GSM Case Study v1 7

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WaveSight’s impact on frequency planning: The added value of using a realistic prediction model instead of a classical model Odeh GHAWI

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1

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

2

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

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INTRODUCTIO INTRODUCTION N ...................... ................................. ...................... ...................... ...................... ...................... ...................... ..................... .......... 7

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DESCRIPTION DESCRIPTION OF THE TEST CONFIGURAT CONFIGURATION ION ..................... ............................... ..................... ............... .... 9

4.1

Sites ............................................. ..................................................................... ............................................... .............................................. ...................................... ...............9 9

4.2

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

4.3

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

4.4

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

5

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

5.1

Methodology.............................................................. Methodology....................................... .............................................. .............................................. ............................. ...... 11

5.2

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

5.3

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

5.4

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

5.5 Parameter settings for the prediction models ...................................... .......................................................... ...................... .. 12 5.5.1 WaveSight................................................................................................................12 5.5.2 Macro-cell classical model............................... model..................................................... ............................................. .................................. ...........13 13 5.5.3 Micro-cell classical model ......................................... ............................................................... ........................................... ........................ ... 13 5.5.4 Comparison of model run times.................... times ........................................... .............................................. .................................... .............14 14 5.6

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

5.7

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

5.8

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

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

6.1

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

6.2

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

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CONCLUSION................ CONCLUSION............................ ....................... ....................... ....................... ....................... ....................... ...................... ............... .... 25

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

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APPENDIX APPENDIX II......................... II..................................... ........................ ....................... ....................... ........................ ....................... ................... ........ 29 APPENDIX APPENDIX III......................... III..................................... ....................... ....................... ........................ ....................... ....................... ................ .... 32

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1 Glossary of terms BCCH C/I DTM DTX ILSA TCH TRX

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Broadcast Control Channel – in this context referring to the entire carrier containing the BCCH Carrier to interference Digital Terrain Model Discontinuous Transmission Intelligent Local Search Algorithm Traffic Channel – in this context referring to carriers not containing the BCCH Transmitter/Receiver

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wavecall Company information Address

Wavecall SA Science Park Swiss Institute of Technology PSE-B / EPFL 1015 Lausanne

Phone Fax

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

Contact

Odeh Ghawi

E-Mail

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

Web

Document history Version 1.0 2.0 3.0 4.0 5.0 6.0 7.0

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Revision O.Ghawi (Quality Manager) O.Ghawi (Quality Manager) O.Ghawi (Quality Manager) O.Ghawi (Quality Manager) O.Ghawi (Quality Manager) O.Ghawi (Quality Manager) J.-F. Wagen (Consultant)

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Date October, 10 2000 November, 8 2000 November, 28 2000 December, 8 2000 December, 18 2000 January, 9 2001 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 model reduces 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 to compare frequency plans obtained by using 1. A classical propagation model and 2. 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 Paris comprising 17 sites (36 cells). All data except the predictions have been kindly provided by the 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 where interference is unacceptable can be reduced by 80%. This reduction could be 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 radio network. In the case investigated here, it was possible to reduce from 47 to 40 the number of necessary carrier. This is significant not only because it can reduce the cost of finetuning 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 to minimise the impact on the existing base stations in order to save cost. This study was conducted to evaluate the impact the advanced propagation prediction model WaveSight 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, which includes 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 data and 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 been generated using the commercially available frequency-planning tool ILSA integrated in the excellent AIRCOM’s 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 predicted by the different models, classical (CM) and WaveSight (WS). This investigations allows to determine the areas where the quality is considered to be acceptable using the classical prediction model (CM) while the performance predicted by the accuracy of WaveSight model (WS) are not sufficient. The results can also be used to determine 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 is unacceptable and such that a new design or changes in the parameters of the radio network would have to be implemented. In this case the implementation changes would 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 than save a few TRX equipments, this investigation is mainly of interest in green field situations or when elaborating bids. However, there are so many possible ways of increasing the capacity (adding TRX, adding sectors, adding sites, …), that it is out of the scope of this case study to investigate the increase of capacity resulting from the use of the accurate WaveSight model. Since the saved carrier frequencies could be used 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 (usually confidential) traffic data. Engineers at Wavecall would be happy to assist any operators 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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wavecall 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 the software 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 provided in 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, a district of Paris, France. The base stations and antenna configuration were kindly provided by Bouygues Telecom (www.bouygtel.com (www.bouygtel.com), ), a French GSM mobile operator, parts of this document cannot be reproduced or extracted without contacting Wavecall ([email protected]).

