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.

[1]  P. Sabatier.  Multivariate Extremes , 2006 .

[2]  Chia-Chin Chong,et al.  Statistical characterization of the 5.2 GHz wideband directional indoor propagation channels with clustering and correlation properties , 2002, Proceedings IEEE 56th Vehicular Technology Conference.

[3]  M. Salazar-Palma,et al.  A survey of various propagation models for mobile communication , 2003 .

[4]  Ernst Bonek,et al.  A Framework for Automatic Clustering of Parametric MIMO Channel Data Including Path Powers , 2006, IEEE Vehicular Technology Conference.

[5]  D. Shutin,et al.  Clustering wireless channel impulse responses in angular-delay domain , 2004, IEEE 5th Workshop on Signal Processing Advances in Wireless Communications, 2004..

[6]  H. Hashemi,et al.  Measurements and modeling of temporal variations of the indoor radio propagation channel , 1994 .

[7]  Andreas F. Molisch,et al.  The COST 259 Directional Channel Model-Part II: Macrocells , 2006, IEEE Transactions on Wireless Communications.

[8]  Andreas F. Molisch,et al.  Statistical characterization of urban spatial radio channels , 2002, IEEE J. Sel. Areas Commun..

[9]  Norman C. Beaulieu,et al.  Performance analysis of digital modulations on Weibull fading channels , 2003, 2003 IEEE 58th Vehicular Technology Conference. VTC 2003-Fall (IEEE Cat. No.03CH37484).

[10]  Chia-Chin Chong,et al.  A new statistical wideband spatio-temporal channel model for 5-GHz band WLAN systems , 2003, IEEE J. Sel. Areas Commun..

[11]  Thomas Zwick,et al.  A stochastic multipath channel model including path directions for indoor environments , 2002, IEEE J. Sel. Areas Commun..

[12]  Azriel Rosenfeld,et al.  Peak Detection Using Difference Operators , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Matti H. A. J. Herben,et al.  Radio channel modelling for terrestrial vehicular mobile applications , 2000 .

[14]  Matti H. A. J. Herben,et al.  Analysis of Clustered Multipath Estimates in Physically Nonstationary Radio Channels , 2007, 2007 IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communications.

[15]  George K. Karagiannidis,et al.  Gaussian class multivariate Weibull distributions: theory and applications in fading channels , 2005, IEEE Transactions on Information Theory.

[16]  A. Fernandez-Duran,et al.  Application of extreme value distribution to model propagation fading in indoor mobile radio environments , 2008, 2008 IEEE Radio and Wireless Symposium.

[17]  P. Vainikainen,et al.  Clusters extracted from measured propagation channels in macrocellular environments , 2005, IEEE Transactions on Antennas and Propagation.