Enhanced Lee Model from Rough Terrain Sampling Data Aspect

The Lee propagation prediction model has been well recognized by the wireless industry as one of the most accurate propagation prediction models. This paper discusses innovative approaches dealing with rough digital samples of terrain data and enhancements to the Lee model during the validation process. In general, the Lee model is composed of two parts, the impact of man-made structures and the impact of the natural terrain variation. There are other papers discuss innovative algorithms on calculating effective antenna gain and diffraction loss as well as on enhancing the Lee model. This paper focuses on the natural (terrain) factor. The new algorithm presented in this paper is quite different than others as it integrates both Line Of Site (LOS) and shadow loss calculation together for the Lee model. First, in the LOS scenario, it addresses the issue of big swing of effective antenna gain due to non-continuous terrain data. Second, in the non-LOS situation, the effective antenna gain is integrated with shadow loss. Both single knife edge and multiple knife edge scenarios are discussed. The new algorithm is developed based on the analysis of measured and predicted (the theoretical shadow loss and effective antenna gain) data. The new algorithm involves more calculation but it improves the accuracy of the predicted value. This algorithm was implemented and verified using field terrain and measurement data from a variety of different environments and different countries including Italy, US, Spain, Japan, South Korea, Taiwan and Romania.