4.1 Sites The 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-cell” sites) and in Table 2 (for the “macro-cell” sites). Table 1 The 7 micro-cell sites Site

X

Y

Height (m)

Site1 Site2 Site3 Site4 Site5 Site6 Site7

600608 600354 600316 600197 599482 600264 600698

2428238 2427795 2428333 2427946 2428168 2428341 2428324

6 6 7 5 5 5 5

Power (dBm) 36 36 36 36 36 36 36

Downtilt

Power (dBm) 53 53 53 53 53 53 53 53 53 51

Downtilt

0 0 0 0 0 0 0

Table 2 The 10 macro-cell sites Site

X

Y

Height (m)

Site8 Site9 Site10 Site11 Site12 Site13 Site14 Site15 Site16 Site17

598592 598 592 599143 599 143 599143 600755 599502 599966 599852 599298 600657 598989

2428336 2427469 2427933 2427738 2427681 2428275 2429455 2428658 2429096 2429016

36 32 36 26 34 32 30 32 35 31

0 0 0 0 0 0 0 0 0 0

4.2 Cell configurations The macro-cell sites consist usually of 3 sector antennas oriented respectively on 0°, 120° and 240° 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-called Bouyguemicrocell omnidirectonal antenna for the micro-cells, and the directional PCN_S_085_19_5 antenna for the macro-cells. The vertical and horizontal patterns for the two antennas are shown in Figure 9, Figure 10, Figure 11 and Figure 12 in Appendix I.

4.4 Description of tools and models The user-friendly ASSET software, from the AIRCOM’s ENTERPRISE suit, was used as the engineering tool to set the radio network configuration, run the prediction models, display the results 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 another one for macro-cells. The macro-cell model is based on the ETSI Hata model, and the microcell 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 study intend to compare generic Classical Model to the WaveSight model we did not used all the possibilities provided by the AIRCOM’s tool to optimise the calibration of the classical model. This calibration that has been performed is described in the next section. The calibration used has not been completely optimised but it provides a very good idea of what could be obtained in the radio planning of changes in antenna orientation or down-tilt, or of a new 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 calibration used in this study is claimed to be sufficient. Furthermore, since it is easier and more cost efficient to optimise a well-planned network and since, it is obviously not possible to take measurements from base-stations not yet deployed, a perfect calibration of any Classical Model is not trivial. The WaveSight model (WS) does not require any calibration. The WaveSight model is a fully deterministic model based on an efficient implementation of real ray-tracing algorithms. The same basic principle and the same basic algorithms are used for both macro-cells and microcells. The WaveSight model uses ground height, building outlines and building height to calculate 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 AIRCOM’s ENTERPRISE suit. More details about the frequency planning algorithms are not provided here but are available directly from AIRCOM (www.aircom.co.uk).

