Hierarchical Radiosity Model for the Prediction of Propagation in Micro-Cellular Urban Environment

As mobile networks are growing fast in size and complexity, radio network planning needs to be optimized to cope with both increased traffic demand and spectrum scarcity. During this process, these objectives are achieved by an accurate propagation models and precise analysis of mobile user´s behavior in terms of mobility and traffic. Nowadays, we can find in the literature many propagation models for radio network planning. These models can be classified in two different categories. The first one, called empirical models are deduced from statistics based on measurements, and express the mean attenuation as function of many parameters like distance, antenna height, frequency, etc... The major drawback of this kind of model is the lack of accuracy especially in micro-cellular environment where the presence of many obstacles is the dominant characteristic and accuracy of coverage required. The second category of models is qualified as exact; it has been introduced to recover the drawbacks of the empirical models, like radiosity. Our previous study done on this model has shown that this kind of model, although it is more accurate than empirical one, it requires a huge memory space and takes long processing time to generate results. In this paper, we focus our attention on the optimization of radiosity algorithm by introducing the concept of hierarchical radiosity. An implementation of this method is done to evaluate its performance and to compare it with progressive refinement radiosity. After, we compare the generated result for the two models with real measurements to determine their accuracy.