Clustering in the Wireless Channel with a Power Weighted Statistical Mixture Model in Indoor Scenario
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摘要
Cluster-based channel model is the main stream of fifth generation mobile communications, thus the accuracy of clustering algorithm is important. Traditional Gaussian mixture model(GMM) does not consider the power information which is important for the channel multipath clustering. In this paper, a normalized power weighted GMM(PGMM)is introduced to model the channel multipath components(MPCs). With MPC power as a weighted factor, the PGMM can fit the MPCs in accordance with the cluster-based channel models. Firstly, expectation maximization(EM) algorithm is employed to optimize the PGMM parameters. Then, to further increase the searching ability of EM and choose the optimal number of components without resort to cross-validation, the variational Bayesian(VB) inference is employed. Finally, 28 GHz indoor channel measurement data is used to demonstrate the effectiveness of the PGMM clustering algorithm.
Cluster-based channel model is the main stream of fifth generation mobile communications, thus the accuracy of clustering algorithm is important. Traditional Gaussian mixture model(GMM) does not consider the power information which is important for the channel multipath clustering. In this paper, a normalized power weighted GMM(PGMM)is introduced to model the channel multipath components(MPCs). With MPC power as a weighted factor, the PGMM can fit the MPCs in accordance with the cluster-based channel models. Firstly, expectation maximization(EM) algorithm is employed to optimize the PGMM parameters. Then, to further increase the searching ability of EM and choose the optimal number of components without resort to cross-validation, the variational Bayesian(VB) inference is employed. Finally, 28 GHz indoor channel measurement data is used to demonstrate the effectiveness of the PGMM clustering algorithm.
Cluster-based channel model is the main stream of fifth generation mobile communications, thus the accuracy of clustering algorithm is important. Traditional Gaussian mixture model(GMM) does not consider the power information which is important for the channel multipath clustering. In this paper, a normalized power weighted GMM(PGMM)is introduced to model the channel multipath components(MPCs). With MPC power as a weighted factor, the PGMM can fit the MPCs in accordance with the cluster-based channel models. Firstly, expectation maximization(EM) algorithm is employed to optimize the PGMM parameters. Then, to further increase the searching ability of EM and choose the optimal number of components without resort to cross-validation, the variational Bayesian(VB) inference is employed. Finally, 28 GHz indoor channel measurement data is used to demonstrate the effectiveness of the PGMM clustering algorithm.
引文
[1]J.Zhang,P.Tang et al.,“6-100 GHz research progress and challenges from a channel perspective for fifth generation(5G)and future wireless communication,”Science China Information Sciences,vol.60,no.8,2017.
[2]T.S.Rappaport,Y.Xing et al.,“Overview of millimeter wave channel models for fifth-generation wireless networks,”IEEE Transactions on Antennas and Propagation,vol.65,no.12,2017,pp.6213-6230.
[3]MiWEBA:Channel modeling and characterization[R],document Deliverable D5.1,Intel,Orange,and HHI,2014.
[4]V Nurmela et al.METIS channel models[R],document deliverable D1.4,METIS,2015.
[5]Study on Channel Model for Frequency Spectrum above 6 GHz[R].V14.2.0,document 3GPPTR 38.900,2016.
[6]Aalto University et al.5G channel model for bands up to 100 GHz[R].V2.0.5GCM,Tech.Rep.,2016.
[7]J.Zhang,“The interdisciplinary research of big data and wireless channel:A cluster-based channel model,”China Communications,vol.13,no.S2,2016,pp.14-26.
[8]K.H.Li,M.A.Ingram et al.,“Impact of clustering in statistical indoor propagation models on link capacity,”IEEE Transactions on Communications,no.50,vol.4,2002,pp.521-523.
[9]Y.Li,J.Zhang et al.,“Clustering in wireless propagation channel with a statistics-based framework,”Proc.IEEE Wireless Communications and Networking Conference,Barcelona,2018:1-6.
[10]Y.Li,J.Zhang et al.,“Clustering analysis in the wireless propagation channel with a variational Gaussian mixture Mmodel,”IEEE Transactions on Big Data,2018,DOI:10.1049/ietcom.2017.0854.
[11]N.Czink,P.Cera et al.,“Automatic clustering of MIMO channel parameters using the multipath component distance measure,”Proc.the International Symposium on Wireless Personal Multimedia Communications,2005.
