Advertisement

Wireless Personal Communications

, Volume 69, Issue 1, pp 387–401 | Cite as

Distributed Joint Source-Channel Coding for Correlated Sources Using Non-systematic Repeat-Accumulate Based Codes

  • Valtteri TervoEmail author
  • Tad Matsumoto
  • Pen-Shun Lu
Open Access
Article

Abstract

In this paper, we propose a technique for coding the data from multiple correlated binary sources, with the aim of providing an alternative solution to the correlated source compression problem. Using non-systematic repeat-accumulate based codes, it is possible to achieve compression which is close to the Slepian–Wolf bound without relying on massive puncturing. With the technique proposed in this paper, instead of puncturing, compression is achieved by increasing check node degrees. Hence, the code rate can be more flexibly adjusted with the proposed technique in comparison with the puncturing-based schemes. Furthermore, the technique is applied to distributed joint source-channel coding (DJSCC). It is shown that in many cases tested, the proposed scheme can achieve mutual information very close to one with the lower signal-to-noise power ratio than turbo and low density generator matrix based DJSCC in additive white Gaussian noise channel. The convergence property of the system is also evaluated via the extrinsic information transfer analysis.

Keywords

Concatenated codes Cooperative coding Iterative decoding EXIT chart 

Notes

Acknowledgments

This research was carried out in the framework of the project Distributed Decision Making for Future Wireless Communication Systems (DIDES) which is funded by Finnish Funding Agency for Technology and Innovation (TEKES). This work has been also in part supported by the Japanese government funding program, Grant-in-Aid for Scientific Research (B), No. 23360170. This work has been also supported by Academy of Finland, Riitta ja Jorma J. Takanen Foundation and Finnish Foundation for Technology Promotion.

Open Access

This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.