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5 Description of the comparisons comparisons 5.1 Methodology To evaluate the end effects resulting from the use of two different prediction models, the following method has been used. The evaluation should include all the major steps involved in the 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 frequency channel constraints, generation of a frequency plan and final evaluation of the performance of this frequency plan. Comparisons are made between the results obtained when using a classical coverage prediction model (CM) and when using the more accurate WaveSight model (WS). The steps are then as follows: 1. Generate a frequency plan (CM_FP) using the AIRCOM’s ILSA software based on the predictions computed with the classical prediction models. 2. 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 Appendix). 3. Generate a frequency plan (WS_FP) using the AIRCOM’s ILSA software based on the predictions computed with the WaveSight model. 4. 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 Appendix). 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 realistic WaveSight model instead of a classical model. Other performance measures quantifying the accuracy of the predictions compared to measurements are not the focus in this study. Therefore, metrics like standard deviation, mean error values, hit-rates, … are not used here. However, such results are available from http://www.wavecall.com/prediction.html.. http://www.wavecall.com/prediction.html

5.2 Neighbour relations The valuable “Neighbour wizard” of the AIRCOM’s Asset software tool was used to generate the neighbours’ relation between cells. Since neither the same carrier nor adjacent carriers could be attributed to neighbouring cells, the number of neighbouring cells affects the frequency 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 interference and not to work at rearranging and/or rejecting frequencies to satisfy neighbours’ relationships. Furthermore, the neighbours’ relationships are usually not very sensitive to the accuracy of the prediction tools. Thus, we have adjusted the neighbours’ relationship, the

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wavecall overlap area factor in particular, in a manner such that the frequency-planning tool could allocate all carriers. The following other parameters were used: •

•

The maximum number of neighbours was set to 12 including co-site cells. A maximum 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 as given in Table 3. Table 3: Frequency numbering in the three bands.

45-56 12 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 BCCH layer in each cell. Additional carriers, if any, belong to the TCH layer.

5.4 Additional frequency planning planning tool configurations •

• • •

The prediction for each cell was performed perfo rmed in an area with wit h a 3 km radius for the macro-cell configuration confi guration and a 1 km radius for the micro-cell configuration with 5 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 a major drawback, the goal of this study is to investigate the overall effect of using a very accurate prediction model. Thus, assuming a uniform traffic simplifies the analysis without a great loss of generality.

5.5 Parameter settings for the prediction models models All prediction models require at least two parameters: the overall frequency band, taken here at 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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wavecall 5.5.2 Macro-cell classical model In a macro-cell configuration, the classical model (CM) is taken as the ETSI Hata model defined 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 lowest standard 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 relative height 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 AIRCOM’s ASSET software, the classical model could be 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 when evaluated over the 10 sites, with a standard deviation of 3.6 dB among the results for each sites. The mean error was 0 dB when averaged over all sites. The mean error computed over each site has a standard deviation of 8 dB. More details are available upon request from the author or from our web site www.wavecall.com 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 167.15

K2 35

K3 -2.55

K4 0

K5 -13.82

K6 0

K7 0.8

An even better calibration might have been obtained from the ASSET software, for example a calibration per site could have been performed. However our goal was to obtain a standard deviation of about 10 dB to simulate the widely accepted performance of a classical model in non 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 buildings outlines and a 5 meters resolution digital terrain model (DTM) height. As the area under investigation is flat, the DTM was not needed. The Micro-cell classical model employs two different algorithms whether the mobile is in line-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 are given in Table 5 and Table 6, respectively.

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wavecall On the two sites where measurements were available for calibration, the prediction mean error was of 6 dB with a standard deviation of 10 dB. Table 5 LOS parameters

Loss at 1 m (dB) 27

Near Slope (dB/decade) 9.2

Far Slope (dB/decade) 45

Ant. Ht. Gain

Breakpoint

8.2

Calculated automatically

Table 6 Non-LOS parameters

Forward scatter. Near slope (dB/decade)

Forward scatter. Far slope (dB/decade)

Back scatter. Near slope (dB/decade)

Back scatter .Far slope (dB/decade)

9.2

20

0

18.7

Break point

0

Highest order virtual source 2

Max distance to diffracting edge 6

Again, the calibration used here might have been further optimised, especially if more measurements had been available. However, the parameters used here are believed to represent 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. It corresponds 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 256 MB RAM. Table 7: Mean computation time for different configurations

