gsm case study v1 7

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Wavecall SA CONFIDENTIAL Page 1/1 November 2000

WaveSights impact on frequencyplanning:

The added value of using a realisticprediction model instead of a

classical model

Odeh GHAWI


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1 GLOSSARY OF TERMS ...................................................................................... 4

2 EXECUTIVE SUMMARY ...................................................................................... 6

3 INTRODUCTION .................................................................................................. 7

4 DESCRIPTION OF THE TEST CONFIGURATION.............................................. 9

4.1 Sites ..................................................................................................................................9

4.2 Cell configurations..........................................................................................................9

4.3 Antenna pattern............................................................................................................10

4.4 Description of tools and models...................................................................................10

5 DESCRIPTION OF THE COMPARISONS ......................................................... 11

5.1 Methodology..................................................................................................................11

5.2 Neighbour relations ......................................................................................................11

5.3 Carrier layers ................................................................................................................12

5.4 Additional frequency planning tool configurations...................................................12

5.5 Parameter settings for the prediction models ............................................................125.5.1 WaveSight................................................................................................................125.5.2 Macro-cell classical model.......................................................................................135.5.3 Micro-cell classical model ....................................................................................... 135.5.4 Comparison of model run times...............................................................................14

5.6 Coverage array..............................................................................................................14

5.7 Interference table..........................................................................................................17

5.8 Frequency planning using ILSA..................................................................................17

6 DESCRIPTION OF RESULTS............................................................................ 20

6.1 Considered parameters ................................................................................................20

6.2 Analysis of results .........................................................................................................21

7 CONCLUSION.................................................................................................... 25

8 APPENDIX I........................................................................................................ 27


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9 APPENDIX II....................................................................................................... 29

10 APPENDIX III................................................................................................... 32


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1 Glossary of terms

BCCH Broadcast Control Channel in this context referring to the entire carriercontaining the BCCH

C/I Carrier to interferenceDTM Digital Terrain ModelDTX Discontinuous TransmissionILSA Intelligent Local Search AlgorithmTCH Traffic Channel in this context referring to carriers not containing the BCCHTRX Transmitter/Receiver


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Company information

Address Wavecall SAScience ParkSwiss Institute of Technology

PSE-B / EPFL1015 Lausanne

Phone

Fax

+41 21 693 84 05+41 21 693 84 06

Contact Odeh Ghawi

E-Mail

Web

[email protected]://www.wavecall.com/

Document history

Version Revision Date

1.0 O.Ghawi (Quality Manager) October, 10 2000

2.0 O.Ghawi (Quality Manager) November, 8 2000

3.0 O.Ghawi (Quality Manager) November, 28 2000

4.0 O.Ghawi (Quality Manager) December, 8 2000

5.0 O.Ghawi (Quality Manager) December, 18 2000

6.0 O.Ghawi (Quality Manager) January, 9 20017.0 J.-F. Wagen (Consultant) September, 6 2001


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2 Executive Summary

The main objective of this case study is to show that using a sophisticated prediction modelreduces cost and saves time in the planning of radio cellular network especially when

adjusting the frequency plans.

In this case study, a frequency-planning tool (ILSA) from AIRCOM, Ltd., was used tocompare frequency plans obtained by using1. A classical propagation model and2. The ray-tracing model WaveSight.The tests presented here were performed on a 4.5 km x 4.5 km area in the city of Pariscomprising 17 sites (36 cells). All data except the predictions have been kindly provided bythe French operator Bouygues Telecom, the buildings database was provided by Istar.

This study demonstrates that using the WaveSight model has the following advantages:

1. The area where interference is unacceptable can be reduced by 80%. This reduction couldbe translated directly into an increase of traffic (or revenue) or less lost traffic.

2. It could reduce the number of carriers needed to provide the same quality in a radionetwork. In the case investigated here, it was possible to reduce from 47 to 40 the numberof necessary carrier. 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.

3. WaveSight does not needs any calibration, thus the use of WaveSight saves time,measurements and provides more realistic prediction.


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3 Introduction

Frequency planning is one of the very demanding tasks in planning cellular networks,especially GSM networks. A better arrangement of frequencies can significantly increase the

service quality an/or ease an increase of network capacity usually required while trying tominimise the impact on the existing base stations in order to save cost.

This study was conducted to evaluate the impact the advanced propagation prediction modelWaveSight will have on frequency planning.

The GSM radio network performance obtained by using two classical propagation models has been compared to the results obtained by using the advanced WaveSight model, whichincludes the effect of buildings.

All results have been obtained for a set of 36 cells deployed by the French operator Bouygues

Telecom over 17 sites in a 4.5 km x 4.5 km area in the city of Paris. The radio network dataand the geographical databases have been kindly provided by Bouygues Telecom and ISTAR,respectively. Predictions have been computed using a classical Okumura-Hata-type model(CM) and the WaveSight model (WS) from Wavecall. The frequency plans have beengenerated using the commercially available frequency-planning tool ILSA integrated in theexcellent AIRCOMs ENTERPRISE suit (www.AIRCOM.co.uk).

