Construction of Minimum Euclidean Distance MIMO Precoders and Their Lattice Classifications

This correspondence deals with the construction of minimum Euclidean distance precoders for multiple-input multiple-output (MIMO) systems with up to four transmit antennas. By making use of a state-of-the-art technique for optimization over the unitary group, we can numerically optimize the MIMO precoders. The correspondence then proceeds by identifying the obtained precoders as well-known lattices (square Z2, Schläfli D4, D6, Gosset E8). With three transmit antennas, the results are slightly different compared with other numbers of transmit antennas since the obtained precoder is not an instance of the densest 6-dimensional lattice. The overall conclusions of the correspondence are that the found precoders for MIMO transmission are highly structured and that, even with small constellations, lattice theory can be used for the design of MIMO precoders.

[1]  Gilles Burel,et al.  Extension of the MIMO Precoder Based on the Minimum Euclidean Distance: A Cross-Form Matrix , 2008, IEEE Journal of Selected Topics in Signal Processing.

[2]  Olivier Berder,et al.  Minimum Distance Based Precoder for MIMO-OFDM Systems Using a 16-QAM Modulation , 2009, 2009 IEEE International Conference on Communications.

[3]  Emre Telatar,et al.  Capacity of Multi-antenna Gaussian Channels , 1999, Eur. Trans. Telecommun..

[4]  Antonia Maria Tulino,et al.  Mercury/waterfilling: optimum power allocation with arbitrary input constellations , 2005, Proceedings. International Symposium on Information Theory, 2005. ISIT 2005..

[5]  N. J. A. Sloane,et al.  Sphere Packings, Lattices and Groups , 1987, Grundlehren der mathematischen Wissenschaften.

[6]  Yi Jiang,et al.  MIMO Transceiver Design via Majorization Theory , 2007, Found. Trends Commun. Inf. Theory.

[7]  R. Fletcher Practical Methods of Optimization , 1988 .

[8]  Visa Koivunen,et al.  Conjugate gradient algorithm for optimization under unitary matrix constraint , 2009, Signal Process..

[9]  Olivier Berder,et al.  Optimal minimum distance-based precoder for MIMO spatial multiplexing systems , 2004, IEEE Transactions on Signal Processing.

[10]  Miguel R. D. Rodrigues,et al.  MIMO Gaussian Channels With Arbitrary Inputs: Optimal Precoding and Power Allocation , 2010, IEEE Transactions on Information Theory.

[11]  Olivier Berder,et al.  3-D Minimum Euclidean Distance Based Sub-Optimal Precoder for MIMO Spatial Multiplexing Systems , 2010, 2010 IEEE International Conference on Communications.

[12]  Roger Fletcher,et al.  Practical methods of optimization; (2nd ed.) , 1987 .

[13]  Fredrik Rusek,et al.  Design of close to optimal Euclidean distance MIMO-precoders , 2009, 2009 IEEE International Symposium on Information Theory.

[14]  Visa Koivunen,et al.  Steepest Descent Algorithms for Optimization Under Unitary Matrix Constraint , 2008, IEEE Transactions on Signal Processing.

[15]  Antonia Maria Tulino,et al.  Optimum power allocation for parallel Gaussian channels with arbitrary input distributions , 2006, IEEE Transactions on Information Theory.

[16]  Anna Scaglione,et al.  Optimal designs for space-time linear precoders and decoders , 2002, IEEE Trans. Signal Process..

[17]  Daniel Pérez Palomar,et al.  On optimal precoding in linear vector Gaussian channels with arbitrary input distribution , 2009, 2009 IEEE International Symposium on Information Theory.

[18]  Ian J. Wassell,et al.  Recovery of a lattice generator matrix from its gram matrix for feedback and precoding in MIMO , 2010, 2010 4th International Symposium on Communications, Control and Signal Processing (ISCCSP).