MMSE-based lattice-reduction for near-ML detection of MIMO systems

Recently the use of lattice-reduction for signal detection in multiple antenna systems has been proposed. In this paper, we adopt these lattice-reduction aided schemes to the MMSE criterion. We show that an obvious way to do this is suboptimum and propose an alternative method based on an extended system model. In conjunction with simple successive interference cancellation this scheme almost reaches the performance of maximum-likelihood detection. Furthermore, we demonstrate that the application of sorted QR decomposition (SQRD) as a initialization step can significantly reduce the computational effort associated with lattice-reduction. Thus, the new algorithm clearly outperforms existing methods with comparable complexity.

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

[2]  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).

[3]  Claus-Peter Schnorr,et al.  Lattice Basis Reduction: Improved Practical Algorithms and Solving Subset Sum Problems , 1991, FCT.

[4]  Robert F. H. Fischer,et al.  Low-complexity near-maximum-likelihood detection and precoding for MIMO systems using lattice reduction , 2003, Proceedings 2003 IEEE Information Theory Workshop (Cat. No.03EX674).

[5]  Audra E. Kosh,et al.  Linear Algebra and its Applications , 1992 .

[6]  J. W. Silverstein,et al.  On the empirical distribution of eigenvalues of a class of large dimensional random matrices , 1995 .

[7]  R. Fischer,et al.  Optimum and Sub-Optimum Lattice-Reduction-Aided Detection and Precoding for MIMO Communications , 2022 .

[8]  C. Windpassinger,et al.  Real versus complex-valued equalisation in V-BLAST systems , 2003 .

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

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

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

[12]  U. Fincke,et al.  Improved methods for calculating vectors of short length in a lattice , 1985 .

[13]  Babak Hassibi,et al.  An efficient square-root algorithm for BLAST , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

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

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

[16]  Huan Yao,et al.  Efficient signal, code, and receiver designs for MIMO communication systems , 2003 .

[17]  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).

[18]  Thomas L. Marzetta,et al.  BLAST training : Estimating Channel Characteristics for High-Capacity Space-Time Wireless , 1999 .