Performance of a Novel Automatic Identification Algorithm for the Clustering of Radio Channel Parameters

A multipath component distance (MCD)-based automatic clustering identification algorithm is proposed to group multipath components (MPCs) obtained from radio channels. The developed algorithm iteratively and dynamically assigns the MPCs to the best cluster thanks to the MCD metric. Its performance and robustness are compared with the K-means MCD algorithm using cluster data simulated with four reference scenarios of the WINNER II channel model. The results indicate that K-means MCD is outperformed for all investigated scenarios in spite of its having a lower computational complexity and faster convergence. Moreover, a by-product of the algorithm is an optimal MCD threshold, that is, the characteristic of the cluster statistical properties for a given propagation scenario. This parameter provides a stronger physical link between the MPCs distribution and the propagation scenario. Therefore, it could be introduced in radio channel models with clusterlike features.

[1]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[2]  Andreas Richter,et al.  Estimation of Radio Channel Parameters , 2005 .

[3]  Martine Lienard,et al.  Impact of clustering at mmW band frequencies , 2015, 2015 IEEE International Symposium on Antennas and Propagation & USNC/URSI National Radio Science Meeting.

[4]  Ujjwal Maulik,et al.  Validity index for crisp and fuzzy clusters , 2004, Pattern Recognit..

[5]  Alfred O. Hero,et al.  Space-alternating generalized expectation-maximization algorithm , 1994, IEEE Trans. Signal Process..

[6]  Alister G. Burr,et al.  Survey of Channel and Radio Propagation Models for Wireless MIMO Systems , 2007, EURASIP J. Wirel. Commun. Netw..

[7]  Ruiyuan Tian,et al.  Tracking Time-Variant Cluster Parameters in MIMO Channel Measurements , 2007, 2007 Second International Conference on Communications and Networking in China.

[8]  Ernst Bonek,et al.  Improving clustering performance using multipath component distance , 2006 .

[9]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[10]  Jun-ichi Takada,et al.  Clusterization of measured direction-of-arrival data in an urban macrocellular environment , 2003, 14th IEEE Proceedings on Personal, Indoor and Mobile Radio Communications, 2003. PIMRC 2003..

[11]  Michael A. Jensen,et al.  Modeling the statistical time and angle of arrival characteristics of an indoor multipath channel , 2000, IEEE Journal on Selected Areas in Communications.

[12]  Thomas Kailath,et al.  ESPRIT-estimation of signal parameters via rotational invariance techniques , 1989, IEEE Trans. Acoust. Speech Signal Process..

[13]  Fredrik Tufvesson,et al.  On mm-Wave Multipath Clustering and Channel Modeling , 2014, IEEE Transactions on Antennas and Propagation.

[14]  Ujjwal Maulik,et al.  Performance Evaluation of Some Clustering Algorithms and Validity Indices , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  J. Franklin,et al.  The elements of statistical learning: data mining, inference and prediction , 2005 .

[16]  Ernst Bonek,et al.  Automatic Clustering of MIMO Channel Parameters using the Multi-Path Component Distance Measure , 2005 .

[17]  Ernst Bonek,et al.  How to Quantify Multipath Separation , 2002 .

[18]  Fernando Perez-Fontan,et al.  Estimation of the Number of Clusters in Multipath Radio Channel Data Sets , 2013, IEEE Transactions on Antennas and Propagation.

[19]  Krzysztof Kryszczuk,et al.  Estimation of the Number of Clusters Using Multiple Clustering Validity Indices , 2010, MCS.

[20]  Weina Wang,et al.  On fuzzy cluster validity indices , 2007, Fuzzy Sets Syst..

[21]  Reiner S. Thoma,et al.  On the reliability of multipath cluster estimation in realistic channel data sets , 2014, The 8th European Conference on Antennas and Propagation (EuCAP 2014).

[22]  Dong-Jo Park,et al.  A Novel Validity Index for Determination of the Optimal Number of Clusters , 2001 .

[23]  Reiner S. Thomä,et al.  Clustering of MIMO Channel Parameters - Performance Comparison , 2009, VTC Spring 2009 - IEEE 69th Vehicular Technology Conference.

[24]  Greg Hamerly,et al.  Alternatives to the k-means algorithm that find better clusterings , 2002, CIKM '02.