Combined Network Planning

Introduction

The CNP mode makes it possible to combine urban and indoor predictions. In the urban region the prediction is computed with the Urban Dominant Path Model (UDP), its settings and the resolution selected for the urban domain. For the prediction in the indoor area the Indoor Dominant Path Model (IDP) with a finer resolution is used. Thus, the resolution is automatically adapted to urban or indoor needs. The settings of the Dominant Path Model, such as path loss exponents and interaction losses, are also adapted to the current situation.

3D-View of a combined urban/indoor database, ready
to be computed with the CNP mode of the Dominant Path Model.

Adaptive Resolution

The CNP mode automatically determines the best resolution of the prediction matrix for the current situation. In the indoor areas the resolution is much finer than in the urban regions.

The CNP mode uses different resolutions for the prediction
matrix in indoor and urban areas.

Autoselection of Settings

If the prediction is done in the urban region, the urban parameter settings are used. Otherwise, if prediction is done indoor, the indoor parameter settings are used. The parameters for both scenarios can be defined by the user. The algorithm uses the parameters (path loss exponents, interaction losses, ..) according to the current situation (urban/indoor).

The CNP mode switches automatically between the different settings (urban/indoor) if a transition between urban and indoor areas is realized.

Multilayer Predictions inside Buildings

Predictions inside of buildings are possible on multiple prediction layers. In the urban region the prediction is accomplished on one single layer, in contrast to the indoor environment, where multiple prediction layers can be defined by the user.

The figure on the right shows a combined urban/indoor prediction with multiple layers inside a building.

The CNP mode offers predictions on multiple prediction
layers in the indoor area.

Sample Predictions

In the following table, some sample predictions are presented. The computations were done on an AMD Athlon XP 3000+ with 1024 MB RAM. Please click on the images to open a large view of these pictures with an additional legend.

Part of a city (191000 mē, 1m resolution, 5 minutes)	Part of a city (2038000 mē, 1m resolution, 5 minutes)
Modern multifloor office building (133000 mē, 1m resolution, 5 minutes)	Part of a city (56730 mē, 1m resolution, 1 minute)

Download a document with a comparison to other prediction models and measurements.

Read more about how the Dominant Path Model works.

Read more about the Urban Dominant Path Model.

Read more about the Indoor Dominant Path Model.

This is a sub-model for rural scenarios. In addition to the topographical database, clutter- and morpho databases can also be taken into account for the computation.

For urban scenarios, this sub-model is used. Not only urban building-databases are considered, but the topographical is also consulted for computation. For the prediction two path-searching algorithms exist: 2D and 3D. Waveguiding effects of street canyons help to achieve most accurate results.

Using indoor scenarios, this sub-model is considered. Two prediction-modes are available, 2D and a full 3D mode. Transmissions through walls are also taken into account, as well as special waveguiding effects which lead to very accurate results.