Estimation of Channel Correlation for Massive MIMO Signal Transmission

Conventional channel estimation schemes may require for the use of orthogonal pilot signals for each antenna in frequency-division duplex (FDD) wireless systems. It is a very challenging issue to get channel information with an affordable signaling overhead in massive multi-input multi-output (m-MIMO) FDD transmission environments. The signaling overhead for the channel estimation can be reduced by exploiting the downlink channel correlation matrix (DCCM) of m-MIMO channel. With the use of DCCM, the channel information can be estimated using a reduced number of pilot signals. However, the accuracy of DCCM may seriously affect the estimation performance. In this paper, we consider the estimation of DCCM in m-MIMO transmission environments. Since the distance between a base station and a user is much larger than that between antennas, a set of antenna pairs of equal distance may experience similar spatial correlation due to the far-field effect. We consider the estimation of DCCM by averaging the correlation of these antenna pairs without reception of a large number of pilot signals, significantly reducing the pilot signaling overhead. We also consider the improvement of estimated DCCM by dropping out inaccurate correlation terms. Finally, we verify the proposed scheme by computer simulation.

[1]  Erik G. Larsson,et al.  Scaling Up MIMO: Opportunities and Challenges with Very Large Arrays , 2012, IEEE Signal Process. Mag..

[2]  Giuseppe Caire,et al.  Achievable Rates of FDD Massive MIMO Systems With Spatial Channel Correlation , 2014, IEEE Transactions on Wireless Communications.

[3]  Giuseppe Caire,et al.  Joint Spatial Division and Multiplexing—The Large-Scale Array Regime , 2012, IEEE Transactions on Information Theory.

[4]  David James Love,et al.  Downlink Training Techniques for FDD Massive MIMO Systems: Open-Loop and Closed-Loop Training With Memory , 2013, IEEE Journal of Selected Topics in Signal Processing.

[5]  Karim Lounici High-dimensional covariance matrix estimation with missing observations , 2012, 1201.2577.

[6]  Yong-Hwan Lee,et al.  Performance of MIMO channel estimation with imperfect channel correlation information , 2015 .

[7]  P. Bickel,et al.  Regularized estimation of large covariance matrices , 2008, 0803.1909.

[8]  Robert W. Heath,et al.  Shifting the MIMO Paradigm , 2007, IEEE Signal Processing Magazine.

[9]  Ying-Chang Liang,et al.  Downlink channel covariance matrix (DCCM) estimation and its applications in wireless DS-CDMA systems , 2001, IEEE J. Sel. Areas Commun..

[10]  Dawei Ying,et al.  Kronecker product correlation model and limited feedback codebook design in a 3D channel model , 2014, 2014 IEEE International Conference on Communications (ICC).

[11]  A. Hero,et al.  Robust shrinkage estimation of high-dimensional covariance matrices , 2010 .

[12]  van A Allert Zelst,et al.  A single coefficient spatial correlation model for multiple-input multiple-output (MIMO) radio channels , 2002 .

[13]  Michael D. Zoltowski,et al.  Pilot Beam Pattern Design for Channel Estimation in Massive MIMO Systems , 2013, IEEE Journal of Selected Topics in Signal Processing.

[14]  Alfred O. Hero,et al.  Covariance Estimation in High Dimensions Via Kronecker Product Expansions , 2013, IEEE Transactions on Signal Processing.

[15]  Antonia Maria Tulino,et al.  Network MIMO With Linear Zero-Forcing Beamforming: Large System Analysis, Impact of Channel Estimation, and Reduced-Complexity Scheduling , 2010, IEEE Transactions on Information Theory.