Skip to main content
Log in

On the source-channel separation theorem for infinite source alphabets

  • Source Coding
  • Published:
Problems of Information Transmission Aims and scope Submit manuscript

Abstract

The single-user source-channel separation theorem has been proved for many classes of sources and channels, including sources with finite or countably infinite alphabets. Typically, the source-channel separation theorem is first proved for sources with a finite alphabet, and then the results are extended to sources with a countably infinite alphabet. This paper considers the direct extension of the source-channel separation theorem for some classes of sources with a finite alphabet to a countably infinite alphabet. Specifically, we provide a solution for memoryless sources and arbitrary channels. It is then discussed how this approach may be extended to the case of general sources and arbitrary channels.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Han, T.S., Joint Source-Channel Coding Revisited: Information-Spectrum Approach, http://arXiv:0712.2959v1 [cs.IT], 2007.

    Google Scholar 

  2. Shannon, C.E., A Mathematical Theory of Communication, Bell Syst. Tech. J., 1948, vol. 27, no. 3, pp. 379–423; no. 4, pp. 623–656.

    Article  MathSciNet  MATH  Google Scholar 

  3. Dobrushin, R.L., General Formulation of Shannon’s Main Theorem in Information Theory, Uspekhi Mat. Nauk, 1959, vol. 14, no. 6, pp. 3–104 [Trans. Amer. Math. Soc., Ser. 2 (Engl. Transl.), 1963, vol. 33, pp. 323–438].

    MATH  Google Scholar 

  4. Pinsker, M.S., Informatsiya i informatsionnaya ustoichivost’ sluchainykh velichin i protsessov, Probl. Peredachi Inf., issue 7, Moscow: Akad. Nauk SSSR, 1960. Translated under the title Information and Information Stability of Random Variables and Processes, San Francisco: Holden-Day, 1964.

    Google Scholar 

  5. Hu, G.D., On Shannon Theorem and Its Converse for Sequence of Communication Schemes in the Case of Abstract Random Variables, in Proc. 3rd Prague Conf. on Information Theory, Statistical Decision Functions, Random Processes, Liblice, Czechoslovakia, June 5–13, 1962, Prague: Czechoslovak Acad. Sci., 1964, pp. 285–333.

    Google Scholar 

  6. Vembu, S., Verdú, S., and Steinberg, Y., The Source-Channel Separation Theorem Revisited, IEEE Trans. Inform. Theory, 1995, vol. 41, no. 1, pp. 44–54.

    Article  MathSciNet  MATH  Google Scholar 

  7. Han, T.S., Information-Spectrum Method in Information Theory, New York: Springer, 2003.

    Book  Google Scholar 

  8. Cover, T.M. and Thomas, J.A., Elements of Information Theory. New York: Wiley, 2006, 2nd ed.

    MATH  Google Scholar 

  9. Ho, S.-W. and Verdú, S., On the Interplay between Conditional Entropy and Error Probability, IEEE Trans. Inform. Theory, 2010, vol. 56, no. 12, pp. 5930–5941.

    Article  MathSciNet  Google Scholar 

  10. Han, T.S. and Verdú, S., Approximation Theory of Output Statistics, IEEE Trans. Inform. Theory, 1993, vol. 39, no. 3, pp. 752–772.

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to A. Aghajan.

Additional information

Original Russian Text © A. Aghajan, S.J. Zahabi, M. Khosravifard, T.A. Gulliver, 2015, published in Problemy Peredachi Informatsii, 2015, Vol. 51, ^no. 2, pp. 122–127.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Aghajan, A., Zahabi, S.J., Khosravifard, M. et al. On the source-channel separation theorem for infinite source alphabets. Probl Inf Transm 51, 200–204 (2015). https://doi.org/10.1134/S0032946015020106

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S0032946015020106

Keywords

Navigation