The investigation documents the results as follows:

Given a frequency plan, we compare the carrier to interference (C/I) values predictedby the different models, classical (CM) and WaveSight (WS). This investigations

allows to determine the areas where the quality is considered to be acceptable usingthe classical prediction model (CM) while the performance predicted by the accuracyof WaveSight model (WS) are not sufficient. The results can also be used todetermine where the CM predicts insufficient C/I while the WS predicts sufficient

performance. This last result is less interesting unless the area of poor C/I isunacceptable and such that a new design or changes in the parameters of the radionetwork would have to be implemented. In this case the implementation changeswould be spurious.

Given a radio network layout, we produce new frequency plans based on the predictions from the two models CM and WS and we compare the number of GSM

frequency carrier frequency required to achieve the same level of network quality.

Since most operators would rather increase the offered capacity their network thansave a few TRX equipments, this investigation is mainly of interest in green fieldsituations or when elaborating bids. However, there are so many possible ways ofincreasing the capacity (adding TRX, adding sectors, adding sites, ), that it is out ofthe scope of this case study to investigate the increase of capacity resulting from theuse of the accurate WaveSight model. Since the saved carrier frequencies could beused to add TRX, it can be claimed that the increase in capacity is roughly

proportional to the number of frequency saved. More quantitative results could only be meaningful when based on a more detailed case study performed with (usuallyconfidential) traffic data. Engineers at Wavecall would be happy to assist anyoperators for more detailed case studies if desired.

The run time taken for the computations performed for this case study are provided.While run times are of increasingly lesser importance as the workstation increase their


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power regularly, it is still of interest for comparison purposes to provide these results.The run time results, together with the previous performance results, allow the radio

planners to appreciate the quality-versus-cost offered by the WaveSight model.

This case study is structured as follows. The next chapter, Chapter 4, describes the network

configuration used here. Chapter 5 presents our methodology. Chapter 5 also presents thesoftware tools, the two prediction models (classical: CM and WaveSight: WS) and the chosen

parameters. The main results are then analysed in Chapter 6. Finally, conclusions are providedin Chapter 7. Additional plots and tables detailing the results are placed in Annexes I to III.


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4 Description of the test configuration

The following investigations are performed on a 4.5 km x 4.5 km area in Saint Michel, adistrict of Paris, France. The base stations and antenna configuration were kindly provided by

Bouygues Telecom (www.bouygtel.com), a French GSM mobile operator, parts of thisdocument cannot be reproduced or extracted without contacting Wavecall([email protected]).

4.1 SitesThe area under study includes 17 sites. These 17 sites are described (location, antenna height,transmitted power and antenna downtilt are given) in Table 1 (for the so-called micro-cellsites) and in Table 2 (for the macro-cell sites).

Table 1 The 7 micro-cell sites

Site X Y Height (m) Power

(dBm)

Downtilt

Site1 600608 2428238 6 36 0

Site2 600354 2427795 6 36 0

Site3 600316 2428333 7 36 0

Site4 600197 2427946 5 36 0

Site5 599482 2428168 5 36 0

Site6 600264 2428341 5 36 0

Site7 600698 2428324 5 36 0

Table 2 The 10 macro-cell sites

Site X Y Height (m) Power

(dBm)

Downtilt

Site8 598592 2428336 36 53 0

Site9 599143 2427469 32 53 0

Site10 599143 2427933 36 53 0

Site11 600755 2427738 26 53 0

Site12 599502 2427681 34 53 0

Site13 599966 2428275 32 53 0Site14 599852 2429455 30 53 0

Site15 599298 2428658 32 53 0

Site16 600657 2429096 35 53 0

Site17 598989 2429016 31 51 0

4.2 Cell configurations

The macro-cell sites consist usually of 3 sector antennas oriented respectively on 0, 120 and240 azimuth. Only site 12 has only two antennas oriented on 0 and 120 azimuth. Each

antenna defined a different sector or cell.The micro-cell sites always define a single cell only.


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4.3 Antenna pattern

Two types of antennas were used: the so-calledBouyguemicrocellomnidirectonal antenna forthe micro-cells, and the directionalPCN_S_085_19_5 antenna for the macro-cells. Thevertical and horizontal patterns for the two antennas are shown in Figure 9, Figure 10, Figure11 and Figure 12 in Appendix I.

4.4 Description of tools and models

The user-friendly ASSET software, from the AIRCOMs ENTERPRISE suit, was used as theengineering tool to set the radio network configuration, run the prediction models, display theresults and export them.

As mentioned in the introduction, two propagation models have been used: a classical model(CM) and the WaveSight model (WS).

The classical model (CM) implies two different algorithms: one for micro-cells and anotherone for macro-cells. The macro-cell model is based on the ETSI Hata model, and the micro-cell model is based on a pseudo-ray technique using terrain height, and building outlines.

As usual, the classical model requires measurements to be calibrated. Since this case studyintend to compare generic Classical Model to the WaveSight model we did not used all the

possibilities provided by the AIRCOMs tool to optimise the calibration of the classical

model. This calibration that has been performed is described in the next section. Thecalibration used has not been completely optimised but it provides a very good idea of whatcould be obtained in the radio planning of changes in antenna orientation or down-tilt, or of anew region or when new buildings have grown, or when measurements are not very extensive.In fact, extensive calibration of a classical model is only useful when optimising the frequency

plan. Any other change in the radio network might affect the calibration. Thus, the calibrationused in this study is claimed to be sufficient. Furthermore, since it is easier and more costefficient to optimise a well-planned network and since, it is obviously not possible to takemeasurements from base-stations not yet deployed, a perfect calibration of any ClassicalModel is not trivial.

