Hybrid Beamforming via the Kronecker Decomposition for the Millimeter-Wave Massive MIMO Systems

Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) seamlessly integrates two wireless technologies, mmWave communications and massive MIMO, which provides spectrums with tens of GHz of total bandwidth and supports aggressive space division multiple access using large-scale arrays. Though it is a promising solution for next-generation systems, the realization of mmWave massive MIMO faces several practical challenges. In particular, implementing massive MIMO in the digital domain requires hundreds to thousands of radio frequency chains and analog-to-digital converters matching the number of antennas. Furthermore, designing these components to operate at the mmWave frequencies is challenging and costly. These motivated the recent development of the hybrid-beamforming architecture, where MIMO signal processing is divided for separate implementation in the analog and digital domains, called the analog and digital beamforming, respectively. Analog beamforming using a phase array introduces uni-modulus constraints on the beamforming coefficients. They render the conventional MIMO techniques unsuitable and call for new designs. In this paper, we present a systematic design framework for hybrid beamforming for multi-cell multiuser massive MIMO systems over mmWave channels characterized by sparse propagation paths. The framework relies on the decomposition of analog beamforming vectors and path observation vectors into Kronecker products of factors being uni-modulus vectors. Exploiting properties of Kronecker mixed products, different factors of the analog beamformer are designed for either nulling interference paths or coherently combining data paths. Furthermore, a channel estimation scheme is designed for enabling the proposed hybrid beamforming. The scheme estimates the angles-of-arrival (AoA) of data and interference paths by analog beam scanning and data-path gains by analog beam steering. The performance of the channel estimation scheme is analyzed. In particular, the AoA spectrum resulting from beam scanning, which displays the magnitude distribution of paths over the AoA range, is derived in closed form. It is shown that the inter-cell interference level diminishes inversely with the array size, the square root of pilot sequence length, and the spatial separation between paths, suggesting different ways of tackling pilot contamination.

[1]  Robert W. Heath,et al.  Compressed sensing based multi-user millimeter wave systems: How many measurements are needed? , 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

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

[3]  James V. Krogmeier,et al.  Millimeter Wave Beamforming for Wireless Backhaul and Access in Small Cell Networks , 2013, IEEE Transactions on Communications.

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

[5]  Alle-Jan van der Veen,et al.  Analog Beamforming in MIMO Communications With Phase Shift Networks and Online Channel Estimation , 2010, IEEE Transactions on Signal Processing.

[6]  Yik-Chung Wu,et al.  DoA Estimation and Capacity Analysis for 3-D Millimeter Wave Massive-MIMO/FD-MIMO OFDM Systems , 2016, IEEE Transactions on Wireless Communications.

[7]  Charles R. Johnson,et al.  Matrix analysis , 1985, Statistical Inference for Engineers and Data Scientists.

[8]  Theodore S. Rappaport,et al.  Radiocommunications , 1967, Revue Internationale de la Croix-Rouge.

[9]  Hucheng Sun,et al.  60-GHz Circularly Polarized U-Slot Patch Antenna Array on LTCC , 2013, IEEE Transactions on Antennas and Propagation.

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

[11]  Lajos Hanzo,et al.  Iterative Matrix Decomposition Aided Block Diagonalization for mm-Wave Multiuser MIMO Systems , 2017, IEEE Transactions on Wireless Communications.

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

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

[14]  R. O. Schmidt,et al.  Multiple emitter location and signal Parameter estimation , 1986 .

[15]  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.

[16]  Martin Haardt,et al.  Zero-forcing methods for downlink spatial multiplexing in multiuser MIMO channels , 2004, IEEE Transactions on Signal Processing.

[17]  Akbar M. Sayeed,et al.  Beamspace MIMO for Millimeter-Wave Communications: System Architecture, Modeling, Analysis, and Measurements , 2013, IEEE Transactions on Antennas and Propagation.

[18]  David Gesbert,et al.  A Coordinated Approach to Channel Estimation in Large-Scale Multiple-Antenna Systems , 2012, IEEE Journal on Selected Areas in Communications.

[19]  Theodore S. Rappaport,et al.  Millimeter Wave Mobile Communications for 5G Cellular: It Will Work! , 2013, IEEE Access.

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

[21]  Kyungwhoon Cheun,et al.  Millimeter-wave beamforming as an enabling technology for 5G cellular communications: theoretical feasibility and prototype results , 2014, IEEE Communications Magazine.

[22]  J. Jost Riemannian geometry and geometric analysis , 1995 .

[23]  Robert W. Heath,et al.  An Overview of Signal Processing Techniques for Millimeter Wave MIMO Systems , 2015, IEEE Journal of Selected Topics in Signal Processing.

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

[25]  Chin-Sean Sum,et al.  Beam Codebook Based Beamforming Protocol for Multi-Gbps Millimeter-Wave WPAN Systems , 2009, GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference.

[26]  Robert W. Heath,et al.  Hybrid precoding for millimeter wave cellular systems with partial channel knowledge , 2013, 2013 Information Theory and Applications Workshop (ITA).

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

[28]  Kaibin Huang,et al.  Analog Spatial Cancellation for Tackling the Near-Far Problem in Wirelessly Powered Communications , 2015, IEEE Journal on Selected Areas in Communications.

[29]  David Gesbert,et al.  From theory to practice: an overview of MIMO space-time coded wireless systems , 2003, IEEE J. Sel. Areas Commun..

[30]  Jeffrey G. Andrews,et al.  Modeling and Analyzing Millimeter Wave Cellular Systems , 2016, IEEE Transactions on Communications.

[31]  Upamanyu Madhow,et al.  Compressive tracking with 1000-element arrays: A framework for multi-Gbps mm wave cellular downlinks , 2012, 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[32]  Jürgen Jost,et al.  Riemannian Geometry and Geometric Analysis, 5th Edition , 2008 .

[33]  W. Root,et al.  An introduction to the theory of random signals and noise , 1958 .

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

[35]  Robert W. Heath,et al.  Equal gain transmission in multiple-input multiple-output wireless systems , 2002, Global Telecommunications Conference, 2002. GLOBECOM '02. IEEE.

[36]  Andrew R. Nix,et al.  Application of compressive sensing in sparse spatial channel recovery for beamforming in mmWave outdoor systems , 2014, 2014 IEEE Wireless Communications and Networking Conference (WCNC).

[37]  Zhi-Quan Luo,et al.  An iteratively weighted MMSE approach to distributed sum-utility maximization for a MIMO interfering broadcast channel , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[38]  Wei Yu,et al.  Multi-Cell MIMO Cooperative Networks: A New Look at Interference , 2010, IEEE Journal on Selected Areas in Communications.

[39]  Jeffrey G. Andrews,et al.  What Will 5G Be? , 2014, IEEE Journal on Selected Areas in Communications.

[40]  M. Abramowitz,et al.  Handbook of Mathematical Functions With Formulas, Graphs and Mathematical Tables (National Bureau of Standards Applied Mathematics Series No. 55) , 1965 .

[41]  Thomas L. Marzetta,et al.  Inter-Cell Interference in Noncooperative TDD Large Scale Antenna Systems , 2013, IEEE Journal on Selected Areas in Communications.