Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

Robust iterative decoding of turbo codes in heavy-tailed noise

Robust iterative decoding of turbo codes in heavy-tailed noise

For access to this article, please select a purchase option:

Buy article PDF
$19.95
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IEE Proceedings - Communications — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

The advancements in channel coding theory over the past decades have been accomplished by considering of additive white Gaussian noise (AWGN) channels. Much less is known about the consequences when the standard AWGN assumption is not fulfilled in realistic environments and, more importantly, the appropriate countermeasures. The paper investigates the robustness of turbo codes decoded by existing quadratic-type algorithms in heavy-tailed, non-Gaussian noise channels. It illustrates that impulsive noise constitutes a major impairment in turbo decoding by studying the a posteriori probabilities computed by the constituent decoders, in addition to a formal account in terms of error probability performance. It is found that the performance of turbo codes is extremely sensitive to the shape of the underlying noise density function, being considerably degraded when this function departs from Gaussianity into a heavy-tailed distribution. A robust variant of existing decoders for reliable decoding of turbo codes in heavy-tailed noise is proposed and studied. The robustness is achieved by the enforcement of a non-quadratic soft metric into the decoder for good estimation of the transition probabilities and reliable extraction of extrinsic information.

References

    1. 1)
      • S.A. Kassam . (1988) Signal detection in non-Gaussian noise.
    2. 2)
      • E.J. Wegman , S.C. Schwartz , J.B. Thomas . (1989) , Topics in non-Gaussian signal processing.
    3. 3)
      • C. Berrou . The ten-year-old turbo codes are entering into service. IEEE Commun. Mag. , 110 - 116
    4. 4)
      • Zhang, L., Yongacoglu, A.: `Turbo decoding with erasures for high-speed transmission in the presence of impulse noise', Proc. Int. Zurich Seminar Broadband Communications, 2002, Zurich, Switzerland, 20, p. 1–6.
    5. 5)
      • J.W. Cook . Wide-band impulsive noise survey of the access network. BT Technol. J. , 155 - 162
    6. 6)
      • Gonzalez, J.G.: `Robust techniques for wireless communications in non-Gaussian environments', 1997, PhD, University of Delaware, Department of Electrical and Computer Engineering, Newark, Delaware.
    7. 7)
    8. 8)
    9. 9)
      • Hagenauer, J.: `The Turbo principle: tutorial introduction and state of the art', Proc. IEEE Int. Symp. on Turbo Codes and Related Topics, Sept. 1997, Brest, France, p. 1–11.
    10. 10)
      • Berrou, C., Glavieux, A., Thitimajshima, P.: `Near Shannon limit error-correcting coding: turbo codes', Proc. IEEE Int. Conf. on Communications, 1993, Geneva, Switzerland, p. 1064–1070.
    11. 11)
      • C.L. Nikas , M. Shao . (1995) Signal processing with alpha-stable distributions and applications.
    12. 12)
      • Summers, T., Wilson, S.G.: `Turbo code performance in heavy-tailed noise', Proc. CISS’98, 1998, Princeton, NJ, USA.
    13. 13)
      • P.J. Huber . (1981) Robust statistics.
    14. 14)
      • K. Koike , H. Ogiwara . Performance evaluation of turbo code over impulsive noise channel. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. , 2418 - 2426
    15. 15)
    16. 16)
      • S. Benedetto , G. Montorsi , D. Divsalar . Concatenated convolutional codes with interleavers. IEEE Commun. Mag. , 102 - 109
    17. 17)
      • L. Bahl , J. Cocke , F. Jelinek , J. Raviv . Optimal decoding of linear codes for minimizing symbol error rate. IEEE Trans. Inf. Theory , 284 - 287
    18. 18)
    19. 19)
    20. 20)
    21. 21)
    22. 22)
      • Hagenauer, J., Hoeher, P.: `A Viterbi algorithm with soft-decision outputs and its applications', Proc. IEEE Global Commun. Conf., Nov. 1989, Dallas, TX, USA, p. 47.1.1–47.1.7.
http://iet.metastore.ingenta.com/content/journals/10.1049/ip-com_20040864
Loading

Related content

content/journals/10.1049/ip-com_20040864
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address