The WaveSight model (WS) does not require any calibration. The WaveSight model is a fullydeterministic model based on an efficient implementation of real ray-tracing algorithms. Thesame basic principle and the same basic algorithms are used for both macro-cells and micro-cells. The WaveSight model uses ground height, building outlines and building height tocalculate the predicted field strength. Other radio parameters can be computed by WaveSight(angle of arrival, delay spread, ) but these are out of the scope of this study.

Accurate terrain data (building and ground) was kindly provided by ISTAR (www.istar.com).

Frequency plans have been computed using the efficient ILSA tools included in the renowned

AIRCOMs ENTERPRISE suit. More details about the frequency planning algorithms are notprovided here but are available directly from AIRCOM (www.aircom.co.uk).


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5 Description of the comparisons

5.1 Methodology

To evaluate the end effects resulting from the use of two different prediction models, thefollowing method has been used. The evaluation should include all the major steps involved inthe planning of a complete radio network in a given area. Thus, the steps include coverage

predictions based on geographical and radio network data, computation of the frequencychannel constraints, generation of a frequency plan and final evaluation of the performance ofthis frequency plan. Comparisons are made between the results obtained when using aclassical coverage prediction model (CM) and when using the more accurate WaveSightmodel (WS). The steps are then as follows:

1. Generate a frequency plan (CM_FP) using the AIRCOMs ILSA software based onthe predictions computed with the classical prediction models.

2. Use the results of the first step along with WaveSight coverage predictions to computethe ratio of Carrier/Interference in the network (CM_FP_WS_MDL in Appendix).

3. Generate a frequency plan (WS_FP) using the AIRCOMs ILSA software based on thepredictions computed with the WaveSight model.

4. Use the results from the previous step along with WaveSight coverage predictions tocompute the ratio of Carrier/Interference in the network (WS_FP_WS_MDL inAppendix).

Assuming that WaveSight provides more accurate coverage predictions, the comparison of the

interference levels (step 2. and 4. above) indicates the value of using the more realisticWaveSight model instead of a classical model.

Other performance measures quantifying the accuracy of the predictions compared tomeasurements are not the focus in this study. Therefore, metrics like standard deviation, meanerror values, hit-rates, are not used here. However, such results are available fromhttp://www.wavecall.com/prediction.html.

5.2 Neighbour relations

The valuable Neighbour wizard of the AIRCOMs Asset software tool was used to generatethe neighbours relation between cells. Since neither the same carrier nor adjacent carrierscould be attributed to neighbouring cells, the number of neighbouring cells affects thefrequency plan.

In this study we want to focus on the difference between the uses of two prediction models.We are not interested in the penalties for misplacing a carrier in two neighbours (see

paragraph 5.8). We expect the frequency planning tool to work at reducing the interferenceand not to work at rearranging and/or rejecting frequencies to satisfy neighboursrelationships. Furthermore, the neighbours relationships are usually not very sensitive to theaccuracy of the prediction tools. Thus, we have adjusted the neighbours relationship, the


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overlap area factor in particular, in a manner such that the frequency-planning tool couldallocate all carriers.

The following other parameters were used:

The maximum number of neighbours was set to 12 including co-site cells. Amaximum of 10 was also used.

The Handover hysteresis margin was set at 6 dB.

5.3 Carrier layers

Three frequency bands spanning a total of 42 GSM carriers were assumed to be available asgiven in Table 3.

Table 3: Frequency numbering in the three bands.

45-56 80-91 107-124

12 carriers available 12 carriers available 18 carriers available

Each carrier must belong to one of the two layers called either the Broadcast Control Channel(BCCH) layer or the Traffic Channel (TCH) layer. Only one carrier is assigned to the BCCHlayer in each cell. Additional carriers, if any, belong to the TCH layer.

5.4 Additional frequency planning tool configurations

The prediction for each cell was performed in an area with a 3 km radius for themacro-cell configuration and a 1 km radius for the micro-cell configuration with5 m x 5 m resolution.

No downlink DTX was used.

The frequency hopping was disabled.

Traffic data was confidential and thus was not used. While this might be seen as amajor drawback, the goal of this study is to investigate the overall effect of using avery accurate prediction model. Thus, assuming a uniform traffic simplifies theanalysis without a great loss of generality.

5.5 Parameter settings for the prediction models

All prediction models require at least two parameters: the overall frequency band, taken hereat 1800 MHz, and the mobile antenna height, set in this study to a usual height of 1.5 m.

5.5.1 WaveSight

WaveSight does not require any calibration.


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5.5.2 Macro-cell classical model

In a macro-cell configuration, the classical model (CM) is taken as the ETSI Hata modeldefined by the following formula:

Path Loss (dB) = k1 + k2*log(d) + k3*Hms + k4*log(Hms)+k5*log (Heff) + k6*log(Heff)*log (d)+ k7*(Diffraction Loss) + Clutter Loss

Where:

The parameters k1, k2, k3, k4, k5, k6, k7 have to be calibrated to reach the loweststandard deviation between the model prediction and the measurements.

