A Stochastic Coupling-Based Channel Impulse Response Matrix Model for Massive MIMO Channels

A novel stochastic channel impulse response matrix (CIRM) model for massive multiple input multiple output (MI-MO) channels is proposed in this papen Under the framework of this proposed model, the CIRM can be modeled as a sum of couplings between the steering vectors at the base station (BS) end and the eigenbases at the mobile station (MS) side. The fading of the coupling between the steering vector and the eigenbase is modeled as Nakagami distribution. Furthermore, the closed form of the capacity is derived based on the proposed framework. Compared with the traditional Weibchselberger model, the proposed model has lower complexity. To validate the proposed model, extensive massive MIMO channel measurements are carried out in an indoor environment The results show that the new model provides a better fit to the measured results than Weibchselberger model. Finally, the closed form and PDF are validated by Monte Carlo realizations of the proposed model. This CIRM model can be used for massive MIMO design in future fifth-generation communication system design.

[1]  Akbar M. Sayeed,et al.  Deconstructing multiantenna fading channels , 2002, IEEE Trans. Signal Process..

[2]  Jie Huang,et al.  Multi-Frequency mmWave Massive MIMO Channel Measurements and Characterization for 5G Wireless Communication Systems , 2017, IEEE Journal on Selected Areas in Communications.

[3]  Erik G. Larsson,et al.  Massive MIMO for next generation wireless systems , 2013, IEEE Communications Magazine.

[4]  Hongbo Zhu,et al.  Measurement and empirical modeling of massive MIMO channel matrix in real indoor environment , 2016, 2016 8th International Conference on Wireless Communications & Signal Processing (WCSP).

[5]  Yang Zhang,et al.  3-D MIMO Parametric Stochastic Channel Model for Urban Macrocell Scenario , 2017, IEEE Transactions on Wireless Communications.

[6]  Cheng-Xiang Wang,et al.  A Non-Stationary 3-D Wideband Twin-Cluster Model for 5G Massive MIMO Channels , 2014, IEEE Journal on Selected Areas in Communications.

[7]  Jiajing Chen,et al.  Measurement-Based Massive MIMO Channel Modeling for Outdoor LoS and NLoS Environments , 2017, IEEE Access.

[8]  O. Edfors,et al.  A General Coupling-Based Model Framework for Wideband MIMO Channels , 2012, IEEE Transactions on Antennas and Propagation.

[9]  P. Moschopoulos,et al.  The distribution of the sum of independent gamma random variables , 1985 .

[10]  F. Massey The Kolmogorov-Smirnov Test for Goodness of Fit , 1951 .

[11]  Tao Jiang,et al.  Channel Prediction in Time-Varying Massive MIMO Environments , 2017, IEEE Access.

[12]  Mérouane Debbah,et al.  Massive MIMO in the UL/DL of Cellular Networks: How Many Antennas Do We Need? , 2013, IEEE Journal on Selected Areas in Communications.

[13]  Ernst Bonek,et al.  A stochastic MIMO channel model with joint correlation of both link ends , 2006, IEEE Transactions on Wireless Communications.

[14]  Preben E. Mogensen,et al.  A stochastic MIMO radio channel model with experimental validation , 2002, IEEE J. Sel. Areas Commun..

[15]  W. R. Braun,et al.  A physical mobile radio channel model , 1991 .

[16]  Cheng-Xiang Wang,et al.  A Non-Stationary Wideband Channel Model for Massive MIMO Communication Systems , 2015, IEEE Transactions on Wireless Communications.

[17]  Theodore S. Rappaport,et al.  Geometrical-based statistical macrocell channel model for mobile environments , 2002, IEEE Trans. Commun..

[18]  Mansoor Shafi,et al.  An Extended One-Ring MIMO Channel Model , 2007, IEEE Transactions on Wireless Communications.

[19]  Soung Chang Liew,et al.  Outage-Limit-Approaching Channel Coding for Future Wireless Communications: Root-Protograph Low-Density Parity-Check Codes , 2019, IEEE Vehicular Technology Magazine.

[20]  Fredrik Tufvesson,et al.  A flexible 100-antenna testbed for Massive MIMO , 2014, 2014 IEEE Globecom Workshops (GC Wkshps).