Loading web-font TeX/Math/Italic
A linear-time nearest point algorithm for the lattice An* | IEEE Conference Publication | IEEE Xplore

A linear-time nearest point algorithm for the lattice An*


Abstract:

The lattice An* is an important lattice because of its covering properties in low dimensions. Two algorithms exist in the literature that compute the nearest point in the...Show More

Abstract:

The lattice An* is an important lattice because of its covering properties in low dimensions. Two algorithms exist in the literature that compute the nearest point in the lattice AAn* in O(n log n) arithmetic operations. In this paper we describe a new algorithm that requires only O(n) operations. The new algorithm makes use of an approximate sorting procedure called a bucket sort. This is the fastest known nearest point algorithm for this lattice.
Date of Conference: 07-10 December 2008
Date Added to IEEE Xplore: 28 April 2009
ISBN Information:
Conference Location: Auckland, New Zealand
References is not available for this document.

1. Introduction

The study of point lattices is of great importance in several areas of number theory, particularly the studies of quadratic forms, the geometry of numbers and simultaneous Diophantine approximation, and also to the practical engineering problems of quantisation and channel coding. They are also important in studying the sphere packing problem and the kissing number problem [1], [2].

Select All
1.
I. V. L. Clarkson, "An algorithm to compute a nearest point in the lattice A*n" in Applied Algebra Algebraic Algorithms and Error-Correcting Codes, Springer:Lecture Notes in Computer Science, vol. 1719, pp. 104-120, 1999.
2.
J. H. Conway and N. J. A. Sloane, Sphere packings lattices and groups, Springer, 1998.
3.
I. V. L. Clarkson, "Approximate maximum-likelihood period estimation from sparse noisy timing data", IEEE Trans. Signal Process., vol. 56, no. 5, pp. 1779-1787, May 2008.
4.
R. McKilliam and I. V. L. Clarkson, "Maximum-likelihood period estimation from sparse noisy timing data", Proc. Internat. Conf. Acoust. Speech Signal Process, pp. 3697-3700, Mar. 2008.
5.
I. V. L. Clarkson, "Frequency estimation phase unwrapping and the nearest lattice point problem", Proc. Internat. Conf. Acoustics Speech Signal Process, vol. 3, pp. 1609-1612, Mar. 1999.
6.
B. G. Quinn, "Estimating the mode of a phase distribution", Asilomar Conference on Signals Systems and Computers, pp. 587-591, Nov 2007.
7.
J. H. Conway and N. J. A. Sloane, "Fast quantizing and decoding and algorithms for lattice quantizers and codes", IEEE Trans. Inform. Theory, vol. 28, no. 2, pp. 227-232, Mar. 1982.
8.
J. H. Conway and N. J. A. Sloane, "Soft decoding techniques for codes and lattices including the Golay code and the Leech lattice", IEEE Trans. Inform. Theory, vol. 32, no. 1, pp. 41-50, Jan. 1986.
9.
R. G. McKilliam, I. V. L. Clarkson and B. G. Quinn, "An algorithm to compute the nearest point in the lattice A*n", IEEE Trans. Inform. Theory, vol. 54, no. 9, pp. 4378-4381, Sep. 2008.
10.
T. H. Cormen, C. E. Leiserson, R. L. Rivest and C. Stein, Introduction to Algorithms, MIT Press. and McGraw-Hill, 2001.
Contact IEEE to Subscribe

References

References is not available for this document.