Schnorr-Euchner sphere decoder with statistical pruning for MIMO systems

A near-maximum-likelihood (ML) detection algorithm for spatially multiplexed multiple-input multiple-output (MIMO) systems has been considered. The sphere decoder (SD) is one of the promising techniques to solve the ML problem. However the SD has a loose necessary condition for pruning branches, and it becomes impractical in large dimensional systems. We propose a Schnorr-Euchner SD with statistical pruning (SP-SESD) in order to further reduce complexity with small performance degradation. Squared statistical constraint radius (SCR) and expected partial path metric from unvisited levels are defined from statistics of noises, and two pruning conditions are jointly applied to search tree for detection efficiency. A flexible trade-off between bit error rate (BER) and complexity can be supported by selecting two pruning probabilities in the proposed scheme, and hence one can design various MIMO detectors according to system demands. Simulation results show the proposed SP-SESD requires lower computational complexity than any statistical pruning approaches, although performance degradation is negligible. The proposed algorithm is effective for MIMO systems with any number of antennas.

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

[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]  Babak Hassibi,et al.  Efficient statistical pruning for maximum likelihood decoding , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

[4]  Angela J. Shum,et al.  Drosophila as an unconventional substrate for microfabrication , 2007, SPIE MOEMS-MEMS.

[5]  B. Hassibi,et al.  On the expected complexity of sphere decoding , 2001, Conference Record of Thirty-Fifth Asilomar Conference on Signals, Systems and Computers (Cat.No.01CH37256).

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

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

[8]  B. Shim,et al.  Radius-adaptive sphere decoding via probabilistic tree pruning , 2007, 2007 IEEE 8th Workshop on Signal Processing Advances in Wireless Communications.

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

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