[12]C.Schneider,M.Ibraheam et al.,“On the Rreliability of multipath cluster estimation in realistic channel data sets,”Proc.8th European Conference on Antennas and Propagation,2014,pp.449-453.
[13]R.He,Q.Li et al.,“A kernel-power-density-based algorithm for channel Mmultipath components clustering,”IEEE Transactions on Wireless Communications,vol.16,no.11,pp.7138-7151,Nov.2017.
[14]C.Huang,R.He et al.,“A novel tracking-based multipath component clustering algorithm,”IEEE Antennas and Wireless Propagation Letters,vol.16,pp.2679-2683,2017.
[15]M.R.Akdeniz,Y.Liu et al,”Millimeter wave channel modeling and cellular capacity evaluation,”IEEE Journal on Selected Areas in Communcations,vol.32,no.6,2014,pp.1164-1179.
[16]3GPP TR 36.901 V14.1.1 technical report:study on channel model for frequencies from 0.5 to100GHz(release 14)[R].July 2017,available:http://www.3gpp.org/.
[17]N.Czink,P.Cera et al.,“A framework for automatic clustering of parametric MIMO channel data including path powers,”Proc.IEEE Vehicular Technology Conference,2006,pp.1-5.
[18]C.Gustafson,“60 GHz wireless propagation channels:characterization,modeling and evaluation,”Polymer,vol.54,no.14,2014,3492-3503.
[19]Y.R.Fan,W.H.Huang et al.,“Hydrologic risk analysis in the yangtze river basin through coupling Gaussian mixtures into copulas,”Advances in Water Resources,2016,pp.170-185.
[20]Y.Zhao,X.Wang et al.,“Channel estimation and throughput evaluation for 5G wireless communication systems in various scenarios on high speed railways,”China Communications,vol.15,no.4,2018,pp.86-97.
[21]D.G.Tzikas,A.C.Likas et al.,“The variational approximation for bayesian inference,”IEEESignal Processing Magazine,vol.25,no.6,2008,pp.131-146.
[22]B.H.Fleury,M.Tschudin et al.,“Channel parameter estimation in mobile radio environments using the SAGE algorithm,”IEEE Journal on Selected Areas in Communications,vo.17,no.3,1999,pp.434-450.
[23]R.Feng,Y.Liu et al.,“Wireless channel parameter estimation algorithms:recent advances and future challenges,”China Communications,vol.15,no.5,2018,pp.211-228.
[24]J.Zhang,Y.Zhang et al.,“3-D MIMO:how much does it meet our expectations observed from channel measurements,”IEEE Journal on Selected Areas in Communications,vol.35,no.8,2017,pp.1887-1903.
[25]D.Du,J.Zhang et al.,“Cluster characteristics of wideband 3D MIMO channels in outdoor-to-indoor scenario at 3.5 GHz,”Proc.79th Vehicular Technology Conference,2014,pp.1-6.
[26]C.M.Bishop,Pattern recognition and machine learning.Springer,2006.
[27]S.Cheng,M.T.Martinez-Ingles et al.,“Performance of a novel automatic identification algorithm for the clustering of radio channel parameters,”IEEE Access,2015,pp.2252-2259.
[28]X.Zhao,S.Li et al.,“Channel measurements,modeling,simulation and validation at 32 GHz in outdoor microcells for 5G radio systems,”IEEE Access,vol.5,2017,pp.1062-1072.
[29]M.Halkidi,Y.Batistakis et al.,“On clustering validation techniques,”Journal of Intelligent Information Systems,vol.17,no.2,2001,107-145.
[2]T.S.Rappaport,Y.Xing et al.,“Overview of millimeter wave channel models for fifth-generation wireless networks,”IEEE Transactions on Antennas and Propagation,vol.65,no.12,2017,pp.6213-6230.
[3]MiWEBA:Channel modeling and characterization[R],document Deliverable D5.1,Intel,Orange,and HHI,2014.
[4]V Nurmela et al.METIS channel models[R],document deliverable D1.4,METIS,2015.
[5]Study on Channel Model for Frequency Spectrum above 6 GHz[R].V14.2.0,document 3GPPTR 38.900,2016.
[6]Aalto University et al.5G channel model for bands up to 100 GHz[R].V2.0.5GCM,Tech.Rep.,2016.
[7]J.Zhang,“The interdisciplinary research of big data and wireless channel:A cluster-based channel model,”China Communications,vol.13,no.S2,2016,pp.14-26.
