Soft-Heuristic Detectors for Large MIMO Systems

We propose low-complexity detectors for large MIMO systems with BPSK or QAM constellations. These detectors work at the bit level and consist of three stages. In the first stage, maximum likelihood decisions on certain bits are made in an efficient way. In the second stage, soft values for the remaining bits are calculated. In the third stage, these remaining bits are detected by means of a heuristic programming method for high-dimensional optimization that uses the soft values (“soft-heuristic” algorithm). We propose two soft-heuristic algorithms with different performance and complexity. We also consider a feedback of the results of the third stage for computing improved soft values in the second stage. Simulation results demonstrate that, for large MIMO systems, our detectors can outperform state-of-the-art detectors based on nulling and canceling, semidefinite relaxation, and likelihood ascent search.

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

[2]  Gerald Matz,et al.  Fundamentals of Time-Varying Communication Channels , 2011 .

[3]  Lajos Hanzo,et al.  Soft-Information Assisted Near-Optimum Nonlinear Detection for BLAST-type Space Division Multiplexing OFDM Systems , 2007, IEEE Transactions on Wireless Communications.

[4]  Claus-Peter Schnorr,et al.  Lattice basis reduction: Improved practical algorithms and solving subset sum problems , 1991, FCT.

[5]  Andreas F. Molisch,et al.  Nonorthogonal pulseshapes for multicarrier communications in doubly dispersive channels , 1998, IEEE J. Sel. Areas Commun..

[6]  B. Rajan,et al.  Improved large-MIMO detection based on damped belief propagation , 2010, 2010 IEEE Information Theory Workshop on Information Theory (ITW 2010, Cairo).

[7]  Gerald Matz,et al.  Simulation of wideband mobile radio channels using subsampled ARMA models and multistage interpolation , 2001, Proceedings of the 11th IEEE Signal Processing Workshop on Statistical Signal Processing (Cat. No.01TH8563).

[8]  Lajos Hanzo,et al.  Multiuser MIMO-OFDM for Next-Generation Wireless Systems , 2007, Proceedings of the IEEE.

[9]  Geert Leus,et al.  OFDM Communications over Time-Varying Channels , 2011 .

[10]  F. Hlawatsch,et al.  Dynamic Nulling-and-Canceling for Efficient Near-ML Decoding of MIMO Systems , 2006, IEEE Transactions on Signal Processing.

[11]  Bernd Freisleben,et al.  Greedy and Local Search Heuristics for Unconstrained Binary Quadratic Programming , 2002, J. Heuristics.

[12]  Dirk Wübben,et al.  Lattice Reduction , 2011, IEEE Signal Processing Magazine.

[13]  John R. Barry,et al.  The Chase Family of Detection Algorithms for Multiple-Input Multiple-Output Channels , 2008, IEEE Transactions on Signal Processing.

[14]  Muhammad Naeem,et al.  An Application of GA for Symbol Detection in MIMO Communication Systems , 2007, Third International Conference on Natural Computation (ICNC 2007).

[15]  David Chase,et al.  Class of algorithms for decoding block codes with channel measurement information , 1972, IEEE Trans. Inf. Theory.

[16]  B. Sundar Rajan,et al.  A Low-complexity near-ML performance achieving algorithm for large MIMO detection , 2008, 2008 IEEE International Symposium on Information Theory.

[17]  Philip Schniter,et al.  Equalization of Time-Varying Channels , 2011 .

[18]  Khaled Fazel,et al.  Multi-Carrier and Spread Spectrum Systems , 2003 .

[19]  Erik G. Larsson,et al.  SUMIS: A Near-Optimal Soft-Ouput MIMO Detector at Low and Fixed Complexity , 2012, ArXiv.

[20]  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).

[21]  Jacob Benesty,et al.  A fast recursive algorithm for optimum sequential signal detection in a BLAST system , 2003, IEEE Trans. Signal Process..

[22]  B. Sundar Rajan,et al.  Random-Restart Reactive Tabu Search Algorithm for Detection in Large-MIMO Systems , 2010, IEEE Communications Letters.

[23]  Jinho Choi.  Iterative receivers with bit-level cancellation and detection for MIMO-BICM systems , 2005, IEEE Transactions on Signal Processing.

[24]  B. Sundar Rajan,et al.  High-Rate Space–Time Coded Large-MIMO Systems: Low-Complexity Detection and Channel Estimation , 2008, IEEE Journal of Selected Topics in Signal Processing.

[25]  Zhi-Quan Luo,et al.  Semidefinite Relaxation of Quadratic Optimization Problems , 2010, IEEE Signal Processing Magazine.

[26]  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.

[27]  Taufik Abrão,et al.  S/MIMO MC-CDMA Heuristic Multiuser Detectors Based on Single-Objective Optimization , 2010, Wirel. Pers. Commun..

[28]  B. Hassibi.  A fast square-root implementation for BLAST , 2000, Conference Record of the Thirty-Fourth Asilomar Conference on Signals, Systems and Computers (Cat. No.00CH37154).

[29]  Stephan M. Winkler,et al.  Genetic Algorithms and Genetic Programming - Modern Concepts and Practical Applications , 2009 .

[30]  Kengo Katayama,et al.  Solving Large Binary Quadratic Programming Problems by Effective Genetic Local Search Algorithm , 2000, GECCO.

[31]  Ghassan Kawas Kaleh.  Channel Equalization for Block Transmission Systems , 1995, IEEE J. Sel. Areas Commun..

[32]  Per Ödling,et al.  Making MLSD decisions by thresholding the matched filter output , 2000, IEEE Trans. Commun..

[33]  Alexander Vardy,et al.  Closest point search in lattices , 2002, IEEE Trans. Inf. Theory.

[34]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..

[35]  Erwin Riegler,et al.  Low-complexity detection for large MIMO systems using partial ML detection and genetic programming , 2012, 2012 IEEE 13th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC).