Wireless Propagation Multipaths using Spectral Clustering and Three-Constraint Affinity Matrix Spectral Clustering

Main Article Content

Jojo Blanza

Abstract

This study focused on spectral clustering (SC) and three-constraint affinity matrix spectral clustering (3CAM-SC) to determine the number of clusters and the membership of the clusters of the COST 2100 channel model (C2CM) multipath dataset simultaneously. Various multipath clustering approaches solve only the number of clusters without taking into consideration the membership of clusters. The problem of giving only the number of clusters is that there is no assurance that the membership of the multipath clusters is accurate even though the number of clusters is correct. SC and 3CAM-SC aimed to solve this problem by determining the membership of the clusters. The cluster and the cluster count were then computed through the cluster-wise Jaccard index of the membership of the multipaths to their clusters. The multipaths generated by C2CM were transformed using the directional cosine transform (DCT) and the whitening transform (WT). The transformed dataset was clustered using SC and 3CAM-SC. The clustering performance was validated using the Jaccard index by comparing the reference multipath dataset with the calculated multipath clusters. The results show that the effectiveness of SC is similar to the state-of-the-art clustering approaches. However, 3CAM-SC outperforms SC in all channel scenarios. SC can be used in indoor scenarios based on accuracy, while 3CAM-SC is applicable in indoor and semi-urban scenarios. Thus, the clustering approaches can be applied as alternative clustering techniques in the field of channel modeling.

Downloads

Download data is not yet available.

Article Details

How to Cite
1.
Blanza J. Wireless Propagation Multipaths using Spectral Clustering and Three-Constraint Affinity Matrix Spectral Clustering. Baghdad Sci.J [Internet]. 2021Jun.20 [cited 2021Aug.3];18(2(Suppl.):1001. Available from: https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/6219
Section
article

References

Suresh G, Balasubramanian R. An ensemble feature selection model using fast convergence ant colony optimization algorithm. International Journal of Emerging Trends in Engineering Research. 2020;8(4),1417-1423. https://doi.org/10.30534/ijeter/2020/77842020

Pavithra G, Abirami P, Bhuvaneshwari S, Dharani S, Haridharani B. A survey on intrusion detection mechanism using machine learning algorithms. International Journal of Emerging Trends in Engineering Research. 2020;8(4), 945-949. https://doi.org/10.30534/ijeter/2020/01842020

Ibrahim S, Rozan M, Sabri N. Comparative analysis of support vector machine (SVM) and convolutional neural network (CNN) for white blood cells’ classification. International Journal of Advanced Trends in Computer Science and Engineering. 2019;8(1.3),394-399. https://doi.org/10.30534/ijatcse/2019/6981.32019

Chandirika B, Sakthivel NK, Subasree S. An energy efficient k-means clustering based trust model for wireless sensor networks. International Journal of Advanced Trends in Computer Science and Engineering. 2019;8(2),144-153. https://doi.org/10.30534/ijatcse/2019/08822019

Blanza, J, Materum L, Hirano T. Deep divergence-based clustering of wireless multipaths for simultaneously addressing the grouping and cardinality. International Journal of Emerging Trends in Engineering Research. 2020;8(7),3104-3110. https://doi.org/10.30534/ijeter/2020/37872020

Blanza, J, Materum L. Joint identification of the clustering and cardinality of wireless propagation multipaths. International Journal of Emerging Trends in Engineering Research. 2019;7(12),762-767. https://doi.org/10.30534/ijeter/2019/057122019

Foschini G, Gans M. On limits of wireless communications in a fading

environment when using multiple antennas. Wireless Personal Communications. 1998;6,311-

https://doi.org/10.1023/A:1008889222784

Verdone R, Zanella A. Pervasive Mobile and Ambient Wireless Communications: COST Action 2100, Signals and Communication Technology. Springer. 2012

Czink N, Cera P, Salo J, Bonek E, Nuutinen JP, Ylitalo J. A framework for automatic clustering of parametric MIMO channel data including path powers. IEEE 64th Vehicular Technology Conference. 2006;1-5. https://doi.org/10.1109/VTCF.2006.35

Gentile C. Using the kurtosis measure to identify clusters in wireless channel impulse responses. IEEE Transactions on Antennas and Propagation. 2013;61(6),3392-3395. https://doi.org/10.1109/TAP.2013.2253299

He R, Li Q, Ai B, Geng Y, Molisch A, Kristem V, Zhong Z, Yu J. A kernel-power-density-based algorithm for channel multipath components clustering using the kurtosis measure to identify clusters in wireless channel impulse responses. IEEE Transactions on Wireless Communications. 2017;16(11),7138-7151. https://doi.org/10.1109/TWC.2017.2740206

Li Y, Zhang J, Ma Z, Zhang Y. Clustering analysis in the wireless propagation channel with a variational Gaussian mixture model. IEEE Transactions on Big Data. 2020;6(2),223-232. https://doi.org/10.1109/TBDATA.2018.2840696

Blanza J, Teologo, A, Materum L. Datasets for multipath clustering at 285 MHz and 5.3 GHz bands based on COST 2100 MIMO channel model. 2019 International Symposium on Multimedia and Communication Technology. 2019;1-5. https://doi.org/10.1109/ISMAC.2019.8836143

Li Z, Cheong L, Yang S, Toh K. Simultaneous clustering and model selection: algorithm, theory and applications. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2018;40(8),1964-1978. https://doi.org/10.1109/TPAMI.2017.2739147

Kampffmeyer M, Løkse S, Bianchi F, Livi L, Salberg A, Jenssen R. Deep divergence-based approach to clustering. Neural Network. 2019;113,91-101. https://doi.org/10.1016/j.neunet.2019.01.015

Chowdhury M, Biswas A. Wireless Communication: Theory and Applications. Cambridge University Press. 2017.