Micro-cell WaveSight model Classical model (Pseudo-Ray-Tracing) 6 min 150 min

Macro-cell WaveSight model Classical model (Okamura-Hata) 25 min 8 min

It is worth noting that in micro-cellular environments, in which computation time is very important, WaveSight is 25 times faster than the classical model. In macro-cellular environments, the classical model is approximately 3 times faster. However, the classical macro-cell model does not take into account building database and thus its prediction accuracy 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 (lowest numerical value) and the total C/I (C/Total_I) for each carrier on each “pixel” representing

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wavecall here a 5 m x 5 m areas in the prediction grid. All the co- and adjacent-carriers from all interfering cells are taken into account. The “per carrier interference array” is an array such that for each pixel we have a list of serving carriers plus the worst and total C/I for each carrier. 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 WaveSight model (WS) are shown in Figure 2, and Figure 3, respectively. The parameters considered to create the interference array are shown in Table 8 and are explained below: Minimum service level : is the minimum service level at which a cell is considered to be a serving 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 and reception. 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 to interference value (C/I) to obtain the adjacent channel C/A value. C/A = C/I + Adjacent channel offset. –18 dB is a typical value. Table 8: Parameters used to create the interference array

Minimum service level (dBm) -104

Maximum timing advance 63

Adjacent channel offset [dB] -18

No traffic data (i.e., a uniform traffic is considered), no Frequency hopping and no downlink DTX were used. In this study, both the Worst interferer and the Total interference criteria were considered for comparisons 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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wavecall Figure 2 The best server coverage for the WaveSight prediction. The improved accuracy is easily 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 table The interference table was created using the AIRCOM’s ASSET planning tool. The interference table describes the interference that would result if any two cells were allocated the same or adjacent carriers. The AIRCOM’s frequency-planning tool (ILSA) uses this interference table to calculate the overall level of interference that a given frequency plans would 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 was appreciated. We used the parameters shown in Table 9. Table 9: Parameters used to create the interference table

Minimum service level dBm -104

Maximum timing advance micro sec 63

5.8 Frequency planning using ILSA The AIRCOM’s frequency planning software ILSA uses a powerful iterative algorithm to generate and improve a frequency plan. The quality of the result is guided by a penalty system of non-respected rules. By appropriately choosing the penalties, the user can guide the software 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 preexisting restriction) • •

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wavecall •

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

The so-called separation rules that dictate the separation between two carriers are defined by an integer. For example, a separation of 0 means that the carriers could be the same. A separation of 1 means that the two carriers can be adjacent. Separation numbers for different cases are shown in Table 10. Table 10 Frequencies separation rules used in this study.

Carrier Layer Layer BCCH TCH

•

•

Co-Cell 2 2

Co-site 2 2

Neighbour 2 2

2nd neighbour 0 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 cost below 50000$ would indicate that ILSA tool places carriers according to the rules and works only on reducing the interference.

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

Carrier Layer Layer

Cell Cost

Site Cost

BCCH TCH

100000 100000

75000 75000

•

Neighbour Cost 50000 50000

Weight 1 1

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

A total of 19 tests were performed.

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Figure 4 For information here is the print screen of a graph given by ILSA, plots the iteration number 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 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 the penalty cost of Table 11). Traffic lost (mErlang. In this investigation, the traffic lost is directly proportional to the 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 socalled worst C/I . The worst C/I is meaningful since frequency hopping was not considered here. Total interference: interference: For each 5 m x 5 m area pixel, the total interference array gives the ratio of the carrier signal strength within the pixel to the power sum of the interfering signal strength generated by other sites. The C/I calculated in the total interference array is not experienced 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 AIRCOM’s 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 AIRCOM’s 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 16 and Table 17 .

