Land use classification as a key component for path loss prediction in rural areas

Path loss prediction is an essential building block for planning and optimization of cellular radio networks. Semi-empirical land use based models yield accurate and efficient path loss prediction results in rural areas. Consequently, land use information serves as a key input for those models. In this paper, we present a new C × K-Nearest-Mean classification method operating on landscape images to provide the required input data for land use based path loss prediction models. With respect to this purpose our approach exceeds conventional land use classification using a neural network particularly in terms of total error rate. Utilization of our classification method by a specific path loss prediction model leads to prediction results with a mean square error of less than 6dB.