References

  1. 1.
    Slepian D., Wolf J. K. (1973) Noiseless coding of correlated information sources. IEEE Transactions on Information Theory 19(4): 471–480MathSciNetzbMATHCrossRefGoogle Scholar
  2. 2.
    Shannon, C. E. (1959). Coding theorems for a discrete source with a fidelity criterion. IRE national convention record (pp. 142–163).Google Scholar
  3. 3.
    Chiang M., Boyd S. (2004) Geometric programming duals of channel capacity and rate distortion. IEEE Transactions on Information Theory 50(2): 245–258MathSciNetCrossRefGoogle Scholar
  4. 4.
    Wyner A. (1974) Recent results in the Shannon theory. IEEE Transactions on Information Theory 20(1): 2–10MathSciNetzbMATHCrossRefGoogle Scholar
  5. 5.
    Berrou, C., Glavieux, A., & Thitimajshima, P. (1993). Near Shannon limit error correcting coding and decoding: Turbo codes. Proceedings of IEEE international conference on communications, Geneva, Switzerland (Vol. 2, pp. 1064–1070).Google Scholar
  6. 6.
    Garcia-Frias J., Zhao Y. (2001) Compression of correlated binary sources using turbo codes. IEEE Communications Letters 5(10): 417–419CrossRefGoogle Scholar
  7. 7.
    Garcia-Frias J., Zhao Y. (2005) Near-Shannon/Slepian-Wolf performance for unknown correlated sources over AWGN channel. IEEE Transactions on Communications 53(4): 555–559CrossRefGoogle Scholar
  8. 8.
    Garcia-Frias, J., Zhong, W., & Zhao, Y. (2002). Iterative decoding schemes for source and joint source-channel coding of correlated sources. Proceedings of annual Asilomar conference on signals, systems and computers, Newark, DE, USA (pp. 250–256).Google Scholar
  9. 9.
    Murugan D. M., Gopala P. K., El Gamal H. (2004) Correlated sources over wireless channels: Cooperative source-channel coding. IEEE Journal on Selected Areas in Communications 22(6): 988–998CrossRefGoogle Scholar
  10. 10.
    Gehric N., Dragotti P. L. (2004) Symmetric and a-symmetric Slepian–Wolf codes with systematic and non-systematic linear codes. IEEE Communications Letters 9(1): 61–63CrossRefGoogle Scholar
  11. 11.
    Xiong Z., Liveris A. D., Cheng S. (2004) Distributed source coding for sensor networks. IEEE Signal Processing Magazine 21(5): 80–94CrossRefGoogle Scholar
  12. 12.
    Girod B., Aaron A. M., Rane S., Rebollo-Monedero D. (2005) Distributed video coding. Proceedings of IEEE (invited paper) 93(1): 71–83CrossRefGoogle Scholar
  13. 13.
    Rup, S., Dash, R., Ray, N. K., & Majhi, B. (2009). Recent advances in distributed video coding. Proceedings of IEEE international conference on computer science and information theory, Beijing, China (pp. 130–135).Google Scholar
  14. 14.
    Shamir G. I., Xie K. (2009) Universal source controlled channel decoding with nonsystematic quick-look-in turbo codes. IEEE Transactions on Communictions 57(4): 960–971CrossRefGoogle Scholar
  15. 15.
    Divsalar, D., Jin, H., & McEliece, R. J. (1998). Coding theorems for ‘turbo-like’ codes. Proceedings of 36th Allerton conference on communication, control and computing, Allerton, IL, USA (pp. 201–210).Google Scholar
  16. 16.
    Gallager R. (1963) Low-density parity-check codes. MIT Press, Cambridge, MAGoogle Scholar
  17. 17.
    Jin, H., & McEliece, R. J. (1999). RA codes achieve AWGN channel capacity. Applied algebra, algebraic algorithms and error-correcting codes, Pasadena, CA, USA. January 1, 1999 (Vol. 1719/1999, p. 729).Google Scholar
  18. 18.
    ten Brink S. (1999) Convergence behavior of iterative decoding. IEE Electronics Letters 35(10): 806–808CrossRefGoogle Scholar
  19. 19.
    ten Brink, S. (2001). Code doping for triggering iterative decoding convergence. Proceedings of IEEE international symposium on information theory, Washington, D.C., USA.Google Scholar
  20. 20.
    ten Brink S., Kramer G. (2003) Design of repeat-accumulate codes for iterative detection and decoding. IEEE Transactions on Signal Processing 51(11): 2764–2772MathSciNetCrossRefGoogle Scholar
  21. 21.
    Abbasfar A., Divsalar D., Yao K. (2007) Accumulate-repeat-accumulate codes. IEEE Transactions on Communications 55(4): 692–702CrossRefGoogle Scholar
  22. 22.
    Yano, T., & Matsumoto, T. (2009). Arithmetic extended-mapping for BICM-ID with repetition codes. Proceedings of ITG workshop smart antennas, Berlin, Germany (pp. 1–8).Google Scholar
  23. 23.
    Ashikhmin, A., Kramer, G., & ten Brink, S. (2002). Code rate and the area under extrinsic information transfer curves. Proceedings of IEEE international symposium on information theory, Lausanne, Switzerland (p. 115).Google Scholar
  24. 24.
    Peyton, Z., & Peebles, J. (2001). Probability, random variables, and random signal principles. 1221 Avenue of the Americas, New York, NY, 10020: Irwin/McGraw-Hill.Google Scholar
  25. 25.
    Brännström, F., Rasmussen, L. K., & Grant, A. J. (2005). Convergence analysis and optimal scheduling for multiple concatenated codes. IEEE Transactions on Information Theory, 51(9), 3354–3364. Proceedings of 2nd international conference on turbo codes, Brest, France (pp. 1–8).Google Scholar

Copyright information

© The Author(s) 2012

Authors and Affiliations

  1. 1.Centre for Wireless CommunicationsUniversity of OuluOuluFinland
  2. 2.Japan Advanced Institute of Science and TechnologyNomiJapan

Personalised recommendations