Quantization-based complexity reduction for range-dependent modified Gilbert model

In this contribution we propose an algorithm for dimension reduction of the previously introduced range-dependent modified Gilbert model. For reduction of the model dimension we suggest to jointly quantize model parameters estimated based on very narrow measurement intervals. We analyze the influence of the number of quantization levels on the model performance and based on this analysis find the optimal model dimension. Furthermore, we show that selected environmental and propagation effects, influencing the error pattern, can be identified by means of quantizing model parameters. Finally we demonstrate that these effects can be accurately reproduced by our model.