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Necessary field size and probability for MDP and complete MDP convolutional codes

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Abstract

It has been shown that maximum distance profile (MDP) convolutional codes have optimal recovery rate for windows of a certain length, when transmitting over an erasure channel. In addition, the subclass of complete MDP convolutional codes has the ability to reduce the waiting time during decoding. Since so far general constructions of these codes have only been provided over fields of very large size, there arises the question about the necessary field size such that these codes could exist. In this paper, we derive upper bounds on the necessary field size for the existence of MDP and complete MDP convolutional codes and show that these bounds improve the already existing ones. For some special choices of the code parameters, we are even able to give the exact minimum field size. Moreover, we derive lower bounds for the probability that a random code is MDP respective complete MDP.

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References

  1. Almeida P.J., Napp D., Pinto R.: Superregular matrices and applications to convolutional codes. Linear Algebra Appl. 499, 1–25 (2016).

    Article  MathSciNet  Google Scholar 

  2. Almeida P.J., Napp D., Pinto R.: A new class of superregular matrices and MDP convolutional codes. Linear Algebra Appl. 439, 2145–2157 (2013).

    Article  MathSciNet  Google Scholar 

  3. Barbero A., Ytrehus O.: Rate \((n-1)/n\) systematic memory maximum distance separable convolutional codes. IEEE Trans. Inf. Theory 64(4), 3018–3030 (2018).

    Article  MathSciNet  Google Scholar 

  4. Gluesing-Luerssen H., Rosenthal J., Smarandache R.: Strongly-MDS convolutional codes. IEEE Trans. Inf. Theory 52(2), 584–598 (2006).

    Article  MathSciNet  Google Scholar 

  5. Hutchinson R.: The existence of strongly MDS convolutional codes. SIAM J. Control Optim. 47, 2812–2826 (2008).

    Article  MathSciNet  Google Scholar 

  6. Hutchinson R., Rosenthal J., Smarandache R.: Convolutional codes with maximum distance profile. Syst. Control Lett. 54, 53–63 (2005).

    Article  MathSciNet  Google Scholar 

  7. Hutchinson R., Smarandache R., Trumpf J.: On superregular matrices and MDP convolutional codes. Linear Algebra Appl. 428, 2585–2596 (2008).

    Article  MathSciNet  Google Scholar 

  8. Lieb J.: Complete MDP convolutional codes. J. Algebra Appl. arXiv:1712.08767 (2018).

  9. Lieb J.: Counting Polynomial Matrices over Finite Fields. Matrices with Certain Primeness Properties and Applications to Linear Systems and Coding Theory (Dissertation), Wuerzburg University Press. (2017). https://www.bod.de/buchshop/couting-polynomial-matrices-over-finite-fields-julia-lieb-9783958260641.

  10. Lieb J.: The probability of primeness for specially structured polynomial matrices over finite fields with applications to linear systems and convolutional codes. Math. Control Signals Syst. 29, 8 (2017). https://doi.org/10.1007/s00498-017-0191-z.

    Article  MathSciNet  MATH  Google Scholar 

  11. Napp D., Smarandache R.: Constructing strongly MDS convolutional codes with maximum distance profile. Adv. Math. Commun. 10(2), 275–290 (2016).

    Article  MathSciNet  Google Scholar 

  12. Rosenthal J.: Connections between linear systems and convolutional codes. In: Marcus B., Rosenthal J. (eds.) Codes, Systems and Graphical Models IMA, vol. 123, pp. 39–66 (2001).

    Chapter  Google Scholar 

  13. Rosenthal J., Smarandache R.: Maximum distance separable convolutional codes. Appl. Algebra Eng. Commun. Comput. 10, 15–32 (1999).

    Article  MathSciNet  Google Scholar 

  14. Schwartz J.T.: Fast probabilistic algorithms for verification of polynomial identities. J. ACM 27(4), 701–717 (1980).

    Article  MathSciNet  Google Scholar 

  15. Tomas V., Rosenthal J., Smarandache R.: Decoding of convolutional codes over the erasure channel. IEEE Trans. Inf. Theory 58(1), 90–108 (2012).

    Article  MathSciNet  Google Scholar 

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Correspondence to Julia Lieb.

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Communicated by D. Panario.

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Lieb, J. Necessary field size and probability for MDP and complete MDP convolutional codes. Des. Codes Cryptogr. 87, 3019–3043 (2019). https://doi.org/10.1007/s10623-019-00661-6

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  • DOI: https://doi.org/10.1007/s10623-019-00661-6

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