A CSI acquisition approach for mmWave massive MIMO

Massive Multiple-Input Multiple-Output (MIMO) is considered as a key technology for 4G and 5G wireless communication systems to improve spectrum efficiency by supporting large number of concurrent users. In addition, for the target frequency band of 5G system, mmWave band, massive MIMO is pivotal in compensating the high pathloss. In this paper, we investigate the channel state information (CSI) acquisition problem for mmWave massive MIMO. With hybrid analog-digital antenna architecture, how to derive the analog beamforming and digital beamforming is studied. An iterative analog beam acquisition approach is proposed to save system overhead and reduce beam searching complexity. Regarding the digital beamforming, a grouping based codebook is proposed to facilitate CSI feedback. The codebook is then extended to incorporate also analog beam acquisition. Furthermore, channel reciprocity is exploited to save CSI reporting overhead and a two-stage approach is proposed to fully utilize the channel reciprocity at both mobile station and base station side and accelerate the CSI acquisition procedure.

[1]  A.F. Molisch,et al.  Variable-phase-shift-based RF-baseband codesign for MIMO antenna selection , 2005, IEEE Transactions on Signal Processing.

[2]  Khaled Ben Letaief,et al.  Alternating Minimization Algorithms for Hybrid Precoding in Millimeter Wave MIMO Systems , 2016, IEEE Journal of Selected Topics in Signal Processing.

[3]  Shuangfeng Han,et al.  Large-scale antenna systems with hybrid analog and digital beamforming for millimeter wave 5G , 2015, IEEE Communications Magazine.

[4]  Xiaodai Dong,et al.  Low-Complexity Hybrid Precoding in Massive Multiuser MIMO Systems , 2014, IEEE Wireless Communications Letters.

[5]  Boyu Li,et al.  A Novel Hybrid Beamforming Algorithm With Unified Analog Beamforming by Subspace Construction Based on Partial CSI for Massive MIMO-OFDM Systems , 2016, IEEE Transactions on Communications.

[6]  Michael D. Zoltowski,et al.  Training Sequence Design for Feedback Assisted Hybrid Beamforming in Massive MIMO Systems , 2016, IEEE Transactions on Communications.

[7]  Yingmin Wang,et al.  Multiple-Beam Selection With Limited Feedback for Hybrid Beamforming in Massive MIMO Systems , 2017, IEEE Access.

[8]  Thomas L. Marzetta,et al.  Noncooperative Cellular Wireless with Unlimited Numbers of Base Station Antennas , 2010, IEEE Transactions on Wireless Communications.

[9]  Wei Yu,et al.  Hybrid Digital and Analog Beamforming Design for Large-Scale Antenna Arrays , 2016, IEEE Journal of Selected Topics in Signal Processing.

[10]  Robert W. Heath,et al.  Energy-Efficient Hybrid Analog and Digital Precoding for MmWave MIMO Systems With Large Antenna Arrays , 2015, IEEE Journal on Selected Areas in Communications.

[11]  Robert W. Heath,et al.  Frequency Selective Hybrid Precoding for Limited Feedback Millimeter Wave Systems , 2015, IEEE Transactions on Communications.

[12]  Long Bao Le,et al.  Beamforming for multiuser massive MIMO systems: Digital versus hybrid analog-digital , 2014, 2014 IEEE Global Communications Conference.

[13]  Vincent K. N. Lau,et al.  Impact of CSI Knowledge on the Codebook-Based Hybrid Beamforming in Massive MIMO , 2016, IEEE Transactions on Signal Processing.

[14]  Robert W. Heath,et al.  Limited Feedback Hybrid Precoding for Multi-User Millimeter Wave Systems , 2014, IEEE Transactions on Wireless Communications.

[15]  Robert W. Heath,et al.  Spatially Sparse Precoding in Millimeter Wave MIMO Systems , 2013, IEEE Transactions on Wireless Communications.

[16]  Andreas F. Molisch,et al.  Hybrid Beamforming for Massive MIMO: A Survey , 2017, IEEE Communications Magazine.

[17]  Rose Qingyang Hu,et al.  Key elements to enable millimeter wave communications for 5G wireless systems , 2014, IEEE Wireless Communications.

[18]  Chen Hu,et al.  Channel Estimation for Millimeter-Wave Massive MIMO With Hybrid Precoding Over Frequency-Selective Fading Channels , 2016, IEEE Communications Letters.

[19]  Limin Xiao,et al.  Robust and Low Complexity Hybrid Beamforming for Uplink Multiuser MmWave MIMO Systems , 2016, IEEE Communications Letters.

[20]  Shaohui Sun,et al.  Adaptive Beamforming in TDD-Based Mobile Communication Systems: State of the Art and 5G Research Directions , 2016, IEEE Wireless Communications.

[21]  Sen Wang,et al.  Reference Signals Design for Hybrid Analog and Digital Beamforming , 2014, IEEE Communications Letters.

[22]  Pengfei Xia,et al.  Channel Estimation and Hybrid Precoding for Millimeter-Wave MIMO Systems: A Low-Complexity Overall Solution , 2017, IEEE Access.

[23]  Ahmed Iyanda Sulyman,et al.  Hybrid Precoding-Beamforming Design With Hadamard RF Codebook for mmWave Large-Scale MIMO Systems , 2017, IEEE Access.

[24]  Ming Xiao,et al.  Millimeter Wave Communications for Future Mobile Networks (Guest Editorial), Part I , 2017, IEEE J. Sel. Areas Commun..

[25]  Robert W. Heath,et al.  MIMO Precoding and Combining Solutions for Millimeter-Wave Systems , 2014, IEEE Communications Magazine.

[26]  Robert W. Heath,et al.  Channel Estimation and Hybrid Precoding for Millimeter Wave Cellular Systems , 2014, IEEE Journal of Selected Topics in Signal Processing.

[27]  Luc Vandendorpe,et al.  On the Number of RF Chains and Phase Shifters, and Scheduling Design With Hybrid Analog–Digital Beamforming , 2014, IEEE Transactions on Wireless Communications.