Molisch A. Wireless Communications. Wiley-IEEE. 2012.

3GPP. Study on channel model for frequencies from 0.5 to 100 GHz. 3GPP 2059 TR 38.901 V16.0.0. Technical report. 2019.

Saleh A, Valenzuela R. A statistical model for indoor multipath propagation. IEEE Journal on Selected Areas in Communications. 1987;5(2),128-137. https://doi.org/10.1109/JSAC.1987.1146527

Molisch A, Cassioli D, Chong C-C, Emami S, Fort A, Kannan B, Karedal J, Kunisch J, Schantz H G, Siwiak K, et al. A comprehensive standardized model for ultrawideband propagation channels. IEEE Transactions on Antennas and Propagation. 2006;54(11),3151-3166. https://doi.org/10.1109/TAP.2006.883983

Meinila J, Kyosti P, Jamsa T, Hentila L. WINNER II channel models. Radio Technologies and Concepts for IMT-Advanced. 2009;39-92. https://doi.org/10.1002/9780470748077.ch3

Molisch A. Modeling the MIMO propagation channel. REVUE HF. 2003;2209(4),5-14.

Chong C-C, Tan C-M, Laurenson DI, McLaughlin S, Beach MA, Nix AR. A new statistical wideband spatio-temporal channel model for 5-GHz band WLAN systems. IEEE Journal on Selected Areas in Communications. 2003;21(2),139-150. https://doi.org/10.1109/JSAC.2002.807347

Ghassemzadeh SS, Greenstein LJ, Sveinsso T, Kavcic A, Tarokh V. UWB delay profile models for residential and commercial indoor environments. IEEE Transactions on Vehicular Technology. 2005;54(4),1235-1244. https://doi.org/10.1109/TVT.2005.851379

Karedal J, Wyne S, Almers P, Tufvesson F, Molisch A. Statistical analysis of the UWB channel in an industrial environment. IEEE 60th Vehicular Technology Conference. 2004;1,81–85. https://doi.org/10.1109/VETECF.2004.1399930

Corrigan M, Walton A, Niu W, Li J, Talty T. Automatic UWB clusters identification. IEEE Radio and Wireless Symposium. 2009;376-379. https://doi.org/10.1109/RWS.2009.4957359

Mlinarsky F. Throughput test methods for MIMO radios. Octoscope. 2014.

Li B, Zhao C, Zhang H, Zhou Z, Nallanathan A. Efficient and robust cluster identification for ultra-wideband propagations inspired by biological ant colony clustering. IEEE Transactions on Communications. 2015;63(1),286-300. https://doi.org/10.1109/TCOMM.2014.2377120

Cheng S, Martinez-Ingles M-T, Gaillot DP, Molina-Garcia-Pardo J-M, Lienard M, Degauque P. Performance of a novel automatic identification algorithm for the clustering of radio channel parameters. IEEE Access. 2015,3,2252-2259. https://doi.org/10.1109/ACCESS.2015.2497970

He R, Chen W, Ai B, Molisch A, Wang W, Zhong Z, Yu J, Sangodoyin A. On the clustering of radio channel impulse responses using sparsity based methods. IEEE Transactions on Antennas and Propagation. 2016;64(6),2465-2474. https://doi.org/10.1109/TAP.2016.2546953

Hanpinitsak P, Saito K, Takada J-i, Kim M, Materum L. Multipath clustering and cluster tracking for geometry-based stochastic channel modeling. IEEE Transactions on Antennas and Propagation. 2017;65(11),6015-6028. https://doi.org/10.1109/TAP.2017.2754417

COST 2100 channel model. 2018. Retrieved from http://github.com/cost2100/cost2100/tree/master/matlab

Poutanen J, Haneda K, Liu L, Oestges C, Tufvesson F, Vainikainen P. Parameterization of the COST 2100 MIMO channel model in indoor scenarios. Proceedings of the 5th European Conference on Antennas and Propagation. 2011; 3606-3610.

Zhu M, Eriksson G, Tufvesson F. The COST 2100 channel model: parameterization and validation based on outdoor MIMO measurements at 300 MHz. IEEE Transactions on Wireless Communications. 2013;12(2),888-897. https://doi.org/10.1109/TWC.2013.010413.120620

Xu R, Wunsch D. Survey of clustering algorithms. IEEE Transactions on Neural Networks. 2005;16(3),645-678. https://doi.org/10.1109/TNN.2005.845141

Steinbauer M, Molisch A, Bonek E. The double directional radio channel. IEEE Antennas and Propagation Magazine. 2001;43(4),51-63. https://doi.org/10.1109/74.951559

RCore. R: A language and environment for statistical computing. R Foundation for Statistical Computing. 2015.

Maechler M. Diptest: Hartigan’s dip test statistic for unimodality corrected. R package version 0.75-7. 2015.

Ng A, Weiss Y, Jordan M. On spectral clustering analysis and an algorithm. Proceedings of Neural Information Processing Systems Conference. 2001;849-856.

Elhamifar E, Vidal R Sparse subspace clustering: algorithm, theory, and applications. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2001;35(11),2765-

https://doi.org/10.1109/TPAMI.2013.57

Blanza J. Identification of wireless propagation multipath lusters and their cardinality using simultaneous clustering and model selection matrix affinity and deep divergence-based clustering approaches.[Doctoral Dissertation]. De La Salle University; 2002.