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wavecall 6.2 Analysis of the results We compare the performance of the prediction models, after frequency planning, according to the total size of areas where interference are unacceptable while sufficient carrier power is available 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 frequencies 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. Indeed, a frequency plan that lowers the resulting interference allows the planner to “free” some frequencies 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 two prediction models, the classical (CM) and the WaveSight (WS) models. Various interference environments were considered to obtain results under different test conditions. Three different tests are documented here. The tests were generated by changing the number of neighbour relations 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.) 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 classical model, no frequency plan could be found to meet the “Co-Cell, Co-site, Neighbour, 2 nd neighbour ” separation rules of (2, 2, 2, 0 ) ( Table 10) when using the 42 available carriers. In order to guess how many frequencies were needed to comply with the rules, more carriers were 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 on the WaveSight predictions used only 40 carriers. Furthermore, Table 12 shows that the classical model leads to 1.63% more bad areas than when using the WaveSight model. (See also Figure 5 and Figure 6). Roughly speaking, the use of the WaveSight model could provide a 1-2% increase in traffic revenue, simply by reducing the area where interference might occur when a classical model is used instead of the more accurate WaveSight model. II.) In the second test condition, a maximum of 10 neighbour relations was imposed (instead of the 12 considered in test I.). Subsequently, frequency plans were found to meet the separation 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 Figure 8). III.) The aim of the third test condition was to determine the minimum number of carriers needed to meet all the rules. The maximum number of neighbour relations was kept at 10, but the minimum overlapping area to consider two cells as neighbours were increased about 10 times from 0.01 km2 to 0.122 km2. The result was that with a reduced number of cell neighbours, 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 meet the frequency planning rules. However, WaveSight gave 5 times less bad area than the classical model as shown in Table 14.

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wavecall The good and bad areas in the Worst interferer and total interferer were estimated using the WaveSight WaveSight 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 neighbour relations using 42 carriers. Description

Worst interferer Bad Quality areas [%] Planning cost [$]

Using the Classical Model 2.18%

Using WaveSight

Improvements Improvements

0.67%

300023

19

1.51% (325 %) relative 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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wavecall Figure 6 Comparison 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 1800000 1600000 t 1400000 s o 1200000 c g 1000000 n i 800000 n n a 600000 l P 400000 200000 0

Classical Model planning cost WaveSight model planning cost

3 2 3 4 3 6 3 8 4 0 42 4 6 4 7 # of carriers

Table 13 Comparison of results using a separation of (2 2 2 0). A maximum of 10 neighbour relations using 39 available carriers. Description Worst interferer Bad Quality areas Planning cost [$]

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Using WaveSight

0.2%

16

10

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1.96% (1080 %) relative 6 (160 %) relative

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wavecall Figure 7 Comparison of results using a separation of (2 2 2 0). A maximum of 10 neighbour relations. 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 14 Comparison of results using a separation of (2 2 2 0). A maximum of 10 neighbour relations using 30 available carriers. Description Worst interferer Bad Quality areas Planning cost [$]

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Using WaveSight

1.04%

103.34

31.33

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4.33% relative 72.01 relative

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7 Conclusion The impact of using a very accurate prediction model such as WaveSight (WS) instead of a classical model (CM) has been investigated. The performances of GSM frequency plans resulting from the two prediction models have been compared using a commercially available radio network planning tool: the user-friendly ASSET 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 areas where interference was unacceptable while sufficient carrier power was available for coverage. This performance measure indicates the amount of lost traffic. Thus, an operator can easily translate improvement in this performance measure into revenue gain (for example a 1% decrease of the area where unacceptable interference occur, means a 1% increase in revenue if the traffic is uniformly distributed. A non-uniform traffic can increase or decrease this factor depending on where the interference occur with respect to traffic). Another performance measure was also used: namely the required number of carrier frequencies 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 three conditions. 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 frequency plans using down to only 40 carriers. With enough carriers, the frequency-planning tool used here 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 42 carriers. Other tests have shown that at least 47 carriers were needed to produce a feasible frequency 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 to apply the rules, WaveSight reduced interference 10 times less than what the Classical Model see Table 16 . III.) In the third test, only 30 carriers were needed for both models, but the Classical Model gave 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 an important 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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wavecall WaveSight was able to provide superior network quality and capacity. The accurate predictions delivered to the radio network-planning tool can be used to reduce the interference to 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, the maximum number of neighbour relations created for each cell was 12, and an overlap of 400 squares is required between neighbour cells.

