Skip to main content
Log in

Turbo decoding of simple product codes in a two user binary adder channel employing the Bahl–Cocke–Jelinek–Raviv algorithm

  • Published:
Telecommunication Systems Aims and scope Submit manuscript

Abstract

The main goal in this paper is an investigation of the Bahl–Cocke–Jelinek–Raviv (BCJR) algorithm applied in a turbo decoding scheme. Binary product codes are employed in a turbo coding scheme and the channel model considered is the two user binary adder channel (2-BAC) with additive white Gaussian noise. A trellis for two users is constructed for a pair of product codes tailored for use in the 2-BAC in order to employ the BCJR decoding algorithm. Computer simulation is employed to show that product codes on the 2-BAC, employing low-complexity component codes, produces considerable gain with few iterations under iterative BCJR decoding.

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. AHA Products Group, Comtech EF Data Corporation. http://www.aha.com/DrawProducts.aspx?Action=GetCategories&ProductTypeID=2&PageID=47. Accessed 2 May 2017

  2. Turbo Concept, a Newtec Company. http://www.turboconcept.com/ip_cores.php?p=tpc-Turbo-Product-Codes. Accessed 2 May 2017.

  3. Kasami, T., & Lin, S. (1976). Coding for a multiple-access channel. IEEE Trans Inf Theory, 2(22), 129–137. https://doi.org/10.1109/TIT.1976.1055529.

    Article  Google Scholar 

  4. Ali, F. H., & Honary, B. (1991). Low complexity soft decision decoding technique for T-User collaborative coding multiple-access channels. Electron Lett, 27(13), 1167–1169. https://doi.org/10.1049/el:19910728.

    Article  Google Scholar 

  5. Ali, F. H., & Honary, B. (1994). Collaborative coding and decoding techniques for multiple access channel. IEE Proc Commun, 141(2), 56–62. https://doi.org/10.1049/ip-com:19941043.

    Article  Google Scholar 

  6. Peterson, R., & Costello, D. J, Jr. (1979). Binary convolutional codes for a multiple-access channel. IEEE Trans Inf Theory, 1(25), 101–105. https://doi.org/10.1109/TIT.1979.1056000.

    Article  Google Scholar 

  7. De Marco, G., & Kowalski, D. R. (2017). Contention resolution in a non-synchronized multiple access channel. Theor Comput Sci, 689, 1–13. https://doi.org/10.1016/j.tcs.2017.05.014.

    Article  Google Scholar 

  8. Xue, Y., Tian, Y., & Yang, C. (2017). Joint coding in parallel symmetric interference channels with deterministic model. EURASIP J Wireless Commun Netw, 2017(1), 49.

    Article  Google Scholar 

  9. Anantharamu, L., Chlebus, B. S., & Rokicki, M. A. (2017). Adversarial multiple access channels with individual injection rates. Theory of Comput Syst, 61(3), 820–850.

    Article  Google Scholar 

  10. Mattas, M., & Ostergard, P. R. J. (2005). A new bound for the zero-error capacity region of the two-user binary adder channel. IEEE Trans Inf Theory, 51(9), 3289–3291. https://doi.org/10.1109/TIT.2005.853309.

    Article  Google Scholar 

  11. Kiviluoto, L., & Ostergard, P. R. J. (2007). New uniquely decodable codes for the \( T \)-user binary adder channel with \(3\le T\le 5\). IEEE Trans Inf Theory, 53(3), 1219–1220. https://doi.org/10.1109/TIT.2006.890692.

    Article  Google Scholar 

  12. Bahl, L. R., Cocke, J., Jelinek, F., et al. (1974). Optimal decoding of linear codes for minimizing symbol error rate. IEEE Trans Inf Theory, 2(20), 284–287. https://doi.org/10.1109/TIT.1974.1055186.

    Article  Google Scholar 

  13. Alcoforado, M. L. M. G., de Jesus, J. J., & da Rocha, V. C. (2017). Turbo coding for the noisy 2-user binary adder channel with punctured convolutional codes. Telecommun Syst, 64(3), 459–465. https://doi.org/10.1007/s11235-016-0185-z.

    Article  Google Scholar 

  14. Alcoforado, M. L. M. G., Oliveira, M. C. C., & da Rocha Jr, V. C. (2015). The Bahl-Cocke-Jelinek-Raviv decoding algorithm applied to the three-user binary adder channel. IET Commun, 9(5), 897–902. https://doi.org/10.1049/iet-com.2014.0376.

    Article  Google Scholar 

  15. Anvar, S. M. M., Khanmohammadi, S., & Niya, J. M. (2016). Game theoretic power allocation for fading MIMO multiple access channels with imperfect CSIR. Telecommun Syst, 61(4), 875–886. https://doi.org/10.1007/s11235-015-0043-4.

    Article  Google Scholar 

  16. Bakin, E. A., & Evseev, G. S. (2016). Remark on on the capacity of a multiple-access vector adder channel by AA Frolov and VV Zyablov. Probl Inf Trans, 52(1), 1–5. https://doi.org/10.1134/S0032946016010014.

    Article  Google Scholar 

  17. Frolov, A. A., & Zyablov, V. V. (2014). On the capacity of a multiple-access vector adder channel. Probl Inf Trans, 50(2), 133–143. https://doi.org/10.1134/S0032946014020021.