D is the distance from the base station to the mobile (km).

Hms is the height of the mobile antenna above ground (m).

Heff is the effective height of the base station antenna (m) defined as the relativeheight to the mobile.

Diffraction Loss was calculated by the Epstein Peterson method.

Clutter Loss was not considered and was set here to 0.

Thanks to a very useful feature of the AIRCOMs ASSET software, the classical model couldbe calibrated on a set of measurements provided for all the 10 macro sites considered here.After calibration we obtained a mean standard deviation of the prediction error of 10 dB whenevaluated over the 10 sites, with a standard deviation of 3.6 dB among the results for eachsites. The mean error was 0 dB when averaged over all sites. The mean error computed overeach site has a standard deviation of 8 dB. More details are available upon request from theauthor or from our web site www.wavecall.com.

The parameters of the calibrated macro-cell classical model are shown below in Table 4:

Table 4: Parameters of the Hata formula

K1 K2 K3 K4 K5 K6 K7

167.15 35 -2.55 0 -13.82 0 0.8

An even better calibration might have been obtained from the ASSET software, for example acalibration per site could have been performed. However our goal was to obtain a standarddeviation of about 10 dB to simulate the widely accepted performance of a classical model innon urban area for a cell radius less than 3 km.

5.5.3 Micro-cell classical model

The micro-cell model considered here is based on pseudo-ray technique that uses buildingsoutlines and a 5 meters resolution digital terrain model (DTM) height. As the area underinvestigation is flat, the DTM was not needed.

The Micro-cell classical model employs two different algorithms whether the mobile is inline-of-sight (LOS) or in non line-of-sight (NLOS) from the base station. In the LOS case, the

path loss is computed by a dual-slope formula. In the NLOS case, the building corner plays

the role of a secondary source. The parameters of the model have been calibrated and aregiven in Table 5 and Table 6, respectively.


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On the two sites where measurements were available for calibration, the prediction mean errorwas of 6 dB with a standard deviation of 10 dB.

Table 5 LOS parameters

Loss at 1 m (dB) Near Slope(dB/decade)

Far Slope(dB/decade)

Ant. Ht. Gain Breakpoint

27 9.2 45 8.2 Calculatedautomatically

Table 6Non-LOS parameters

Forwardscatter. Nearslope(dB/decade)

Forwardscatter. Farslope(dB/decade)

Back scatter.Near slope(dB/decade)

Back scatter.Far slope(dB/decade)

Breakpoint

Highestordervirtualsource

Maxdistancetodiffractingedge

9.2 20 0 18.7 0 2 6

Again, the calibration used here might have been further optimised, especially if moremeasurements had been available. However, the parameters used here are believed torepresent fairly the performance of this type of LOS/NLOS micro-cell model.

5.5.4 Comparison of computation times

Table 7 shows the computation time required to predict coverage over a single cell. Itcorresponds to the average found over the 47 cells of the study.Both prediction models (CM and WS) were executed on a Pentium III-PC 650MHz with 256MB RAM.

Table 7: Mean computation time for different configurations

Micro-cell Macro-cell

WaveSight model Classical model(Pseudo-Ray-Tracing)

WaveSight model Classical model(Okamura-Hata)

6 min 150 min 25 min 8 min

It is worth noting that in micro-cellular environments, in which computation time is veryimportant, WaveSight is 25 times faster than the classical model. In macro-cellularenvironments, the classical model is approximately 3 times faster. However, the classicalmacro-cell model does not take into account building database and thus its predictionaccuracy is rather poor, especially near a base station.

5.6 Coverage array

Based on the coverage predictions, it is possible with the versatile ASSET software to

generate a so-called per carrier interference array. This array stores the worst C/I (lowestnumerical value) and the total C/I (C/Total_I) for each carrier on each pixel representing


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here a 5 m x 5 m areas in the prediction grid. All the co- and adjacent-carriers from allinterfering cells are taken into account. The per carrier interference array is an array suchthat for each pixel we have a list of serving carriers plus the worst and total C/I for eachcarrier.

The best server map displays the colour-coded cell providing the highest carrier power (C)

value in each pixel. The best server coverage map provides the highest C value in each pixel.The best server coverage map obtained from the classical model (CM) and the WaveSightmodel (WS) are shown inFigure2, and Figure 3, respectively.

The parameters considered to create the interference array are shown in Table 8 and areexplained below:Minimum service level: is the minimum service level at which a cell is considered to be aserving cell. 104 dBm is a rather typical value (the minimum value is 110 dBm).Maximum timing advance: Is the maximum difference in timing between transmission andreception. This effectively defines the maximum radius at which a cell will be considered a

best server even if the signal is still good in terms of absolute value. The maximum value is 63(corresponding to a 35 km radius) which means that no restriction occur here.

Adjacent channel offset: specifies the offset that will be applied to co-channel carriers tointerference value (C/I) to obtain the adjacent channel C/A value. C/A = C/I + Adjacentchannel offset. 18 dB is a typical value.