Test description

CM_FP_CM_MDL CM_FP_WS_MDL WS_FP_WS_MDL CM_FP_CM_MDL CM_FP_WS_MDL WS_FP_WS_MDL CM_FP_CM_MDL CM_FP_WS_MDL WS_FP_WS_MDL CM_FP_CM_MDL CM_FP_WS_MDL WS_FP_WS_MDL CM_FP_CM_MDL CM_FP_WS_MDL WS_FP_WS_MDL CM_FP_CM_MDL CM_FP_WS_MDL WS_FP_WS_MDL CM_FP_CM_MDL CM_FP_WS_MDL WS_FP_WS_MDL CM_FP_CM_MDL CM_FP_WS_MDL WS_FP_WS_MDL

Test # of # carrier per cell micro 1 2 1 2 1 2 2 2 2 2 2 2 3 2 3 2 3 2 4 2 4 2 4 2 5 2 5 2 5 2 6 2 6 2 6 2 7 2 7 2 7 2 8 2 8 2 8 2

# of carrier per cell macro 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

# of Planning carrier cost$ used

Traffic lost mErlang

32 32 32 34 34 34 36 36 36 38 38 38 40 40 40 42 42 42 46 46 46 47 47 47

22,27 22,274. 4.70 70 22,27 22,274. 4.70 70 1530 15302.1 2.1 16,66 16,667. 7.70 70 16,66 16,667. 7.70 70 8334.5 13,88 13,889. 9.50 50 13,88 13,889. 9.50 50 5556.4 9,722.70 9,722.70 1389.5 6,944.90 6,944.90 0.4 4,166.90 4,166.90 0.26 1,389.00 1,389.00 0.13 0.13 0.13 0.08

1603 160377 778. 8.51 51 1603 160377 778. 8.51 51 1101 110175 750. 0.57 57 1200 120007 076. 6.73 73 1200 120007 076. 6.73 73 600083.50 1000 100004 042. 2.87 87 1000 100004 042. 2.87 87 400059.23 700037.62 700037.62 100044.76 500029.94 500029.94 28.82 300023.24 300023.24 18.93 100008.77 100008.77 9.07 8.84 8.84 4.98

Worst interferer % of bad area 5.27 5.27 6.94 6.94 6.80 6.80 1.34 1.34 4.51 4.51 4.18 0.18 0.18 3.82 3.82 3.10 0 2.91 1.96 0.15 2.11 1.06 0 2.18 0.67 0 2.08 0.15 0 1.71 0.08

Total interference % of bad area

6.73 6.73 9.04 9.04 7.98 7.98 1.69 1.69 6.23 6.23 5.45 0.22 0.22 4.63 4.63 3.56 0.03 3.7 2.39 0.22 2.63 1.49 0.01 2.6 0.92 0 2.47 0.17 0 2.07 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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wavecall Table 16 statistic 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 were required for neighbour creations.

Test description

CM_FP_CM_MDL CM_FP_WS_MDL WS_FP_WS_MDL CM_FP_CM_MDL CM_FP_WS_MDL WS_FP_WS_MDL CM_FP_CM_MDL CM_FP_WS_MDL WS_FP_WS_MDL CM_FP_CM_MDL CM_FP_WS_MDL WS_FP_WS_MDL

Test # of # carrier per cell micro 2 9 2 9 2 9 10 2 2 10 2 10 2 11 2 11 2 11 2 12 2 12 2 12

