Signal Processing Perspectives to Radio Channel Modelling

In this paper, we show that a proper data-model for a measured radio channel consists of deterministic and stochastic components. The deterministicdescribing the part maps channel parameters to the radio channel using an algebraic function, while the stochastic part is parameterized using second order statistics. We focus our discussion on the stochastic part describing the dense multipath component (DMC) of the channel. We propose to use the von Mises-Fisher distribution to describe the power angular profile at the transmitter and the receiver in azimuth and elevation. This distribution is appropriate for angular data. Furthermore, the uniform angular distribution, used so far to model the DMC, is included as an extreme case. We identify the basic components necessary to implement this channel model for analysis and synthesis purposes. Finally, using channel sounding measurements, we demonstrate that uniform as well as concentrated distributions occur in real world radio propagation scenarios.