Low-complexity dominance-based sphere decoder for MIMO systems

The sphere decoder (SD) is an attractive low-complexity alternative to maximum likelihood (ML) detection in a variety of communication systems. It is also employed in multiple-input multiple-output (MIMO) systems where the computational complexity of the optimum detector grows exponentially with the number of transmit antennas. We propose an enhanced version of the SD based on an additional cost function derived from conditions on worst case interference that we call dominance conditions. The proposed detector, the king sphere decoder (KSD), has a computational complexity that results to be not larger than the complexity of the sphere decoder and numerical simulations show that the complexity reduction is usually quite significant.

[1]  Teruyuki Miyajima,et al.  On the Multiuser Detection Using a Neural Network in Code-Division Multiple-Access Communications (Special Issue on Spread Spectrum Techniques and Applications) , 1993 .

[2]  BolcskeiH.,et al.  Soft-output sphere decoding , 2008 .

[3]  Helmut Bölcskei,et al.  Outdoor MIMO wireless channels: models and performance prediction , 2002, IEEE Trans. Commun..

[4]  Wai Ho Mow.  Universal Lattice Decoding , 2005 .

[5]  Bernard Fino,et al.  Multiuser detection: , 1999, Ann. des Télécommunications.

[6]  Yi Sun.  A family of likelihood ascent search multiuser detectors: an upper bound of bit error rate and a lower bound of asymptotic multiuser efficiency , 2009, IEEE Transactions on Communications.

[7]  Westervelt,et al.  Dynamics of iterated-map neural networks. , 1989, Physical review. A, General physics.

[8]  Mohamed Oussama Damen,et al.  Lattice code decoder for space-time codes , 2000, IEEE Communications Letters.

[9]  J J Hopfield,et al.  Neurons with graded response have collective computational properties like those of two-state neurons. , 1984, Proceedings of the National Academy of Sciences of the United States of America.

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

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

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

[13]  Reinaldo A. Valenzuela,et al.  Keyholes, correlations, and capacities of multielement transmit and receive antennas , 2002, IEEE Trans. Wirel. Commun..

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

[15]  Steven D. Galbraith,et al.  Mathematics of Public Key Cryptography: Lattice basis reduction , 2012 .

[16]  Elias S. Manolakos,et al.  A hybrid digital signal processing-neural network CDMA multiuser detection scheme , 1996 .

[17]  Björn E. Ottersten,et al.  On the complexity of sphere decoding in digital communications , 2005, IEEE Transactions on Signal Processing.

[18]  Wai Ho Mow.  Maximum likelihood sequence estimation from the lattice viewpoint , 1994, IEEE Trans. Inf. Theory.

[19]  Per Ödling,et al.  Making MLSD decisions by thresholding the matched filter output , 2000, IEEE Trans. Commun..

[20]  Wai Ho Mow,et al.  Universal lattice decoding: principle and recent advances , 2003, Wirel. Commun. Mob. Comput..

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

[22]  Samuel Dolinar,et al.  A* decoding of block codes , 1996, IEEE Trans. Commun..

[23]  van A Allert Zelst,et al.  A single coefficient spatial correlation model for multiple-input multiple-output (MIMO) radio channels , 2002 .

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

[25]  Joshua Alspector,et al.  Implementing the Optimal CDMA Multiuser Detector with Hopfield Neural Networks , 2013 .

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

[27]  J.E. Mazo,et al.  Digital communications , 1985, Proceedings of the IEEE.

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

[29]  Francesco Palmieri,et al.  Tree-search ML detection for underdetermined MIMO systems with M-PSK constellations , 2010, 2010 7th International Symposium on Wireless Communication Systems.

[30]  Francesco Palmieri,et al.  A tree-search algorithm for ML decoding in underdetermined MIMO systems , 2009, 2009 6th International Symposium on Wireless Communication Systems.

[31]  Giuseppe Caire,et al.  A unified framework for tree search decoding: rediscovering the sequential decoder , 2005, IEEE 6th Workshop on Signal Processing Advances in Wireless Communications, 2005..

[32]  Krishna R. Pattipati,et al.  Fast optimal and suboptimal any-time algorithms for CDMA multiuser detection based on branch and bound , 2004, IEEE Transactions on Communications.

[33]  Elias S. Manolakos,et al.  Hopfield neural network implementation of the optimal CDMA multiuser detector , 1996, IEEE Trans. Neural Networks.

[34]  Jürgen Lindner,et al.  A survey of multiuser/multisubchannel detection schemes based on recurrent neural networks , 2002, Wirel. Commun. Mob. Comput..

[35]  Helmut Bölcskei,et al.  Soft-output sphere decoding: algorithms and VLSI implementation , 2008, IEEE Journal on Selected Areas in Communications.

[36]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[37]  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..

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

[39]  Gorazd Kandus,et al.  MIMO: Wireless Communications , 2008, Encyclopedia of Wireless and Mobile Communications.

[40]  Daniel Axehill,et al.  A Low-Complexity High-Performance Preprocessing Algorithm for Multiuser Detection Using Gold Sequences , 2008, IEEE Transactions on Signal Processing.