    Article  Google Scholar 

  18. Tavakoli, H. (2017). Polarization of a point-to-point channel by a multiple access channel: a new method for different channel polarization. Iran J Sci Technol Trans Electr Eng, 41(2), 115–122. https://doi.org/10.1007/s40998-017-0022-8.

    Article  Google Scholar 

  19. Ordentlich, O., & Shayevitz, O. (2015, June). A VC-dimension-based outer bound on the zero-error capacity of the binary adder channel. In: International symposium on information theory (ISIT), 2015 IEEE pp. 2366–2370. IEEE. https://doi.org/10.1109/ISIT.2015.7282879.

  20. Bidokhti, S. S., & Kramer, G. (2016, July). Capacity of two-relay diamond networks with rate-limited links to the relays and a binary adder multiple access channel. In: 2016 IEEE international symposium on information theory (ISIT), pp. 1665–1669. IEEE. https://doi.org/10.1109/ISIT.2016.7541582.

  21. Song, H., Brandt-Pearce, M., Xie, T., & Wilson, S. G. (2012, December). Combined constrained code and LDPC code for long-haul fiber-optic communication systems. In: Global communications conference (GLOBECOM), 2012 IEEE, pp. 2984–2989. https://doi.org/10.1109/GLOCOM.2012.6503571.

  22. Du, Q., Sun, L., Song, H., & Ren, P. (2016). Security enhancement for wireless multimedia communications by fountain code. IEEE COMSOC MMTC E-Lett, 11(2), 47–51.

    Google Scholar 

  23. Song, J., Shen, P., Wang, K., Zhang, L., & Song, H. (2016). Can gray code improve the performance of distributed video coding? IEEE Access, 4, 4431–4441. https://doi.org/10.1109/ACCESS.2016.2604358.

    Article  Google Scholar 

  24. Shojafar, M., Abolfazli, S., Mostafaei, H., & Singhal, M. (2015). Improving channel assignment in multi-radio wireless mesh networks with learning automata. Wirel Pers Commun, 82(1), 61–80. https://doi.org/10.1007/s11277-014-2194-0.

    Article  Google Scholar 

  25. Alcoforado, M. L. M. G., da Rocha Jr., V. C. & Markarian, G.(2005). Turbo block codes for the binary adder channel. In: 2005 IEEE international symposium on information theory (ISIT), Adelaide, Australia, Vol. 2, pp. 1883–1887. IEEE.

  26. Peterson, W. W., & Weldon Jr, E. J. (1972). Error-correcting codes. Cambridge: MIT Press.

    Google Scholar 

  27. Wolf, J. K. (1978). Efficient maximum likelihood decoding of linear block codes using a trellis. IEEE Trans Inf Theory, 1(24), 76–80. https://doi.org/10.1109/TIT.1978.1055821.

    Article  Google Scholar 

  28. Morelos-Zaragoza, R. H. (1998). The art of error correcting coding. Hoboken: John Wiley & Sons Ltd.

    Google Scholar 

  29. Pyndiah, R. (1998). Near-optimum decoding of product codes: block turbo codes. IEEE Trans Commun, 46(8), 1003–1010. https://doi.org/10.1109/26.705396.

    Article  Google Scholar 

  30. Haesik, K., Markarian, G., & da Rocha Jr., V. C. (2010). Low complexity iterative decoding of product codes using a generalized array code form of the Nordstrom-Robinson code. In: Proceesings 6th international symposium on turbo codes and iterative information processing (ISTC), France, September, pp. 88–92. https://doi.org/10.1109/ISTC.2010.5613809.

  31. Cover, T. M., & Thomas, J. A. (2006). Elements of information theory 2nd edition (p. 200). Hoboken: Wiley-interscience.

    Google Scholar 

  32. Hagenauer, J., Offer, E., & Papke, L. (1996). Iterative decoding of binary block and convolutional codes. IEEE Trans Inf Theory, 42(2), 429–445.

    Article  Google Scholar 

  33. Franz, V., & Anderson, J. B. (1998). Concatenated decoding with a reduced-search BCJR algorithm. IEEE J Sel Areas in Commun, 16(2), 186–195. https://doi.org/10.1109/49.661107.

    Article  Google Scholar 

  34. Sabeti, L., Ahmadi, M., & Tepe, K. E. (2005, September). Low-complexity BCJR decoder for turbo decoders and its VLSI implementation in 0.18-/spl mu/m CMOS. In: IEEE 62nd vehicular technology conference, 2005. VTC-2005-Fall. 2005 IEEE. Vol. 2, pp. 912–916. https://doi.org/10.1109/VETECF.2005.1558058.

  35. Benchimol, I., Pimentel, C., & Souza, R. D. (2015). Low complexity trellis representations of convolutional codes via sectionalization of the minimal trellis. Telecommun Syst, 59(4), 491–500. https://doi.org/10.1007/s11235-014-9909-0.

    Article  Google Scholar 

Download references

Acknowledgements

The work of the third author received partial support from the Brazilian National Council for Scientific and Technological Development - CNPq under Project No. 307467/2015-5.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to M. L. M. G. Alcoforado.

Ethics declarations

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

de Souza, I.M.M., Alcoforado, M.L.M.G. & da Rocha, V.C. Turbo decoding of simple product codes in a two user binary adder channel employing the Bahl–Cocke–Jelinek–Raviv algorithm. Telecommun Syst 68, 513–521 (2018). https://doi.org/10.1007/s11235-017-0407-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11235-017-0407-z

Keywords

Navigation