Table 8: Parameters used to create the interference array

Minimum service level(dBm)

Maximum timing advance Adjacent channel offset[dB]

-104 63 -18

No traffic data (i.e., a uniform traffic is considered), no Frequency hopping and no downlinkDTX were used.

In this study, both the Worst interfererand the Total interference criteria were considered forcomparisons between the planning using the classical model (CM) and the WaveSight model(WS).

Poor quality areas are those where the C/I level is less than 12 dB (all non-green area in the

Figures shown in the Appendix).


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Figure 1 Best server coverage for the classical model (micro- and macro-cell) predictions.

The coarse accuracy of these predictions is obvious.


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Figure 2 The best server coverage for the WaveSight prediction. The improved accuracy iseasily appreciated when comparing the results with those of Figure 1.

Figure 3 Colour code used in the best server coverage maps above.

5.7 Interference tableThe interference table was created using the AIRCOMs ASSET planning tool. Theinterference table describes the interference that would result if any two cells were allocatedthe same or adjacent carriers. The AIRCOMs frequency-planning tool (ILSA) uses thisinterference table to calculate the overall level of interference that a given frequency planswould give. In this study, we tried to reduce the overall level of interference by area, as the

traffic raster data is not available.

To create the interference table, the user-friendly interference table wizard of ASSET wasappreciated. We used the parameters shown in Table 9.

Table 9: Parameters used to create the interference table

Minimum service level dBm Maximum timing advance micro sec

-104 63

5.8 Frequency planning using ILSA

The AIRCOMs frequency planning software ILSA uses a powerful iterative algorithm togenerate and improve a frequency plan. The quality of the result is guided by a penalty systemof non-respected rules. By appropriately choosing the penalties, the user can guide thesoftware towards a desired goal.

In this study we assumed one carrier per layer (BCCH, TCH), with the following parameters:

Entire sites where selected for planning.

No fix carriers were allowed (a realistic assumption if the frequency plan has no pre-existing restriction)


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Frequency hopping and downlink DTX were disabled (to simplify the interpretation ofthe results).

The so-called separation rules that dictate the separation between two carriers are defined byan integer. For example, a separation of 0 means that the carriers could be the same. Aseparation of 1 means that the two carriers can be adjacent. Separation numbers for differentcases are shown in Table 10.

Table 10 Frequencies separation rules used in this study.

Carrier Layer Co-Cell Co-site Neighbour 2nd neighbour

BCCH 2 2 2 0

TCH 2 2 2 0

The penalty or relative cost (in $ for example) of not applying to those rules is shown

in Table 11. Since the cost of interference alone is substantially low, this will help identify whether

or not the planning tool ISLA applied the rule presented in Table 10. Any relative costbelow 50000$ would indicate that ILSA tool places carriers according to the rules andworks only on reducing the interference.

Table 11Frequencies separation penalty costs for not applying separation rules.

Carrier Layer Cell Cost Site Cost NeighbourCost

Weight

BCCH 100000 75000 50000 1

TCH 100000 75000 50000 1

The ILSA software was allowed to run until the cost of the plan had becomereasonably stable. We obtained an average time of 6 hours per test. Figure 4 shows theevolution of some ILSA parameters during the frequency planning process.

A total of 19 tests were performed.


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Figure 4For information here is the print screen of a graph given by ILSA, plots the iterationnumber vs. the cost of the plan (green), the average interference (red), and the Worst

interference (blue). The fast drop of the blue line shows the time when all the separation rules

were met.


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6 Description of results

6.1 The considered parameters

To make a meaningful comparison, the following parameters were considered:

Planning cost (this is the cost resulting from the violations of the constraints given thepenalty cost of Table 11).

Traffic lost (mErlang. In this investigation, the traffic lost is directly proportional tothe area since we assume a uniform traffic distribution).

Percent of bad areas based on the Worst interference statistics.

Percent of bad areas based on the Total interference statistics.

Worst interference: For each 5 m x 5 m area pixel, the carrier with the worst C/I is

determined by taking the stronger interferer generated by the others sites. The result is the so-called worst C/I.The worst C/Iis meaningful since frequency hopping was not considered here.

Total interference: For each 5 m x 5 m area pixel, the total interference array gives the ratioof the carrier signal strength within the pixel to the power sum of the interfering signalstrength generated by other sites. The C/I calculated in the total interference array is notexperienced by any subscriber but provides an idea of the interference strength.

The interference arrays were calculated as follows:1. Generate a frequency plan (CM_FP) using the AIRCOMs ILSA software based on the

predictions computed with the classical prediction models.2. Use the results of the first step along with the classical prediction models to compute the

ratio of Carrier/Interference in the network (CM_FP_CM_MDL in Tables 15-17).3. Use the results of the first step along with WaveSight coverage predictions to compute the

ratio of Carrier/Interference in the network (CM_FP_WS_MDL in Tables 15-17).4. Generate a frequency plan (WS_FP) using the AIRCOMs ILSA software based on the

predictions computed with the WaveSight model.5. Use the results from the previous step along with WaveSight coverage predictions to

compute the ratio of Carrier/Interference in the network (WS_FP_WS_MDL in Tables 15-17).

The results are detailed in Appendix II, Table 15 ,Table 16and Table 17.