[8]K.H.Li,M.A.Ingram et al.,“Impact of clustering in statistical indoor propagation models on link capacity,”IEEE Transactions on Communications,no.50,vol.4,2002,pp.521-523.
[9]Y.Li,J.Zhang et al.,“Clustering in wireless propagation channel with a statistics-based framework,”Proc.IEEE Wireless Communications and Networking Conference,Barcelona,2018:1-6.
[10]Y.Li,J.Zhang et al.,“Clustering analysis in the wireless propagation channel with a variational Gaussian mixture Mmodel,”IEEE Transactions on Big Data,2018,DOI:10.1049/ietcom.2017.0854.
[11]N.Czink,P.Cera et al.,“Automatic clustering of MIMO channel parameters using the multipath component distance measure,”Proc.the International Symposium on Wireless Personal Multimedia Communications,2005.
[12]C.Schneider,M.Ibraheam et al.,“On the Rreliability of multipath cluster estimation in realistic channel data sets,”Proc.8th European Conference on Antennas and Propagation,2014,pp.449-453.
[13]R.He,Q.Li et al.,“A kernel-power-density-based algorithm for channel Mmultipath components clustering,”IEEE Transactions on Wireless Communications,vol.16,no.11,pp.7138-7151,Nov.2017.
[14]C.Huang,R.He et al.,“A novel tracking-based multipath component clustering algorithm,”IEEE Antennas and Wireless Propagation Letters,vol.16,pp.2679-2683,2017.
[15]M.R.Akdeniz,Y.Liu et al,”Millimeter wave channel modeling and cellular capacity evaluation,”IEEE Journal on Selected Areas in Communcations,vol.32,no.6,2014,pp.1164-1179.
[16]3GPP TR 36.901 V14.1.1 technical report:study on channel model for frequencies from 0.5 to100GHz(release 14)[R].July 2017,available:http://www.3gpp.org/.
[17]N.Czink,P.Cera et al.,“A framework for automatic clustering of parametric MIMO channel data including path powers,”Proc.IEEE Vehicular Technology Conference,2006,pp.1-5.
[18]C.Gustafson,“60 GHz wireless propagation channels:characterization,modeling and evaluation,”Polymer,vol.54,no.14,2014,3492-3503.
[19]Y.R.Fan,W.H.Huang et al.,“Hydrologic risk analysis in the yangtze river basin through coupling Gaussian mixtures into copulas,”Advances in Water Resources,2016,pp.170-185.
[20]Y.Zhao,X.Wang et al.,“Channel estimation and throughput evaluation for 5G wireless communication systems in various scenarios on high speed railways,”China Communications,vol.15,no.4,2018,pp.86-97.
[21]D.G.Tzikas,A.C.Likas et al.,“The variational approximation for bayesian inference,”IEEESignal Processing Magazine,vol.25,no.6,2008,pp.131-146.
[22]B.H.Fleury,M.Tschudin et al.,“Channel parameter estimation in mobile radio environments using the SAGE algorithm,”IEEE Journal on Selected Areas in Communications,vo.17,no.3,1999,pp.434-450.
[23]R.Feng,Y.Liu et al.,“Wireless channel parameter estimation algorithms:recent advances and future challenges,”China Communications,vol.15,no.5,2018,pp.211-228.
[24]J.Zhang,Y.Zhang et al.,“3-D MIMO:how much does it meet our expectations observed from channel measurements,”IEEE Journal on Selected Areas in Communications,vol.35,no.8,2017,pp.1887-1903.
[25]D.Du,J.Zhang et al.,“Cluster characteristics of wideband 3D MIMO channels in outdoor-to-indoor scenario at 3.5 GHz,”Proc.79th Vehicular Technology Conference,2014,pp.1-6.
[26]C.M.Bishop,Pattern recognition and machine learning.Springer,2006.
[27]S.Cheng,M.T.Martinez-Ingles et al.,“Performance of a novel automatic identification algorithm for the clustering of radio channel parameters,”IEEE Access,2015,pp.2252-2259.
[28]X.Zhao,S.Li et al.,“Channel measurements,modeling,simulation and validation at 32 GHz in outdoor microcells for 5G radio systems,”IEEE Access,vol.5,2017,pp.1062-1072.
[29]M.Halkidi,Y.Batistakis et al.,“On clustering validation techniques,”Journal of Intelligent Information Systems,vol.17,no.2,2001,107-145.