# of carrier per cell macro 2 2 2 2 2 2 2 2 2 2 2 2

# of Planning carrier cost$ used

42 42 42 40 40 40 38 38 38 39 39 39

Traffic lost mErlang

Worst interferer % of bad area 3.15 0.04 0 3.15 0.04 0.78 6.76 0.10 0.14 10.31 0.14 0.05 10.31 0.14 1.89 11.23 0.16 0.24 1000 100028 28.1 .17 7 1,389 1,389.2 .28 8 0.06 0.06 1000 100028 28.1 .17 7 1,389 1,389.2 .28 8 2.69 2.69 11.23 0.16 0.28 16.04 0.22 0.08 16.04 0.22 2.16 10.20 0.14 0.20

Total interference % of bad area

0 0.95 0.17 0.05 1.99 0.26 0.06 0.06 3.26 3.26 0.33 0.08 2.76 0.24


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wavecall Table 17 statistic 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 squares were required for neighbours to reduce as much as possible the total number of required carriers .

Test description

CM_FP_CM_MDL CM_FP_WS_MDL WS_FP_WS_MDL CM_FP_CM_MDL CM_FP_WS_MDL WS_FP_WS_MDL CM_FP_CM_MDL CM_FP_WS_MDL WS_FP_WS_MDL CM_FP_CM_MDL CM_FP_WS_MDL WS_FP_WS_MDL CM_FP_CM_MDL CM_FP_WS_MDL WS_FP_WS_MDL CM_FP_CM_MDL CM_FP_WS_MDL WS_FP_WS_MDL CM_FP_CM_MDL CM_FP_WS_MDL WS_FP_WS_MDL

Test # of # carrier per cell micro 13 2 13 2 13 2 14 2 14 2 14 2 15 2 15 2 15 2 16 2 16 2 16 2 17 2 17 2 17 2 18 2 18 2 18 2 19 2 19 2 19 2

# of carrier per cell macro 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

# of # of Planning carrier square cost$ used required used

37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 30 30 30

1600 1600 1600 1600 1600 2000 2000 2000 2000 2000 2500 2500 2500 2500 2500 3000 3000 3000 3000 3000 3600 3600 3600 3600 3600 4900 4900 4900 4900 4900 4900

1000 100028 28.6 .6 1000 100028 28.6 .6 13.2 1000 100023 23.6 .6 1000 100023 23.6 .6 11.2 1000 100009 09.8 .8 1000 100009 09.8 .8 12.2 1000 100014 14.5 .5 1000 100014 14.5 .5 12.7 1000 100011 11.4 .4 1000 100011 11.4 .4 11.3 7.02 7.02 10.1 103.34 103.34 35.33

Traffic Worst lost interferer mErlang % of bad area 1389 1389.3 .3 0.08 0.08 1389 1389.3 .3 2.29 2.29 0.18 0.26 1389 1389.2 .2 0.08 0.08 1389 1389.2 .2 2.27 2.27 0.15 0.16 1389 1389.0 .0 0.01 0.01 1389 1389.0 .0 2.15 2.15 0.17 0.32 1389 1389.1 .1 0.02 0.02 1389 1389.1 .1 1.54 1.54 0.18 0.28 1389 1389.5 .5 0.02 0.02 1389 1389.5 .5 1.53 1.53 0.16 0.25 0.1 0 0.1 2.09 0.14 0.27 1.44 1.12 1.44 5.37 0.49 1.29

Total interfer. % of bad area 0.26 0.26 2.85 2.85 0.31 0.24 0.24 2.85 2.85 0.25 0.01 0.01 2.45 2.45 0.36 0.02 0.02 1.88 1.88 0.31 0.05 0.05 2.22 2.22 0.29 0 2.27 0.31 2.02 7.79 1.66


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10 Appendix III

Figure 13 The worst interferer when using WaveSight for planning in test number 19, 1.4% of bad area <12dB was obtained.

Figure 14 worst interferer patterns.

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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 <12dB was obtained.

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Figure 16 The total interference when using WaveSight for planning in test number 19, 1.48% of bad area <12dB was obtained.

Figure 17 total interference patterns

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Figure 18 The total interference when using the classical model for planning in test number 19, 7.79% of bad area <12dB was obtained.

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GSM Case Study v1 7

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