A toolbox for urban multipath radio wave propagation prediction

This thesis provides a concept and its implementation for multipath radio wave propagation prediction for urban environments. Such predictions are essential for applications like the Covering-Location- Problem in network planning. Measurement campaigns are too complex and resource-intensive and, although pure stochastic propagation models deliver a very good average performance, they do not cover particular scenarios. A propagation prediction tool reflecting the particular environment geometry without highly complex input data fit for practical use in terms of simulation run-time is of particular interest. The environment data and the path loss model play a crucial role. This work suggests the use of so called 2.5-d data, where buildings are not only defined by their outline, but also by the ground level and height. Readily available data sources are land registries or OpenStreetMap, for instance. The mandatory preprocessing of such environment data is introduced. It yields first of all a valid data set, but secondly the simulation prerequisites structural information increasing the run-time performance. Since it is infeasible to measure the error imposed by the preprocessing, in terms of the propagation prediction results, this work introduces a method to quantize the amount of deviation. Hence, an optimization of the processing steps in order to minimize the error is given. Propagation paths from the transmitter are modeled by a graph forming the basis for the proposed algorithm. Instead of conventional 3-d ray tracing, this approach consists of two successive 2-d steps, first the top view is processed and thereafter height information is handled. This eliminates redundancy and significantly improves computational speed. After the graph is computed, concrete multipath information – sequences of coordinates – is instantiated. Each coordinate bears additional information about the deflection and surfaces. This algorithm scales well ranging from calculating paths for a couple up to millions of receivers. Thus, it may be used to produce a solution for a simple route of a car or pedestrian, or to cover the whole scenario in a large grid of receivers. A properly calibrated path loss model mitigates the effects of the incomplete environment data. The suggested calibration process searches a parameter set, minimizing the RMSE. This process follows several steps, initially estimating the strongest path per receiver and than combining these paths and the reference data into an equation system. The solution to this equation is a possible parameter set, but it is iteratively refined considering new path combinations. Altogether, this tool generates multipath information in minutes for complex scenarios and in subsecond run-time for small scenarios. The output is a set of transmitter receiver links taking deflection effects in a simplified environment model into account. Finally, it can be efficiently evaluate in postprocessing to various ends. One possible application could be creating a path loss map as input for network optimization tools. Since the structure allows adding an antenna field pattern in the post-processing, a multitude of configurations can be tested efficiently. For a whole new field of applications, the multipath information is extended and embedded into a MIMO channel model. This novel semi-stochastic channel model combines the deterministic output with the geometry-based stochastic WINNER II channel model.