Modeling of Wireless Networks Using Multivariate Time Models
The literature analysis of propagation models has investigated different statistical prediction methods to identify appropriate techniques for this purpose. This paper presents the results of propagation channel modeling, based on multivariate time series models using data collected in measurement campaigns and the main characteristics of urbanization in the city of Belem-PA. Transfer function models were used to evaluate the relationship between received power signal and other variables, such as the height of buildings, the distance between buildings, and the distance to the radio base station. A multivariate model was designed in which the contributions due to the height of the buildings and the distance between buildings had a significant effect on the received power signal.