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May  2017, 11(2): 359-366. doi: 10.3934/amc.2017029

Minimum dimensional Hamming embeddings

Rafael G. L. D'Oliveira , and  Marcelo Firer

University of Campinas, São Paulo, Brazil

* Corresponding author

Received  February 2016 Revised  March 2016 Published  May 2017

We consider two metrics decoding equivalent if they impose the same minimum distance decoding for every code. It is known that, up to this equivalence, every metric is isometrically embeddable into the Hamming cube.

We present an algorithm which for any translation invariant metric gives an upper bound on the minimum dimension of such an embedding. We also give lower and upper bounds for this embedding dimension over the set of all such metrics.

Keywords: Coding theory, hypercube embeddings, maximum-likelihood decoding, minimum distance decoding.
Mathematics Subject Classification: 94A24.
Citation: Rafael G. L. D'Oliveira, Marcelo Firer. Minimum dimensional Hamming embeddings. Advances in Mathematics of Communications, 2017, 11 (2) : 359-366. doi: 10.3934/amc.2017029
References:
[1]

V. Chvétal, Recognizing intersection patterns, Ann. Discrete Math., 8 (1980), 249-251. Google Scholar

[2]

M. Deza and M. Laurent, Geometric properties, in Geometry of Cuts and Metrics, Springer-Verlag, 1997,511-550. doi: 10.1007/978-3-642-04295-9_31. Google Scholar

[3]

R. G. L. D'Oliveira and M. Firer, Channel metrization, preprint, arXiv: 1510.03104Google Scholar

[4] F. Eisenbrand, Fast Integer Programming in Fixed Dimension, Springer, Berlin, 2003. doi: 10.1007/978-3-540-39658-1_20. Google Scholar
[5]

M. Firer and J. L. Walker, Matched metrics and channels, IEEE Trans. Inf. Theory, 62 (2016), 1150-1156. doi: 10.1109/TIT.2015.2512596. Google Scholar

[6]

H. W. Lenstra Jr., Integer programming with a fixed number of variables, Math. Oper. Res., 8 (1983), 538-548. doi: 10.1287/moor.8.4.538. Google Scholar

show all references

References:
[1]

V. Chvétal, Recognizing intersection patterns, Ann. Discrete Math., 8 (1980), 249-251. Google Scholar

[2]

M. Deza and M. Laurent, Geometric properties, in Geometry of Cuts and Metrics, Springer-Verlag, 1997,511-550. doi: 10.1007/978-3-642-04295-9_31. Google Scholar

[3]

R. G. L. D'Oliveira and M. Firer, Channel metrization, preprint, arXiv: 1510.03104Google Scholar

[4] F. Eisenbrand, Fast Integer Programming in Fixed Dimension, Springer, Berlin, 2003. doi: 10.1007/978-3-540-39658-1_20. Google Scholar
[5]

M. Firer and J. L. Walker, Matched metrics and channels, IEEE Trans. Inf. Theory, 62 (2016), 1150-1156. doi: 10.1109/TIT.2015.2512596. Google Scholar

[6]

H. W. Lenstra Jr., Integer programming with a fixed number of variables, Math. Oper. Res., 8 (1983), 538-548. doi: 10.1287/moor.8.4.538. Google Scholar

Figure 1.  The partition of $\mathbb{R}_{> 0}^3$ by the $\mathit{Cone}$ function into $13$ cones: six $3$-dimensional, six $2$-dimensional, and one $1$-dimensional (the ray $(\lambda,\lambda,\lambda)$ with $\lambda > 0)$
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