Identifying and Modelling Multipath Clusters in Propagation Measurement Data

In this paper, a new algorithm that is able to identify and track multipath clusters in the delay-angular domain is introduced. The introduced algorithm avoids calculations of a single distance measure from quantities with different nature; instead it performs the clustering via extracting multiple 1D waveforms from the 2D delay-angular domain. The results from applying this algorithm to measured radio propagation data (recorded using a sounding system with 32 receive antenna) is used to classify the identified multipath clusters into different groups. The lengths of the active intervals (which are defined as the lengths of the intervals in which clusters exists) of the multipath clusters as well as their powers in the different groups are modelled. It was shown that the power of multipath clusters in these different groups can be modelled using the Generalized Extreme Value distribution and that this model passes the Kolmogorov-Smirnov (KS) goodness of fit test at the 5% significance level.