[1] Liu Jinzhu, Xing Song, Shen Lianfeng, et al. Ordered successive noise projection cancellation algorithmfor dual lattice-reduction-aided MIMO detection [J]. Journal of Southeast University (English Edition), 2013, 29 (3): 229-234. [doi:10.3969/j.issn.1003-7985.2013.03.001]

Copy

Ordered successive noise projection cancellation algorithmfor dual lattice-reduction-aided MIMO detection()

Share：

Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

29

Issue:

2013 3

Page:

229-234

Research Field:

Information and Communication Engineering

Publishing date:

2013-09-20

Info

Title:

Ordered successive noise projection cancellation algorithmfor dual lattice-reduction-aided MIMO detection

Author(s):

Liu Jinzhu¹;  2;  Xing Song³;  Shen Lianfeng¹

¹National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China
²School of Electronic & Information Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China
³Department of Information Systems, California State University, Los Angeles, CA 90032, USA

Keywords:

ordered successive noise projection cancellation(OSNPC);  dual lattice reduction(DLR);  multi-input multi-output(MIMO)detection;  ordered successive interference cancellation(OSIC)

PACS:

TN911

DOI:

10.3969/j.issn.1003-7985.2013.03.001

Abstract:

A novel nonlinear multi-input multi-output(MIMO)detection algorithm is proposed, which is referred to as an ordered successive noise projection cancellation(OSNPC)algorithm. It is capable of improving the computation performance of the MIMO detector with the conventional ordered successive interference cancellation(OSIC)algorithm. In contrast to the OSIC in which the known interferences in the input signal vector are successively cancelled, the OSNPC successively cancels the known noise projections from the decision statistic vector. Analysis indicates that the OSNPC is equivalent to the OSIC in error performance, but it has significantly less complexity in computation. Furthermore, when the OSNPC is applied to the MIMO detection with the preprocessing of dual lattice reduction(DLR), the computational complexity of the proposed OSNPC-based DLR-aided detector is further reduced due to the avoidance of the inverse of the reduced basis of the dual lattice in computation, compared to that of the OSIC-based one. Simulation results validate the theoretical conclusions with regard to both the performance and complexity of the proposed MIMO detection scheme.

References:

[1] Viterbo E, Boutros J. A universal lattice code decoder for fading channels [J]. IEEE Transactions on Information Theory, 1999, 45(7): 1639-1642.
[2] Hanzo L, Münster M, Choi B J, et al. OFDM and MC-CDMA for broadband multi-user communications, WLANs and broadcasting [M]. John Wiley & Sons, 2003.
[3] Wolniansky P W, Foschini G J, Golden G D, et al. V-BLAST: an architecture for realizing very high data rates over the rich-scattering wireless channel [C]//1998 URSI International Symposium on Signals, Systems, and Electronics. Pisa, Italy, 1998: 295-300.
[4] Li X, Huang H C, Lozano A, et al. Reduced-complexity detection algorithms for systems using multi-element arrays [C]//Proceedings of IEEE Global Telecommunications. San Francisco, CA, USA, 2000:1072-1076.
[5] Gan Y H, Ling C, Mow W H. Complex lattice reduction algorithm for low-complexity full-diversity MIMO detection [J]. IEEE Transactions on Signal Processing, 2009, 57(7): 2701-2710.
[6] Wübben D, Seethaler D, Jaldén J, et al. Lattice reduction: a survey with applications in wireless communications [J]. IEEE Signal Processing Magazine, 2011, 28(3):70-91.
[7] Ling C, Mow W H, Gan L. Dual-lattice ordering and partial lattice reduction for SIC-based MIMO detection [J]. IEEE Journal of Selected Topics in Signal Processing, 2009, 3(6): 975-985.
[8] Lenstra A K, Lenstra H W Jr, Lovász L. Factoring polynomials with rational coefficients [J]. Math Annual, 1982, 261(4): 513-534.
[9] Seysen M. Simultaneous reduction of a lattice basis and its reciprocal basis [J]. Combinatorica, 1993, 13(3): 363-376.
[10] Ben-Israel A, Greville T N E. Generalized inverses: theory and applications [M]. New York: Springer-Verlag, 2003.
[11] Kusume K, Joham M, Utschick W. Cholesky factorization with symmetric permutation applied to detecting and precoding spatially multiplexed data streams [J]. IEEE Transactions on Signal Processing, 2007, 55(6): 3089-3103.

Memo

Memo:

Biographies: Liu Jinzhu(1963—), male, graduate; Shen Lianfeng(corresponding author), male, professor, lfshen@seu.edu.cn.
Foundation items: The National Science and Technology Major Project(No.2012ZX03004005-003), the National Natural Science Foundation of China(No.61171081, 61201175), the Innovation Technology Fund of Jiangsu Province(No.BC2012006).
Citation: Liu Jinzhu, Xing Song, Shen Lianfeng.Ordered successive noise projection cancellation algorithm for dual lattice-reduction-aided MIMO detection[J].Journal of Southeast University(English Edition), 2013, 29(3):229-234.[doi:10.3969/j.issn.1003-7985.2013.03.001]

Common functions