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6.2 Analysis of the results

We compare the performance of the prediction models, after frequency planning, according tothe total size of areas where interference are unacceptable while sufficient carrier power isavailable for coverage. This performance measure is related to the amount of lost traffic.

Another performance measure is also used: namely the required number of carrier frequenciesachieving a frequency plan without penalty. This minimum number of carriers provides arough estimation of the possible increase in capacity remaining in the network. Indeed, afrequency plan that lowers the resulting interference allows the planner to free somefrequencies to plan for additional carriers, thus offering an increase in capacity.

The goal is to evaluate the performance resulting from the use of either one of the twoprediction models, the classical (CM) and the WaveSight (WS) models. Various interferenceenvironments were considered to obtain results under different test conditions. Three differenttests are documented here. The tests were generated by changing the number of neighbourrelations and the minimum overlapping area size (dictating whether two cells are neighbour or

not):I.) Maximum of 12 neighbour relations, minimum overlapping area size: 0.01 km2.II.) Maximum of 10 neighbour relations, minimum overlapping area size: 0.01 km2.III.) Maximum of 10 neighbour relations, large minimum overlapping area size: 0.122 km2.

I.) In the first test, a maximum of 12 neighbour relations was imposed. With the classicalmodel, no frequency plan could be found to meet the Co-Cell, Co-site, Neighbour, 2ndneighbour separation rules of (2, 2, 2, 0) (Table 10) when using the 42 available carriers. Inorder to guess how many frequencies were needed to comply with the rules, more carrierswere artificially added to the network. It could thus be shown that at least 47 carriers were

required for the classical model to give the same network quality. A frequency plan based onthe WaveSight predictions used only 40 carriers.

Furthermore, Table 12 shows that the classical model leads to 1.63% more bad areas thanwhen using the WaveSight model. (See also Figure 5 and Figure 6). Roughly speaking, theuse of the WaveSight model could provide a 1-2% increase in traffic revenue, simply byreducing the area where interference might occur when a classical model is used instead of themore accurate WaveSight model.

II.) In the second test condition, a maximum of 10 neighbour relations was imposed (insteadof the 12 considered in test I.). Subsequently, frequency plans were found to meet theseparation rules for the two prediction models. However, the frequency plan (CM_FP)

based on the classical model predictions leads to 10 times more bad area than the

frequency plan (WS_FP) based on WaveSight predictions (Table 13, Figure 7 and Figure8).

III.) The aim of the third test condition was to determine the minimum number of carriersneeded to meet all the rules. The maximum number of neighbour relations was kept at 10, butthe minimum overlapping area to consider two cells as neighbours were increased about 10times from 0.01 km2 to 0.122 km2. The result was that with a reduced number of cellneighbours, it is easier for the frequency planning tool to meet rules and stress its works on

reducing the C/I interference level. With both models, only 30 carriers were needed to meetthe frequency planning rules. However, WaveSight gave 5 times less bad area than theclassical model as shown in Table 14.


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The good and bad areas in the Worst interfererand total interfererwere estimated using theWaveSight or Classical Model. Results are shown Figure 13, Figure 14, Figure 15, Figure 16 Figure

18 for the test No. 19 of Table 17.

Table 12 Comparison of results using a separation of (2 2 2 0). A maximum of 12 neighbourrelations using 42 carriers.

Description Using the Classical

Model

Using WaveSight Improvements

Worst interferer BadQuality areas [%]

2.18% 0.67% 1.51% (325 %)relative

Planning cost [$] 300023 19 300006

Figure 5 Comparison of results using a separation of (2 2 2 0). A maximum of 12 neighbour

relations. Percent of bad area-vs-number of available carrier.


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Figure 6Comparison of results using a separation of (2 2 2 0). A maximum of 12 neighbour

relations. Planning cost-vs-number of available carrier.

Classical model vs. WaveSight

0

200000

400000

600000

800000

1000000

1200000

1400000

1600000

1800000

32 34 36 38 40 42 46 47

# of carriers

Planningc

ost

Classical Model

planning cost

WaveSight model

planning cost

Table 13 Comparison of results using a separation of (2 2 2 0). A maximum of 10 neighbourrelations using 39 available carriers.


Model


Worst interferer

Bad Quality areas

2.16% 0.2% 1.96% (1080 %)relative

Planning cost [$] 16 10 6 (160 %) relative


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Figure 7Comparison of results using a separation of (2 2 2 0). A maximum of 10 neighbourrelations. Percent of bad area-vs. -number of available carrier.

Figure 8 Comparison of results using a separation of (2 2 2 0). A maximum of 10 neighbour

relations. Planning cost-vs.-number of available carrier.

Table 14Comparison of results using a separation of (2 2 2 0). A maximum of 10 neighbour

relations using 30 available carriers.


Model


Worst interferer

Bad Quality areas

5.37% 1.04% 4.33% (516 %)relative

Planning cost [$] 103.34 31.33 72.01 (330 %)relative


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7 Conclusion

The impact of using a very accurate prediction model such as WaveSight (WS) instead of aclassical model (CM) has been investigated.

