Ordered MMSE–SIC via sorted QR decomposition in ill conditioned large-scale MIMO channels

In this work, some aspects of the sorted QR decomposition are addressed for ordered successive–interference–cancellation detection in large-scale antenna systems. An analysis on the sorted QR decomposition behavior, including its impact on the performance of symbol detection in large ill conditioned MIMO channel matrices, has been presented. As the correlation on the channel matrix grows, the sorted QR decomposition may not ensure its requirements, causing misleading symbol estimation. In this context, it is shown that orthogonality condition may be broken, depending on the matrix condition, which comes from propagation errors on the norm updating of the modified Gram–Schimidt method. Numerical results have corroborated our claims, demonstrating the sensitivity of the Gram–Schimidt algorithm, as well as the deterioration of the large-scale MIMO detection performance under highly correlated channels scenarios.

[1]  Dirk Wübben,et al.  Reduced complexity MMSE detection for BLAST architectures , 2003, GLOBECOM '03. IEEE Global Telecommunications Conference (IEEE Cat. No.03CH37489).

[2]  Robert W. Heath,et al.  Five disruptive technology directions for 5G , 2013, IEEE Communications Magazine.

[3]  Ahmed M. Eltawil,et al.  A Best-First Soft/Hard Decision Tree Searching MIMO Decoder for a 4 $\times$ 4 64-QAM System , 2012, IEEE Transactions on Very Large Scale Integration (VLSI) Systems.

[4]  Yuan-Hao Huang,et al.  A modified sorted-QR decomposition algorithm for parallel processing in MIMO detection , 2009, 2009 IEEE International Symposium on Circuits and Systems.

[5]  P. Glenn Gulak,et al.  VLSI implementation of a WiMAX/LTE compliant low-complexity high-throughput soft-output K-Best MIMO detector , 2010, Proceedings of 2010 IEEE International Symposium on Circuits and Systems.

[6]  Ananthanarayanan Chockalingam,et al.  Layered Tabu Search Algorithm for Large-MIMO Detection and a Lower Bound on ML Performance , 2011, IEEE Trans. Commun..

[7]  Taufik Abrão,et al.  Efficient Near-Optimum Detectors for Large MIMO Systems Under Correlated Channels , 2015, Wireless Personal Communications.

[8]  Taufik Abrão,et al.  LR-Aided MIMO Detectors under Correlated and Imperfectly Estimated Channels , 2013, Wireless Personal Communications.

[9]  Poras T. Balsara,et al.  Complex QR Decomposition Using Fast Plane Rotations for MIMO Applications , 2014, IEEE Communications Letters.

[10]  Björn E. Ottersten,et al.  On the complexity of sphere decoding in digital communications , 2005, IEEE Transactions on Signal Processing.

[11]  John R. Barry,et al.  The sorted-QR Chase detector for multiple-input multiple-output channels , 2005, IEEE Wireless Communications and Networking Conference, 2005.

[12]  Thomas L. Marzetta,et al.  Performance of Conjugate and Zero-Forcing Beamforming in Large-Scale Antenna Systems , 2013, IEEE Journal on Selected Areas in Communications.

[13]  Walter Gander,et al.  Algorithms for the QR-Decomposition , 2003 .

[14]  Jie-Bang Yan,et al.  24-Port and 36-Port Antenna Cubes Suitable for MIMO Wireless Communications , 2008, IEEE Transactions on Antennas and Propagation.

[15]  Reinaldo A. Valenzuela,et al.  V-BLAST: an architecture for realizing very high data rates over the rich-scattering wireless channel , 1998, 1998 URSI International Symposium on Signals, Systems, and Electronics. Conference Proceedings (Cat. No.98EX167).

[16]  Tae-Hwan Kim.  Low-Complexity Sorted QR Decomposition for MIMO Systems Based on Pairwise Column Symmetrization , 2014, IEEE Trans. Wirel. Commun..

[17]  Gerald Matz,et al.  Worst- and average-case complexity of LLL lattice reduction in MIMO wireless systems , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[18]  Thomas L. Marzetta,et al.  Argos: practical many-antenna base stations , 2012, Mobicom '12.

[19]  Sudip Mondal,et al.  A novel approach for K-best MIMO detection and its VLSI implementation , 2008, 2008 IEEE International Symposium on Circuits and Systems.

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

[21]  Jing Wang,et al.  A fast recursive sphere decoder with linear storage , 2004, APCC/MDMC '04. The 2004 Joint Conference of the 10th Asia-Pacific Conference on Communications and the 5th International Symposium on Multi-Dimensional Mobile Communications Proceeding.

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

[23]  Xiaoli Ma,et al.  Performance analysis for MIMO systems with lattice-reduction aided linear equalization , 2008, IEEE Transactions on Communications.

[24]  Lajos Hanzo,et al.  Fifty Years of MIMO Detection: The Road to Large-Scale MIMOs , 2015, IEEE Communications Surveys & Tutorials.

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

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

[27]  Nicholas J. Higham,et al.  Stability of Householder QR Factorization for Weighted Least Squares Problems , 2010 .

[28]  K.-D. Kammeyer,et al.  MMSE extension of V-BLAST based on sorted QR decomposition , 2003, 2003 IEEE 58th Vehicular Technology Conference. VTC 2003-Fall (IEEE Cat. No.03CH37484).

[29]  Jun Sun,et al.  Sparsity-Boosted Detection for Large MIMO Systems , 2015, IEEE Communications Letters.

[30]  K. Kammeyer,et al.  Efficient algorithm for decoding layered space-time codes , 2001 .

[31]  Erik G. Larsson,et al.  Scaling Up MIMO , 2013 .