On the scale effects oriented MIMO detector: Diversity order, worst-case unit complexity and scale effects

In this paper, the insight into the scale effects oriented MIMO detector (SEOD) which is developed for the MIMO system that has to detect signals of multiple users, e.g., the multiple-antenna base-station (BS) system, is presented. The diversity order, complexity and scale effects of SEOD are investigated. The main contributions of this work can be listed as follows: (1) applying the concept of scale effects in microeconomics to MIMO detection and establishing the corresponding model for the analysis of scale effects; (2) presenting the complete proof of the existence of scale effects in our proposed SEOD scheme; (3) demonstrating that the scale effects can be utilized to realize MIMO detection that has the optimal performance in the sense of diversity order while keeping the worst-case unit complexity be polynomial in multiple-user system. Through the computer simulation, we obtain the numerical results which support the theoretical analysis.

[1]  Rohit U. Nabar,et al.  Introduction to Space-Time Wireless Communications , 2003 .

[2]  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).

[3]  Zhan Guo,et al.  Algorithm and implementation of the K-best sphere decoding for MIMO detection , 2006, IEEE Journal on Selected Areas in Communications.

[4]  Mohamed Oussama Damen,et al.  Lattice sequential decoder for coded MIMO channel: Performance and complexity analysis , 2010, 2010 IEEE International Symposium on Information Theory.

[5]  Lizhong Zheng,et al.  Diversity and multiplexing: a fundamental tradeoff in multiple-antenna channels , 2003, IEEE Trans. Inf. Theory.

[6]  Norman C. Beaulieu,et al.  Linear threaded algebraic space-time constellations , 2003, IEEE Trans. Inf. Theory.

[7]  Amir K. Khandani,et al.  On the Complexity of Decoding Lattices Using the Korkin-Zolotarev Reduced Basis , 1998, IEEE Trans. Inf. Theory.

[8]  Cong Xiong,et al.  A simplified fixed-complexity sphere decoder for V-BLAST systems , 2009, IEEE Communications Letters.

[9]  Helmut Bölcskei,et al.  On the Complexity Distribution of Sphere Decoding , 2011, IEEE Transactions on Information Theory.

[10]  Gerald Matz,et al.  Low-Complexity and Full-Diversity MIMO Detection Based on Condition Number Thresholding , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[11]  Björn E. Ottersten,et al.  Full Diversity Detection in MIMO Systems with a Fixed-Complexity Sphere Decoder , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[12]  Jacob Goldberger,et al.  MIMO Detection for High-Order QAM Based on a Gaussian Tree Approximation , 2010, IEEE Transactions on Information Theory.

[13]  S. Roger,et al.  Combined K-Best sphere decoder based on the channel matrix condition number , 2008, 2008 3rd International Symposium on Communications, Control and Signal Processing.

[14]  Joohwan Chun,et al.  ML Symbol Detection Based on the Shortest Path Algorithm for MIMO Systems , 2007, IEEE Transactions on Signal Processing.

[15]  Dirk Wübben,et al.  MMSE-based lattice-reduction for near-ML detection of MIMO systems , 2004, ITG Workshop on Smart Antennas (IEEE Cat. No.04EX802).

[16]  B. Hassibi,et al.  On the expected complexity of sphere decoding , 2001, Conference Record of Thirty-Fifth Asilomar Conference on Signals, Systems and Computers (Cat.No.01CH37256).

[17]  B. Lankl,et al.  Low-effort near maximum likelihood MIMO detection with optimum hardware resource exploitation , 2007 .

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

[19]  Yuan Qi,et al.  Scale effects oriented MIMO detector , 2011, 2011 IEEE 22nd International Symposium on Personal, Indoor and Mobile Radio Communications.

[20]  Anthony Man-Cho So On the performance of semidefinite relaxation MIMO detectors for QAM constellations , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[21]  I. S. Gradshteyn,et al.  Table of Integrals, Series, and Products , 1976 .

[22]  Björn E. Ottersten,et al.  On the limits of sphere decoding , 2005, Proceedings. International Symposium on Information Theory, 2005. ISIT 2005..

[23]  John M. Cioffi,et al.  Combined ML and DFE decoding for the V-BLAST system , 2000, 2000 IEEE International Conference on Communications. ICC 2000. Global Convergence Through Communications. Conference Record.

[24]  John S. Thompson,et al.  Performance Analysis of a Fixed-Complexity Sphere Decoder in High-Dimensional Mimo Systems , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[25]  Qing Wang,et al.  Wireless network cloud: Architecture and system requirements , 2010, IBM J. Res. Dev..

[26]  Giuseppe Caire,et al.  On maximum-likelihood detection and the search for the closest lattice point , 2003, IEEE Trans. Inf. Theory.

[27]  Joakim Jaldén,et al.  Sphere Decoding Complexity Exponent for Decoding Full-Rate Codes Over the Quasi-Static MIMO Channel , 2011, IEEE Transactions on Information Theory.

[28]  G. Marsaglia,et al.  A New Class of Random Number Generators , 1991 .

[29]  Helmut Bölcskei,et al.  Performance and Complexity Analysis of Infinity-Norm Sphere-Decoding , 2010, IEEE Transactions on Information Theory.

[30]  Shivkumar Kalyanaraman,et al.  Unlocking wireless performance with co-operation in co-located base station pools , 2010, 2010 Second International Conference on COMmunication Systems and NETworks (COMSNETS 2010).