The performances of GSM frequency plans resulting from the two prediction models havebeen compared using a commercially available radio network planning tool: the user-friendlyASSET tool from AIRCOM (www.aircom.co.uk). We compared the performance of the two

prediction models (CM and WS), after frequency planning, according to the total size of areaswhere interference was unacceptable while sufficient carrier power was available forcoverage. This performance measure indicates the amount of lost traffic. Thus, an operatorcan easily translate improvement in this performance measure into revenue gain (for examplea 1% decrease of the area where unacceptable interference occur, means a 1% increase inrevenue if the traffic is uniformly distributed. A non-uniform traffic can increase or decreasethis factor depending on where the interference occur with respect to traffic).

Another performance measure was also used: namely the required number of carrierfrequencies achieving a frequency plan without penalty. This minimum number of carriers

provides a rough estimation of the possible increase in capacity remaining in the network.

The results derived from the two prediction models were compared under the following threeconditions.I.) Firstly, we assume that up to 12 neighbours were allowed, with 42 carriers available.II.) Secondly, we decrease to 10 the number of allowed neighbours in order to ease the

feasibility of the frequency plan.III.) Lastly, we assume that only cells with large overlap (0.122 km2) could be neighbours.

In this case, frequency plans without penalties could be obtained with only 30 carriers(Table 11).

I.) The results of the first test demonstrated that WaveSight could lead to feasible frequencyplans using down to only 40 carriers. With enough carriers, the frequency-planning tool usedhere focused mainly on the interference reduction, which subsequently leads to minimum

penalty cost. Conversely the classical model failed to meet the same rules even with 42carriers. Other tests have shown that at least 47 carriers were needed to produce a feasiblefrequency plan based on the Classical Model based predictions.

II.) The second test showed that WaveSight requires less than 38 carriers to meet the rule,while the Classical Model requires 39 carriers. Moreover, when both models where able toapply the rules, WaveSight reduced interference 10 times less than what the ClassicalModel see Table 16.

III.) In the third test, only 30 carriers were needed for both models, but the Classical Modelgave an interference area that is 5 times larger than when using WaveSight.

Time efficiency, including run time as well as measurement and calibration time, is also animportant factor. The WaveSight computations do not only need little time to carry out the

predictions, but the WaveSight model does not require performing calibrations against

measurements. The resulting savings are worth to be seriously considered.


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WaveSight was able to provide superior network quality and capacity. The accuratepredictions delivered to the radio network-planning tool can be used to reduce the interferenceto levels lower than what is possible with a classical model.


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8 Appendix I

Figure 9 vertical patterns for the omni like antenna

Figure 10 The horizontal pattern for the omni like antenna


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Figure 11 The vertical pattern for the directional antenna (used in macro-cells)

Figure 12 The horizontal pattern for the directional antenna (used in macro-cells)


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9 Appendix II

Table 15 Statistic of the test done where the carriers separation pattern was set at 2 2 2 0, themaximum number of neighbour relations created for each cell was 12, and an overlap of 400

squares is required between neighbour cells.

Test description Test#

# ofcarrier

percellmicro

# ofcarrier

percellmacro

# ofcarrierused

Planningcost$

TrafficlostmErlang

Worstinterferer%of badarea

Totalinterference%of bad area

CM_FP_CM_MDL 1 2 2 32 1603778.51 22,274.70 5.27 6.73CM_FP_WS_MDL 1 2 2 32 1603778.51 22,274.70 6.94 9.04

WS_FP_WS_MDL 1 2 2 32 1101750.57 15302.1 6.80 7.98CM_FP_CM_MDL 2 2 2 34 1200076.73 16,667.70 1.34 1.69CM_FP_WS_MDL 2 2 2 34 1200076.73 16,667.70 4.51 6.23WS_FP_WS_MDL 2 2 2 34 600083.50 8334.5 4.18 5.45CM_FP_CM_MDL 3 2 2 36 1000042.87 13,889.50 0.18 0.22CM_FP_WS_MDL 3 2 2 36 1000042.87 13,889.50 3.82 4.63WS_FP_WS_MDL 3 2 2 36 400059.23 5556.4 3.10 3.56CM_FP_CM_MDL 4 2 2 38 700037.62 9,722.70 0 0.03CM_FP_WS_MDL 4 2 2 38 700037.62 9,722.70 2.91 3.7WS_FP_WS_MDL 4 2 2 38 100044.76 1389.5 1.96 2.39

CM_FP_CM_MDL 5 2 2 40 500029.94 6,944.90 0.15 0.22CM_FP_WS_MDL 5 2 2 40 500029.94 6,944.90 2.11 2.63WS_FP_WS_MDL 5 2 2 40 28.82 0.4 1.06 1.49CM_FP_CM_MDL 6 2 2 42 300023.24 4,166.90 0 0.01CM_FP_WS_MDL 6 2 2 42 300023.24 4,166.90 2.18 2.6WS_FP_WS_MDL 6 2 2 42 18.93 0.26 0.67 0.92CM_FP_CM_MDL 7 2 2 46 100008.77 1,389.00 0 0CM_FP_WS_MDL 7 2 2 46 100008.77 1,389.00 2.08 2.47WS_FP_WS_MDL 7 2 2 46 9.07 0.13 0.15 0.17CM_FP_CM_MDL 8 2 2 47 8.84 0.13 0 0CM_FP_WS_MDL 8 2 2 47 8.84 0.13 1.71 2.07WS_FP_WS_MDL 8 2 2 47 4.98 0.08 0.08 0.09

Where:

CM_FP_CM_MDL Classical Model frequency planning using Classical Model.CM_FP_WS_MDL Classical Model frequency planning using WaveSight.WS_FP_WS_MDL WaveSight frequency planning using WaveSight model.


