0..1 reduced orthogonality

0 no orthogonality at all

Table 1: Definition of Orthogonality factor within ProMan

Typical values for the orthogonality factor are OF = 0.5 for the ITU Vehicular A profile and 0.9 forthe ITU Pedestrian A channel profile. For the constant OF model an appropriate (mean) value forthe expected OF has to be used.

2.3 Service Mix

The simulator supports the definition of different services and mobile different mobile typecategories. Within the traffic specification (either homogenous or location dependent, see above)

each service can be considered individually, so that it is possible to define an arbitrary service mix.The most important parameters related to the service are the following:

Bitrate (UL/DL)

max. link power (UL/DL)

required Eb/No (to ensure service quality)

traffic parameters (Erl./sqkm)

2.4 System modeling

The aim of the downlink computation is to allocate the downlink transmit powers in order to match

the service downlink Eb/N0 requirements. The following scheme (Figure 2-1) should help you tofigure out the context of the calculation of the downlink power for MS(i). The aim of the uplinkcomputation is finding the uplink transmit power, i.e. which power the mobile station has to use tocommunicate with its serving base station. The following picture depicts the context of theprevious calculation of the uplink transmit power.

Figure 2-1: Downlink problem Figure 2-2: Uplink problem

The required Eb/Nofor each Mobile Station is known by link level simulation and is provided by thesystem supplier. It is defined as:


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( ),

, (1 )

i

i

TX BS i ib

o own other N DL MS

P a GE

I I P

=

+ +

(1)

Where: Eb/Nois the signal to noise ratio (on a net bit basis) PTXi is the required transmit power for the link (Serving sector of a Base Station BS(i) of

Mobile Station i to MS i aBS(i),iis the linear attenuation for the path BS(i) to MSi G is the processing gain is the downlink orthogonality factor for the considered link (UL: = 0) Iownis the interference level from the own cell Iotheris the interference level from the other cells

The previously mentioned interference levels are given by the following:

downlink uplink

( )( ), ( ),

_ _ _ ( )j BS iown TX B S i i CCH BS i i

j served by BS ij i

I P a P a

= +

(2) , , ( )_ _ _ ( ) UL jown TX j BS i

j served by BS ij i

I P a

= (3)

, , ,

( ) _ _ _ u j uother TX u i CCH u i

u BS i j served by u

I P a P a

= +

(4) , , ( )

_UL jother TX j BS i

j other cell

I P a

= (5)

2.5 Linear Equation System

Coming from the fundamental equations shown in the previous chapter, a linear system with thelink transmission powers as solutions can be built. This system can be described using a matrixnotation. PTX is the vertical vector containing the desired solutions for all transmission powers.

Using the fundamental equations the following coefficients can be determined:

downlink uplink

TXA P B = (6) TXC P D = (7)

( ) ( ), ,

( ),

(1 )BS i u

i

bi CCH BS i i N u i CCH

u BS io DL MS

EB P a P a P

N

= + +

(8)

, ( )

bi N

o UL BS i

ED P

N= (9)

( ),

, ( ),

( ),

(1 );(1)

;(2)

;(3)

i

i

bBS i i

o MS

bi j BS j i

o MS

BS i i

Ea

N

EA a

N

a G

=

(10)

, ( )

, ( )

,

( ),

; (1)

;(2)

bj BS i

o UL BS i

i j

BS i i

Ea

N

C

a G

=

(11)

with: (1) is valid if the MS j is different from the

MS i and belongs to the same cell as theMS i; i.e. they have the same servingsector BS(i)=BS(j).

(2) is used when the MS j belongs to

another cell, i.e. BS(j)

BS(i) (3) is used when i=j

with: (1) is valid if j is different from i (2) is used when i=j


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2.6 Coverage estimation

It was previously mentioned that the MS generation phase of the algorithm includes a coveragecheck. The algorithm used during this verification is detailed in this chapter.

2.6.1 Pilot channel based coverage criterions

In UMTS systems the coverage is mainly depending on the common pilot channels (CPICH)parameters and the interference level. Here, a MS is considered to be in the coverage zone if thefollowing two constraints are fulfilled:

The received CPICH level is above a predefined limit (for example -90dBm) CPICH carrier to interference ratio is above the threshold (for example -14dB)

As the transmit powers are not a priori known, assumptions have to be made in order to estimatethe interference level. The simulator assumes that the DL and UL transmit powers are maximum,and the resulting interferences is scaled by a predefined scaling factor.

2.6.2 Uplink related coverage criterionsThe condition checked by the simulator is the ability for a MS to communicate with its serving BS

with a sufficient uplink Eb/N0ratio assuming that the MS is transmitting with full power and theinterference in the network is fixed (predefined). The point is that the interference contributionsare unknown at this step of the calculations and therefore a fixed load defined by the user definedmaximum load factor and a scaling factor is used to assumes a typical interference level.

2.7 Capacity determination

For the capacity determination only the MS in the coverage zone are considered. This enables aseparate evaluation of capacity and coverage problems. The required transmit powers in DL andUL directions are determined according to the linear equation system. Then the snapshot can beevaluated, i.e. it is checked if all MS of this snapshot can be served by the network. Practically itmeans that some constraints are tested for both UL and DL. The snapshot is declared valid if all

these conditions are fulfilled. With the percentage of valid snapshots the probability for a certaincapacity can be estimated based on the traffic load. Usually a fixed number of 200 snapshots isperformed as it turned out within many simulations that more snapshots do not increase thereliability of the statistical results.

2.7.1 Downlink snapshot evaluationThe downlink transmit powers are determined to fulfill the service downlink Eb/N0requirements. Avalid snapshot must fulfill the following constraints:

The downlink transmit powers must be in the range [0..Pmax,DL] The total transmit power for the BS must not be exceeded by the sum of all links powers

2.7.2 Uplink snapshot evaluationThe UL transmit powers are computed accordingly to match the uplink service Eb/N0for all MS. Avalid snapshot must fulfill the following condition:

All determined transmission power must be in the range [0..Pmax,UL]


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Figure 2-3: Snapshot operations

3 Examples simulationTo demonstrate the performance of the Monte Carlo simulator, some examples and case studieswill now be presented. The first example illustrates the case of a bad scenario that has to beimproved with the help of the Monte Carlo simulator.

3.1 Optimization method

The coverage information provided by the simulator is calculated under typical traffic conditions.You can define the conditions of this evaluation by adapting the maximum load value factor. If youdont want to consider the effect of the coverage you can simply set this parameter to zero. Aconsequence of this method for the coverage estimation is that the coverage results provided are

independent of the traffic generated during the simulation: they depend only of the fixed loadfactor. Of course the capacity determined later on depends on the resulting coverage ( as it is wellknown for WCDMA networks). In this simulation model is has to be assured (by the user) that theassumed load (in coverage prediction) matches roughly the load determined by the capacityevaluation.

3.2 The example scenario

A single simulation is very useful in order to have a fast insight in the network for a defined trafficlevel. It provides interesting information about the behavior of the network in this state. Forexample, lets imagine that you need to estimate the quality of the following layout of a network.You want to test the network for a high data rate service (384kbps for DL, 64kbps for UL). You

expect at least 60% of valid snapshots for DL and UL for 25 users in the whole network (1.2 userper cell) and it is required that more than 90% of the area is covered for a typical load of 70%.


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Figure 3-1: Example network layout in a dense urban area (Paris France).

Extract of the result file:Parameters:Thu Dec 08 14:33:30 2005Number of snapshots: 100Traffic scale factor: 18.900Average Number of MS: 25.0Repartition of the generated MS per service:Service 384 -> 100.00 %

Coverage for Service 384:Coverage -> 99.84 %

Nr MS (according to traffic parameters)-> 25.00 1.19 users per cellMean Nr MS in the coverage zone-> 24.96 1.19 users per cellMean CPICH C/I -> -7.79 dBMean throughput per cell-> DL: 456.41 kb/s UL: 76.22 kb/s

Capacity:Mean total Nr of MS per cell: 1.19Mean total throughput per cell: DL:

456.41 kb/s UL: 76.22 kb/sResult of the simulations:Downlink -> 18 % of the Snapshots are validUplink -> 100 % of the Snapshots are valid

Mean power per Cell: 1999.229 mW 33.01 dBm (mean over all BS ofall valid snapshots)Mean Noise Rise per Cell: 1.107860 0.445 dB (average over all BS ofall valid snapshots)

3.2.1 Influence of the CPICH level

On the previous example, the Monte-Carlo simulator indicates that the layout and networkparameters should be improved because the capacity of the network does not satisfy therequirements. You can try to decrease the CPICH level in order to minimize the interferences. Of


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course the impact on coverage has to be observed. The effect of the CPICH power at the BS canbe checked by making an additional simulation with a CPICH level divided by two compared to theprevious simulation (now: 30dBm). A part of the result file is listed below

Coverage Service 384 :Coverage -> 99.00 %Nr MS (according to traffic parameters) -> 25.00 1.19 users per cellMean Nr MS in the coverage zone -> 24.75 1.18 users per cellMean CPICH C/I -> -10.79dBMean throughput per cell-> DL: 452.57 kb/s UL: 75.58 kb/s

