Lattice basis reduction: improved practical algorithms and solving subset sum problems
Mathematical Programming: Series A and B
Matrix computations (3rd ed.)
Complexity of Lattice Problems
Complexity of Lattice Problems
Improved algorithms for integer programming and related lattice problems
STOC '83 Proceedings of the fifteenth annual ACM symposium on Theory of computing
An efficient square-root algorithm for BLAST
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 02
On the sphere-decoding algorithm I. Expected complexity
IEEE Transactions on Signal Processing - Part I
Speeding up the Sphere Decoder With H∞ and SDP Inspired Lower Bounds
IEEE Transactions on Signal Processing
Integer parameter estimation in linear models with applications toGPS
IEEE Transactions on Signal Processing
Solving Box-Constrained Integer Least Squares Problems
IEEE Transactions on Wireless Communications
Closest point search in lattices
IEEE Transactions on Information Theory
On maximum-likelihood detection and the search for the closest lattice point
IEEE Transactions on Information Theory
IEEE Journal on Selected Areas in Communications
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Sphere Decoding is a popular Maximum Likelihood algorithm that can be used to detect signals coming from multiple-input, multiple-output digital communication systems. It is well known that the complexity required to detect each signal with the Sphere Decoding algorithm may become unacceptable, especially for low signal-to-noise ratios. In this paper, we describe an auxiliary technique that drastically decreases the computation required to decode a signal. This technique was proposed by Stojnic, Hassibi and Vikalo in 2008, and is based on using continuous box-bounded minimization in combination with Sphere Decoding. Their implementation is, however, not competitive due to the box minimization algorithm selected. In this paper we prove that by judiciously selecting the box minimization algorithm and tailoring it to the Sphere Decoding environment, the computational complexity of the resulting algorithm for low signal-to-noise ratios is better (by orders of magnitude) than standard Sphere Decoding implementations.