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Table 16statistic of the test done where the carrier separation pattern was set at 2 2 2 0, the

maximum number of neighbour relations created for each cell was 10, and 400 squares wererequired for neighbour creations.


# ofcarrier

percellmicro

# ofcarrier

percellmacro

# ofcarrierused

Planningcost$

TrafficlostmErlang


Totalinterference%of bad area

CM_FP_CM_MDL 9 2 2 42 3.15 0.04 0 0CM_FP_WS_MDL 9 2 2 42 3.15 0.04 0.78 0.95WS_FP_WS_MDL 9 2 2 42 6.76 0.10 0.14 0.17CM_FP_CM_MDL 10 2 2 40 10.31 0.14 0.05 0.05

CM_FP_WS_MDL 10 2 2 40 10.31 0.14 1.89 1.99WS_FP_WS_MDL 10 2 2 40 11.23 0.16 0.24 0.26CM_FP_CM_MDL 11 2 2 38 100028.17 1,389.28 0.06 0.06CM_FP_WS_MDL 11 2 2 38 100028.17 1,389.28 2.69 3.26WS_FP_WS_MDL 11 2 2 38 11.23 0.16 0.28 0.33CM_FP_CM_MDL 12 2 2 39 16.04 0.22 0.08 0.08CM_FP_WS_MDL 12 2 2 39 16.04 0.22 2.16 2.76WS_FP_WS_MDL 12 2 2 39 10.20 0.14 0.20 0.24

Where:

CM_FP_CM_MDL Classical Model frequency planning using Classical Model.CM_FP_WS_MDL Classical Model frequency planning using WaveSight.WS_FP_WS_MDL WaveSight frequency planning using WaveSight model.


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Table 17statistic of the test done where the carrier separation pattern was set at 2 2 2 0, the

maximum number of neighbour relations created for each cell was 10, and different squareswere required for neighbours to reduce as much as possible the total number of required

carriers .


# ofcarrier

percellmicro

# ofcarrier

percellmacro

# ofcarrierused

# ofsquarerequiredused

Planningcost$

TrafficlostmErlang


Totalinterfer.%of bad

area

CM_FP_CM_MDL 13 2 2 37 1600 100028.6 1389.3 0.08 0.26CM_FP_WS_MDL 13 2 2 37 1600 100028.6 1389.3 2.29 2.85WS_FP_WS_MDL 13 2 2 37 1600 13.2 0.18 0.26 0.31CM_FP_CM_MDL 14 2 2 37 2000 100023.6 1389.2 0.08 0.24CM_FP_WS_MDL 14 2 2 37 2000 100023.6 1389.2 2.27 2.85

WS_FP_WS_MDL 14 2 2 37 2000 11.2 0.15 0.16 0.25CM_FP_CM_MDL 15 2 2 37 2500 100009.8 1389.0 0.01 0.01CM_FP_WS_MDL 15 2 2 37 2500 100009.8 1389.0 2.15 2.45WS_FP_WS_MDL 15 2 2 37 2500 12.2 0.17 0.32 0.36CM_FP_CM_MDL 16 2 2 37 3000 100014.5 1389.1 0.02 0.02CM_FP_WS_MDL 16 2 2 37 3000 100014.5 1389.1 1.54 1.88WS_FP_WS_MDL 16 2 2 37 3000 12.7 0.18 0.28 0.31CM_FP_CM_MDL 17 2 2 37 3600 100011.4 1389.5 0.02 0.05CM_FP_WS_MDL 17 2 2 37 3600 100011.4 1389.5 1.53 2.22WS_FP_WS_MDL 17 2 2 37 3600 11.3 0.16 0.25 0.29CM_FP_CM_MDL 18 2 2 37 4900 7.02 0.1 0 0

CM_FP_WS_MDL 18 2 2 37 4900 7.02 0.1 2.09 2.27WS_FP_WS_MDL 18 2 2 37 4900 10.1 0.14 0.27 0.31CM_FP_CM_MDL 19 2 2 30 4900 103.34 1.44 1.12 2.02CM_FP_WS_MDL 19 2 2 30 4900 103.34 1.44 5.37 7.79WS_FP_WS_MDL 19 2 2 30 4900 35.33 0.49 1.29 1.66

Where:

CM_FP_CM_MDL Classical Model frequency planning using Classical Model.CM_FP_WS_MDL Classical Model frequency planning using WaveSight.

WS_FP_WS_MDL WaveSight frequency planning using WaveSight model.


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10 Appendix III

Figure 13 The worst interferer when using WaveSight for planning in test number 19, 1.4% ofbad area


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Figure 15 The worst interferer when using the classical model for planning in test number 19,5.37% of bad area


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Figure 16The total interference when using WaveSight for planning in test number 19, 1.48%

of bad area


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Figure 18 The total interference when using the classical model for planning in test number19, 7.79% of bad area

gsm case study v1 7

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