Cluster Power Variation Characteristics for 3GHz-Band MIMO Communication System in a Crowded Indoor Environment

[1]  Takeshi Manabe,et al.  Estimation of propagation-path visibility for indoor wireless LAN systems under shadowing condition by human bodies , 1998, VTC '98. 48th IEEE Vehicular Technology Conference. Pathway to Global Wireless Revolution (Cat. No.98CH36151).

[2]  Mark A Beach,et al.  Temporal variation of multiple-input multiple-output (MIMO) channels in indoor environments , 2001 .

[3]  Wilhelm Keusgen,et al.  Measuring the busy urban 60 GHz outdoor access radio channel , 2014, 2014 IEEE International Conference on Ultra-WideBand (ICUWB).

[4]  Tetsuro Imai,et al.  Time-Varying Path-Shadowing Model for Indoor Populated Environments , 2010, IEEE Transactions on Vehicular Technology.

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

[6]  J. Kunisch,et al.  Ultra-wideband double vertical knife-edge model for obstruction of a ray by a person , 2008, 2008 IEEE International Conference on Ultra-Wideband.

[7]  Teruya Fujii,et al.  Physical channel modeling for static mobile terminals in indoor environments , 2010, Proceedings of the Fourth European Conference on Antennas and Propagation.

[8]  Yoann Corre,et al.  Femtocell wireless time-variant stochastic channel modelling related to indoor human activity , 2011, 2011 IEEE 22nd International Symposium on Personal, Indoor and Mobile Radio Communications.

[9]  Gerhard Bauch,et al.  Doppler spectrum from moving scatterers in a random environment , 2009, IEEE Transactions on Wireless Communications.

[10]  Alister G. Burr,et al.  A Time-Variant Wideband Spatial Channel Model Based on the 3GPP Model , 2006, IEEE Vehicular Technology Conference.

[11]  Emanuel Staudinger,et al.  Impact of human crowd activity on indoor wireless channels - Assessment and modeling , 2013, 2013 10th Workshop on Positioning, Navigation and Communication (WPNC).

[12]  William Scanlon,et al.  Modelling of MIMO channels for the populated indoor environment , 2001 .

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

[14]  Ali Kara,et al.  Effect of people moving near short-range indoor propagation links at 2.45 GHz , 2006, Journal of Communications and Networks.

[15]  T. Imai,et al.  Cluster Power Variation Estimation for MIMO Channel Modeling in Crowded Indoor Environment , 2014, 2014 IEEE 80th Vehicular Technology Conference (VTC2014-Fall).

[16]  Michael Cheffena.  Time-varying on-body wireless channel model during walking , 2014, EURASIP J. Wirel. Commun. Netw..

[17]  Rik Pintelon,et al.  Estimating the parameters of a Rice distribution: A Bayesian approach , 2009, 2009 IEEE Instrumentation and Measurement Technology Conference.

[18]  Ingolf Karls,et al.  Quasi-deterministic approach to mmWave channel modeling in a non-stationary environment , 2014, 2014 IEEE Globecom Workshops (GC Wkshps).

[19]  G. Y. Delisle,et al.  Influence of human motion on indoor wireless millimeter-wave channel characteristics , 1997, 1997 IEEE 47th Vehicular Technology Conference. Technology in Motion.

[20]  J. Medbo,et al.  Channel modeling for the stationary UE scenario , 2013, 2013 7th European Conference on Antennas and Propagation (EuCAP).

[21]  Teruya Fujii,et al.  Dynamic Channel Modeling for Static Mobile Terminals in Indoor NLOS Environments , 2007, 2007 IEEE 66th Vehicular Technology Conference.

[22]  Taga Tokio,et al.  A Study on Prediction for Field Strength using Time-varying Path Shadowing Model considering Path Shadowing Correlation , 2009 .

[23]  Jishu Das Gupta,et al.  Body-shadowing effects in indoor MIMO-OFDM channel capacity , 2009, 2009 Australasian Telecommunication Networks and Applications Conference (ATNAC).

[24]  Jens Zander,et al.  A body-shadowing model for indoor radio communication environments , 1998 .

[25]  William G. Scanlon,et al.  Prediction of variation in MIMO channel capacity for the populated indoor environment using a Radar cross-section-based pedestrian model , 2005, IEEE Transactions on Wireless Communications.

[26]  Visa Koivunen,et al.  Detection and Tracking of MIMO Propagation Path Parameters Using State-Space Approach , 2009, IEEE Transactions on Signal Processing.

[27]  Tiankui Zhang,et al.  A time-variant MIMO channel model based on the IMT-Advanced channel model , 2012, 2012 International Conference on Wireless Communications and Signal Processing (WCSP).

[28]  Michael A. Jensen,et al.  A Stochastic Model of the Time-Variant MIMO Channel Based on Experimental Observations , 2009, IEEE Transactions on Vehicular Technology.

[29]  Zhizhang Chen,et al.  Experimental studies of the 2.4-GHz ISM wireless indoor channel , 2005, 3rd Annual Communication Networks and Services Research Conference (CNSR'05).

[30]  Ali Abdi,et al.  On the estimation of the K parameter for the Rice fading distribution , 2001, IEEE Communications Letters.

[31]  Ernst Bonek,et al.  A Time-Variant MIMO Channel Model Directly Parametrised from Measurements , 2009, EURASIP J. Wirel. Commun. Netw..