Capacity Service 384 :Mean total Nr of MS per cell: 1.19Mean total throughput per cell: DL: 452.57 kb/s UL: 75.58 kb/sResult of the simulations:Downlink -> 65 % of the Snapshots are validUplink -> 100 % of the Snapshots are valid

Mean power per Cell: 1219.734 [mW] 30.86 dBm (mean over all BSof all valid snapshots)Mean Noise Rise per Cell: 1.103706 0.429 dB (average over all BS ofall valid snapshots)

From the results the influence of the CPICH power level can be clearly seen: 65% of valid snapshots for DL instead of 18% with the first simulation) impact on coverage is negligible (99% instead of 99.84%)

3.2.2 Influence of the orthogonality factor

In the previous simulations the orthogonality factor (quantifying the negative impact of the multi-paths propagation on the performances of the network in downlink) was set to 60%. Assuming amore optimistic value for the orthogonality factor (e.g. 80%) the following results are obtained(CPCIH power again 30dBm):

Coverage Service 384 :Coverage -> 99.04 %Nr MS (according to traffic parameters) -> 26.00 1.24 users per cellMean Nr MS in the coverage zone -> 25.75 1.23 users per cellMean CPICH C/I -> -12.04dBMean throughput per cell-> DL: 470.86 kb/s UL: 78.63 kb/s

Capacity Service 384 :Mean total Nr of MS per cell: 1.24Mean total throughput per cell: DL: 470.86 kb/s UL: 78.63 [kb/s]Result of the simulations:Downlink -> 78 % of the Snapshots are validUplink -> 100 % of the Snapshots are valid

Mean power per Cell: 1140.029 mW 30.57 dBm (mean over all BS ofall valid snapshots)Mean Noise Rise per Cell: 1.112659 0.464 dB (average over all BS of

all valid snapshots)


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You can notice that the previous change has the following consequences on the simulation results: 78% of downlink valid snapshots instead of 65% whereas the traffic has been increased similar coverage, mean power per cell, and noise rise values

If the orthogonality factor was set to 1.0 (perfect orthogonality) 97% of the snapshots would bevalid for DL under the same conditions. This shows the great impact of the downlink orthogonalityfactor. The low capacity of this network is due to the simple design of the layout (orientation ofthe antennas, location of the base stations).

3.3 Evaluation of maximum capacity

Performing several simulations with the parameters as described above and additionally withincreasing network load the following results are obtained. It can be seen, that in this (notoptimized) network scenario a cell capacity of about 400kbps is predicted if 90% of the UEdistributions should be served. For a higher cell throughput, i.e. more mobiles, the probability toserve a certain constellation decreases, but there are still (some) advantageous constellation thatstill can be served. Thats why there is a soft degradation in the serving probability. Furthermorethe resulting MS and BS power distributions are depicted in the figure below.

Figure 3-2: Percentage of successful snapshots for DL (left) and average DL power per cell (right).

Figure 3-3: Example for the noise rise output (with increasing traffic load)


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Figure 3-4: DL link power distribution (left) and UE power distribution (right)

4 Propagation Prediction

The 3G Monte-Carlo simulation approach is based on the predicted path loss matrices for all base

stations. So after having defined a network scenario, first the propagation prediction has to beperformed, and in a second step the network prediction (i.e. 3g Monte-Carlo simulation) can berun. In small scenarios the propagation may be predicted for each transmitter for the completesimulation area. Considering large areas a prediction radius can be defined for each transmitter.Note that the definition of the prediction radius has to ensure sufficient overlap of neighboringcells. This overlap is not only required for best server determination, but also for interferenceconsiderations, i.e. the overlap should be designed to account for the interference of approx. tworings of interfering cells at each pixel. That means that the prediction radius should be approx. 2-3times the inter-site distance.

Many different propagation models are available within ProMan. Depending on the scenario, the

environment, and the available database the best suitable propagation model must be chosen. TheMonte Carlo simulator is able to handle the propagation results generated by all available models.Further information about the propagation models can be found on the following web site:

http://www.awe-communications.com/Propagation/index.htm

Furthermore it should be mentioned that additional information about the propagation models isalso available in form of application notes. See the following web site:

http://www.awe-communications.com/ApplicationNotes/index.html

5 Further Information

For further information you are invited to visit AWE Communications website

http://www.awe-communications.comhttp://www.awe-communications.com/Network/3G/SimulatorsOverview/MonteCarlo.html

or to send an e-mail to the responsible editor of this document

[email protected]

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