Projecte final de carrera fet en col.laboració amb FTW i Technische Universität Wien

English: In this thesis we will characterize the vehicle-to-vehicle channel in various scenarios based in risk situations. We estimate diferent channel parameters as the time-varying root mean square (rms) delay and Doppler spreads, as well as the stationarity time. Also, we present a new approach for the identiï¬ cation of scattering objects. We move one step forward from the method used until now, where the identiï¬ cation was done visually based on the power delay proï¬ le and video material recorded in the measurement campaigns. We propose to use the local scattering function (LSF), which brings the Doppler domain into play. The LSF is a multitaper estimate of the 2 dimensional power spectral density in delay and Doppler. Each peak of the LSF is composed by several multipath components (MPCs) coming from the same scattering object. Our approach consists of two steps: detection of the relevant peaks, and assignment of MPCs to the scattering objects. We do that by using a clustering algorithm. We apply the method to a set of vehicular radio channel measurements and extract the time-varying cluster parameters. The clusters have ellipsoidal shape with their longer axes in the Doppler domain. The ï¬ rst detected cluster presents different properties than the rest of the clusters, being larger, constant in time, and more static in the delay-Doppler plane. By identifying properly only the relevant scattering objects, vehicular channel models can be written in simpler ways than current approaches, such as the geometry- based stochastic channel model, very well suited for modeling the vehicular channel.