SPM Atoll to PGM model.pdf

1.1.1

How to “approximate” Atoll’s Standard Propagation Models

1.1.1.1

Methodology

 Atoll’s Standard Propagation Propagation Models can be approximated in Mentum Mentum Planet using the Planet General Model. Although perfect replication of the models are not always possible, due to some differences in terms of propagation modelling, predictions have proved to be s imilar with both propagation models. Here are the steps to be followed to create a “PGM” model, based on a given “Standard Propagation Model”: -

Create a new PGM model.

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Set the same frequency as the one defined in the “Standard “Standa rd Propagation Model”.

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Set the same Rx height as as the one defined in the “Standard Propagation Model”.

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K1 and K2 factors: factors: Atoll models use separate separate K1 K1 and K2 factors factors for the the near near the transmitter and far from the transmitted; a distance threshold needs to be input to define when the “near factors” should be used, and where the “far factors” factors” should be used. This can be modelled with the Planet General Model as well. In the Planet General Model parameters, parameters, select the “2 Piece” type, and input the distance that was defined in the SPM model. The SPM models also has the ability to specify different K1 and K2 factors for Line of Sight and Non Line of Sight areas (for both “near” and “far” distances”); the PGM model does not offer such capability; it is hence recommended to use the NLOS K1 and K2 factors, as it will help approximate the Standard Propagation Model where it matters more (i.e. Non Line of Sight areas).

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The K3 value defined in the SPM model model should should be be input as “-K3” “-K3” in the PGM model (for instance, 5.83 should be input as -5.83).

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K4 factors are equivalent equivalen t in the SPM and the PGM models.

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The K3 factor defined in the SPM model should be input as “-K5” in the PGM model (for instance, -6.55 should be input as 6.55).

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K6 factors are equivalent equivalen t in the SPM and the PGM models.

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The “Kclutter” “Kclutter” factor factor is used used to to multiply the per-clutter per-clutter losses, losses, called called CAL (Clutter (Clutter  Absorption Loss) v alues in Mentum Mentum Planet. If “Kclutter” is different from “1”, then in the PGM parameters, in the “Path Clutter” tab, check the “enable path clutter” option, and: o

Set the distance as defined in the “Maximum distance” cell in the SPM SPM model. If equal to 0, only the loss of the clutter class of the receiving Bin will be accounted for.

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Set the “Function coefficient” to the “Kclutter” values defined in the SPM model. Set the “Function” based on the option selected in the SPM model (Uniform in the SPM model would be “rectangular” in the Planet General Model for instance; this option gives the same weighting for all the clutter losses; the other options in the Planet General Model are “triangular”, “exponential” and “logarithmic”).

Figure 19: Planet General Model – Path Clutter tab

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If the “Hilly terrain correction” is used (i.e. if it does not say “0 – No” in the SPM model), which was not the case in the Vancouver (and hence the Calgary and Edmonton) models, further investigation of the SPM model would be needed to analyse which correction factors should be enabled in the Planet General Mo del. It is understood that this setting only impacts the predictions generated by the SPM model for the Bins/pixels that are in line of sight. For more information on the various correction factors in the PGM model, please refer to the Planet General Model technical n otes.

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Predictions generated by the SPM model can be “radial-based” or “Bin-based”; in Mentum Planet, all the predictions are “radial-based”, because predictions with “Binbased” methods are much slower, and studies have shown that radial-based predictions are as accurate as Bin-based predictions; more information on this can be provided if needed.

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The grid calculations can be “centred”, in which case the predictions are done at the centre of the Bins, or “bottom – left”, in which case the predictions are done for the bottom left of the Bins, with the SPM model. With the PGM model, the predictions are always gene rated at the centre of the Bins.

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If the “Consider heights in diffraction” option is set to “0 – No” in the SPM model (which was the case for the models in Vancouver”, then set all the clutter heights to 0m in the Planet General Model. If the “Consider heights in diffraction” option is not set to “0 – No”, then assign the same c lutter heights as the ones defined in the SPM model (clutter tab). The “clearance” values defined in the SPM model are equivalent to the “clutter separation value” defined in Planet”.

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With regards to the “Effective Antenna Height” options, the SPM and the PGM models both offer various methodologies; after running multiple tests, the “ground reflection slope” methodology seems to give better approximations of the SPM predictions, regardless of the option selected in the SPM model. This w as verified for the both models in Vancouver, where the SPM dense urban model used the “Height above average profile” option, while the suburban model was assigned the “enhanced slope at receiver” option.

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As mentioned earlier in the document, the PGM model uses a modified version of the Epstein-Peterson algorithm to compute diffraction calculations; the SPM model offers four choices (Epstein-Peterson, Deygout, Deygout with correction and Millington). When approximating the SPM models in Planet, some differences may be noticed. The differences will vary depending on the environments.

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If the “Receiver on top of clutter” option is enabled (i.e. the parameter is not set to “0  – No”), the calculations (in Atoll) will assume that the receiver height is the sum of the clutter height and the receiver height defined in the receiver tab (e.g. 20 + 1.5 = 21.5m). If this option is enabled, separate receiver heights should be set in the PGM’s “.cpa” file. The receiver heights should be equal to the clutter heights the receiver height defined in the SPM model (e.g. 20 + 1.5 = 21.5m).

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Limitation to free space loss: this is a feature offered by the SPM model (whereby the pathloss will never be smaller than the free space loss) that is not available in the PGM model.

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In the SPM model, it is also possible to set different receiver heights for different clutter classes; if this is the case, the same values should be input in the receiver height column of the PGM’s “.cpa” file.

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1.1.1.2

“SPM” model approximation - verification

The verification of the approximation of the SPM models was done based on statistics, so only s tatistical comparisons could be performed:

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The statistics between the measurements and the approximated predictions in Planet were computed; these statistics were compared against the statistics shown in the model tuning report coming from Atoll. As shown on the table below, the statistics between the measurements and the SPM model on the one hand, and the statistics between the measurements and the approximated SPM model on the other hand are almost identical for the dense urban model; for the suburban model, the difference seems to be a bit more significant. In both cases, these statistics suggest a good approximation of the SPM models in Mentum Planet.

Calibration Survey Broughton Georgia W Georgia O Global

Measurements Environment

 

Farrow David Gray Quality Inn Global

Validation Survey

 

SPM-Atoll

SPM-Approximated

Mean

STDEV

Mean

STDEV

13272

DU

1.5

5.5

1.2

5.7

5770

DU

-1.9

6.4

-1.2

6.3

3783

DU

-2.5

6.7

-1.9

7.0

22825

DU

0.0

5.9

0.1

6.0

16225

SU

0.3

6.0

0.1

7.2

8648

SU

-2.5

4.4

-2.9

6.2

21347

SU

0.4

6.9

-1.9

7.7

46220

SU

-0.2

6.1

-1.4

7.2

Measurements

Environment

SPM-Atoll

SPM-Approximated

Mean

STDEV

Mean

STDEV

Best Western

12592

DU

0.6

5.8

-1.0

6.5

Johnson

20406

SU

0.6

4.9

-0.9

4.9

Table 5: SPM and SPM-Approximated predictions compared against measurements

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The predicted values of the SPM models were given to Mentum, for the Bins with measurements only, and the approximated predictions in Planet were compared against the predicted values in Atoll. Again, these statistics suggest a really good approximation of the SPM models.

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"SPM Predictions" - "PGM-Atoll Predictions"

Calibration Survey

Measurements Environment

Mean

STDEV

Correlation

Broughton Georgia W

13272

DU

-0.2

2.6

0.97

5770

DU

0.6

1.9

0.99

Georgia O Global

3783

DU

0.5

1.8

0.99

22825

DU

0.2

2.3

0.98

Farrow David Gray Quality Inn

16225

SU

-0.2

3.2

0.96

8648

SU

-0.4

3.6

0.98

21347

SU

-2.3

3.1

0.97

Global

46220

SU

-1.2

3.2

0.97

Measurements

Environment

Mean

STDEV

Validation Survey

Correlation

Best Western 12592 DU -1.6 2.9 0.97 Johnson 20406 SPM predictions SU and Approximated -1.5 2.6predictions 0.97 Table 6: Difference between SPM

Here is also the distribution (pdf and cdf) of the difference between the predictions generated with the SPM m odel in Atoll, and the predictions generated in Mentum plane t with the approximated SPM model, for the Bins that had measurements, like the two previous tables, this suggests a really good replication of the SPM models, as the difference observed are quite small when co mpared to the accuracy of the models:

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Figure 20: Distribution of the difference between SPM and SPM-Approximated predictions

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