Prevoting Cancellation-Based Detection for Underdetermined MIMO Systems

Various detection methods including the maximum likelihood (ML) detection have been studied for multiple-input multiple-output (MIMO) systems. While it is usually assumed that the number of independent data symbols, , to be transmitted by multiple antennas simultaneously is smaller than or equal to that of the receive antennas, , in most cases, there could be cases where , which results in underdetermined MIMO systems. In this paper, we employ the prevoting cancellation based detection for underdetermined MIMO systems and show that the proposed detectors can exploit a full receive diversity. Furthermore, the prevoting vector selection criteria for the proposed detectors are taken into account to improve performance further. We also show that our proposed scheme has a lower computational complexity compared to existing approaches, in particular when slow fading MIMO channels are considered.

[1]  László Lovász,et al.  Factoring polynomials with rational coefficients , 1982 .

[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]  Jinho Choi.  Adaptive and Iterative Signal Processing in Communications , 2006 .

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

[5]  John R. Barry,et al.  Partial decision-feedback detection for multiple-input multiple-output channels , 2004, 2004 IEEE International Conference on Communications (IEEE Cat. No.04CH37577).

[6]  Jinho Choi,et al.  Lattice Reduction Aided Detection for Underdetermined MIMO Systems: A Pre-Voting Cancellation Approach , 2010, 2010 IEEE 71st Vehicular Technology Conference.

[7]  Zhi-Quan Luo,et al.  Parallel detection for V-BLAST system , 2002, 2002 IEEE International Conference on Communications. Conference Proceedings. ICC 2002 (Cat. No.02CH37333).

[8]  M. O. Damen,et al.  Generalised sphere decoder for asymmetrical space-time communication architecture , 2000 .

[9]  Gregory W. Wornell,et al.  Lattice-reduction-aided detectors for MIMO communication systems , 2002, Global Telecommunications Conference, 2002. GLOBECOM '02. IEEE.

[10]  Zongkai Yang,et al.  A new approach for fast generalized sphere decoding in MIMO systems , 2005, IEEE Signal Process. Lett..

[11]  Fumiyuki Adachi,et al.  User Selection Criteria for Multiuser Systems With Optimal and Suboptimal LR Based Detectors , 2010, IEEE Transactions on Signal Processing.

[12]  Antonia Maria Tulino,et al.  Performance of space-time codes for a large number of antennas , 2002, IEEE Trans. Inf. Theory.

[13]  A. Edelman.  Eigenvalues and condition numbers of random matrices , 1988 .

[14]  Reinaldo A. Valenzuela,et al.  Simplified processing for high spectral efficiency wireless communication employing multi-element arrays , 1999, IEEE J. Sel. Areas Commun..

[15]  Emanuele Viterbo,et al.  A universal lattice code decoder for fading channels , 1999, IEEE Trans. Inf. Theory.

[16]  Kai-Kit Wong,et al.  Efficient Near Maximum-Likelihood Detection for Underdetermined MIMO Antenna Systems Using a Geometrical Approach , 2007, EURASIP J. Wirel. Commun. Netw..

[17]  Paul D. Alexander,et al.  List detection for multi-access channels , 2002, Global Telecommunications Conference, 2002. GLOBECOM '02. IEEE.

[18]  Amir K. Khandani,et al.  LLLl lattice-basis reduction achieves the maximum diversity in MIMO systems , 2005, Proceedings. International Symposium on Information Theory, 2005. ISIT 2005..

[19]  C. Tellambura,et al.  An efficient generalized sphere decoder for rank-deficient MIMO systems , 2004 .

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

[21]  Xiaohua Yang,et al.  An Efficient Tree Search Decoder with Column Reordering for Underdetermined MIMO Systems , 2007, IEEE GLOBECOM 2007 - IEEE Global Telecommunications Conference.

[22]  Dirk Wübben,et al.  Near-maximum-likelihood detection of MIMO systems using MMSE-based lattice reduction , 2004, 2004 IEEE International Conference on Communications (IEEE Cat. No.04CH37577).

[23]  Robert W. Heath,et al.  Space-time Chase decoding , 2005, IEEE Transactions on Wireless Communications.

[24]  Tho Le-Ngoc,et al.  A low-complexity Generalized Sphere Decoding Approach for Underdetermined MIMO Systems , 2006, 2006 IEEE International Conference on Communications.

[25]  Amir K. Khandani,et al.  LLL Reduction Achieves the Receive Diversity in MIMO Decoding , 2006, IEEE Transactions on Information Theory.

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

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

[28]  Jinho Choi,et al.  Partial MAP-Based List Detection for MIMO Systems , 2009, IEEE Transactions on Vehicular Technology.

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

[30]  Mahesh K. Varanasi,et al.  Multiuser detection for overloaded CDMA systems , 2003, IEEE Transactions on Information Theory.

[31]  Lutz H.-J. Lampe,et al.  From Lattice-Reduction-Aided Detection Towards Maximum-Likelihood Detection in MIMO Systems , .

[32]  Wai Ho Mow,et al.  Complex Lattice Reduction Algorithm for Low-Complexity Full-Diversity MIMO Detection , 2009, IEEE Transactions on Signal Processing.

[33]  Wai Ho Mow,et al.  Near maximum likelihood detection schemes for wireless MIMO systems , 2004, IEEE Transactions on Wireless Communications.

[34]  Babak Hassibi,et al.  On the sphere-decoding algorithm I. Expected complexity , 2005, IEEE Transactions on Signal Processing.

[35]  Georgios B. Giannakis,et al.  A simple and general parameterization quantifying performance in fading channels , 2003, IEEE Trans. Commun..

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