Improved Max-Log-MAP Turbo Decoding by Extrinsic Information Scaling and Combining

  • Lei Sun
  • Hua WangEmail author
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 516)


Turbo codes are among the best error-correcting codes, but trade-offs between performance and complexity in decoding are required for hardware implementation. In this paper, a novel extrinsic information scaling scheme for max-log-MAP decoder is proposed. It scales and combines extrinsic information generated at successive iteration round. The proposed method is evaluated for 3GPP LTE turbo codes in terms of decoding performance, complexity, and convergence. The simulation results show it has decoding gain near to log-MAP while keeps almost the same computation complexity as max-log-MAP with slight increment in memory resource. Moreover, it maintains insensitivity to SNR estimation error of max-log-MAP algorithm. Compared with conventional scaling scheme, it accelerates extrinsic information exchange between two constituent decoders to get better convergence and decoding performance.


Turbo codes Extrinsic information Scaling factor 


  1. 1.
    Alvarado A, Nunez V, Szczecinski L, Agrell E. Correcting suboptimal metrics in iterative decoders. In: IEEE international conference on communications; 2009. p. 1–6.Google Scholar
  2. 2.
    Cheng JF, Ottosson T. Linearly approximated log-map algorithms for turbo decoding. In: Vehicular technology conference proceedings, 2000. Vtc 2000-Spring Tokyo, vol 3. 2000 IEEE; 2000. p. 2252–56.Google Scholar
  3. 3.
    El-Khamy M, Wu J, Lee J, Roh H, Kang I Near-optimal turbo decoding in presence of SNR estimation error. In: Global communications conference; 2013. p. 3737–42.Google Scholar
  4. 4.
    El-Khamy M, Wu J, Lee J, Kang I. Online log-likelihood ratio scaling for robust turbo decoding. IET Commun. 2013;8(2):217–26.CrossRefGoogle Scholar
  5. 5.
    Hagenauer J. The exit chart—introduction to extrinsic information transfer in iterative processing. In: 2004 European signal processing conference; 2004. p. 1541–48.Google Scholar
  6. 6.
    Liu Z, Wu B, Ye T. Improved turbo decoding with multivariable taylor series expansion. IEEE Commun Lett. 2017;99:1–1.Google Scholar
  7. 7.
    Papaharalabos S, Mathiopoulos PT, Masera G, Martina M. Non-recursive \({\max ^*}\) operator with reduced implementation complexity for turbo decoding. IET Commun. 2012;6(7):702–7.MathSciNetCrossRefGoogle Scholar
  8. 8.
    Robertson P, Villebrun E, Hoeher P. A comparison of optimal and sub-optimal map decoding algorithms operating in the log domain. In: IEEE international conference on communications, 1995. ICC’95 Seattle, gateway to globalization, vol 2; 1993. p. 1009–13.Google Scholar
  9. 9.
    Roth C, Belfanti S, Benkeser C, Huang Q. Efficient parallel turbo-decoding for high-throughput wireless systems. IEEE Trans Circ Syst I Regul Pap 2014;61(6):1824–35 (2014)CrossRefGoogle Scholar
  10. 10.
    Sapra R, Jagannatham AK. Exit chart based ber expressions for turbo decoding in fading MIMO wireless systems. IEEE Commun Lett. 2015;19(1):10–3.CrossRefGoogle Scholar
  11. 11.
    Vogt J, Finger A. Improving the max-log-map turbo decoder. Electron Lett. 2000;36(23):1937–9.CrossRefGoogle Scholar
  12. 12.
    Worm A, Hoeher P, Wehn N. Turbo-decoding without snr estimation. IEEE Commun Lett. 2000;4(6):193–5.CrossRefGoogle Scholar
  13. 13.
    Yue DW, Nguyen HH. Unified scaling factor approach for turbo decoding algorithms. IET Commun. 2010;4(8):905–14.MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.School of Information and ElectronicsBeijing Institute of TechnologyBeijingChina

Personalised recommendations