om TX (m) Distance from TX (m) (a) Measured and simulated path loss. (b) Standard deviation of the field strength (right) vs. distance. Figure 4.7-24 Measured and simulated path loss and standard deviation of the field strength VS. distance. reducing dependency on the accuracy of the input database flow is determined. No individual reflection or dif- with respect to complete ray models. The model requires fraction coefficients need to be computed. These a simple calibration with measurements or reference data. interactions are included in the 'parameter' of the The model itself can be subdivided into two steps. paths (e.g. waveguiding factor, depending on the (a) Determination of the dominant paths reflection loss and distance of walls in the scenario). Determining the dominant paths is the most The paths can either be determined in a horizontal critical task. For indoor scenarios the algorithm is plane (2D mode) in 2.5D (urban case, Figure 4.7- explained more in detail in [WLGB97] and 26) or 3D (indoor). So in urban scenarios the wave [WoLa98] and a comparison with RT is shown in can also propagate over the rooftops as well as in the Figure 4.7-25. The model is able to determine the street canyons. 'less obstructed path' which usually transfers most (b) Prediction of the path loss along the paths of the power to the RX. The same principle can also The (empirical) prediction of the path loss along be used for urban scenarios. Figure 4.7-26 shows an the paths depends on path length d (path loss pro- example for urban scenarios (Hong Kong, incl. to- portional to n X log(d)), on the number of interac- pography). The dominant paths describe the main tions, optionally also on the angles in the path at the direction of propagation (the corridors or rooms interaction points and on the model-specific param- passed (indoor)), i.e. the path of the main energy eters (waveguiding effect, explained above). These RT (multipath) Dominant paths Figure 4.7-25 Example of dominant paths tracing as opposed to RT in an indoor scenari