By default, Overture assumes that signal propagation is modulated only by the parameters of the prediction model and optional terrain diffraction and shadowing. However, if the user supplies extra data about the local environment, it can be used to refine the predictions.

This type of data is usually referred to as morphology or clutter, and classifies geographic areas into categories such as "Urban" or "Woodlands". Overture can use any Categorical data source, including rasters and polygons.

Demonstration Data

The built-in tutorial data for Ann Arbor contains population density data that we can use as the basis for a demonstration. The Census Polygon data was imported as shown below

Then a new Density Ranges component was added to the Categorical geodata list with the following ranges:

Category	Color	Lower Bound (km⁻²)
Rural	Green	0
Suburban	Yellow	500
Urban	Red	5000

Creating a categorical map like this:

Then a single sector was added with an isotropic-omnidirectional antenna (to eliminate pattern artifacts) using the default COST231 model:

The radiation pattern is uniform and unobstructed as the prediction model was not yet considering clutter or terrain.

Clutter Passthrough Adjustment

The prediction model property Cateogries was set to point at the new clutter information as shown below

The Category Unit Size property is used to calculate how passthrough loss should be applied. By default it is set to 1 km, so if a passthrough loss of 10 dB was specified, the loss per metre would be 10 / 1000 = 0.1 dB/m. It can be set to any positive distance value to suit your prefered unit system, but changing it will invalidate any passthrough adjustments already specified.

To set the passthrough losses, edit the Overrides property as shown

In this example, the Passthrough Loss property for Suburban clutter was set to 5 dB, Rural 0 dB, and Urban 10 dB. The effect this has on signal propagation can be seen here

So for every meter of Suburban clutter the signal passes through, it will lose an additonal 0.05 dB.

Clutter Termination Adjustment

While passthrough loss can give a more accurate view of the whole signal path, it is sometimes desirable to apply an additional loss that takes account of the receivers likely position. For instance, users in an urban area are likely to be indoors, so a building penetration loss can be added on top of the other loses.

Here is the effect of termination loss only (no passthrough loss) on the signal

The shape of the clutter can be clearly seen in the signal where termination losses have been applied.

Note that this termination loss effect can also be produced using the Signal Modifier property of Presto prediction models. The adjustment can then be shared between models rather than applied model-by-model.

It is also useful to note that this sort of pentration loss is often already factored into the link-budget such that the target signal threshold varies by clutter type. In this situation, termination loss should not be used because you will be double-counting the effect.

Automatic Clutter Tuning

Both passthrough loss and termination loss can be tuned automatically. To enable this feature, change the Prediction Tuning properties as shown below

The minimum and maximum ranges for the clutter passthrough can be set on a per-clutter basis. Clutter adjustments will be tuned to minimize overall prediction error.