Principle of the Dominant Paths

Group similar rays together based on the sequence of rooms and walls transmitted through.

It must be pointed out that in contrast to the deterministic models the number of interactions is not used for the classification of the rays. It is not important how many reflections, or diffractions occur along the ray - more important is the sequence of rooms passed and sequence of walls transmitted through.

All rays, passing through the same rooms and transmitting through the same walls, can be described by a representative dominant path. Therefore each receiver point is reached by different dominant paths, passing the rooms and transmitting the walls in a different sequence.

This effect is pointed out with the example given in Figure 1. All rays reaching receiver R2 in the left part of the figure can be described by their corresponding dominant paths in the right part of the figure. As these dominant paths have no reflection, or diffraction points but only points of changing directions, it is impossible to compute the field strength at their ends by using GTD/UTD. One possibility for computing the field strength is some kind of empirical model, based on the regression of measurements. Another approach is artificial neural networks.

One of the basic ideas of the new prediction model is the fast determination of the dominant paths. While in principle it is possible to determine all rays with a ray-tracing algorithm and then to combine the different groups of rays to dominant paths, a very fast algorithm is developed for the determination of the dominant paths. This algorithm is nearly as fast as the prediction with empirical models.