Skip to main content
Log in

On the Logical Computational Complexity Analysis of Turbo Decoding Algorithms for the LTE Standards

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Evaluating the computational complexity of decoders is a very important aspect in the area of Error Control Coding. However, most evaluations have been performed based on hardware implementations. In this paper, different decoding algorithms for binary Turbo codes which are used in LTE standards are investigated. Based on the different mathematical operations in the diverse equations, the computational complexity is derived in terms of the number of binary logical operations. This work is important since it demonstrates the computational complexity breakdown at the binary logic level as it is not always evident to have access to hardware implementations for research purposes. Also, in contrast to comparing different Mathematical operations, comparing binary logic operations provides a standard pedestal in view to achieve a fair comparative analysis for computational complexity. The usage of the decoding method with fewer number of binary logical operations significantly reduces the computational complexity which in turn leads to a more energy efficient/power saving implementation. Results demonstrate the variation in computational complexities when using different algorithms for Turbo decoding as well as with the incorporation of Sign Difference Ratio (SDR) and Regression-based extrinsic information scaling and stopping mechanisms. When considering the conventional decoding mechanisms and streams of 16 bits in length, Method 3 uses 0.0065% more operations in total as compared to Method 1. Furthermore, Method 2 uses only 0.0035% of the total logical complexity required with Method 1. These computational complexity analysis at the binary logical level can be further used with other error correcting codes adopted in different communication standards.

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
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

References

  1. Hajiyat, A. R. M., Sali, A., Mokhtar, M., & Hashim, F. (2019). Channel coding scheme for 5G mobile communication system for short length message transmission. Wireless Personal Communications, 2019(106), 377–400.

    Article  Google Scholar 

  2. Arora, K., Singh, J., & Randhawa, Y. S. (2020). A survey on channel coding techniques for 5G wireless networks. Telecommunication Systems, 73, 637–663.

    Article  Google Scholar 

  3. Foukalas, F., & Tsiftsis, T. A. (2018). Energy efficient power allocation for carrier aggregation in heterogeneous networks: partial feedback and circuit power consumption. IEEE Transactions on Green Communications and Networking, 2(1), 1–1.

    Article  Google Scholar 

  4. Mowla, M. M., Ahmad, I., Habibi, D., & Phung, Q. V. (2017). A green communication model for 5G systems. IEEE Transactions on Green Communications and Networking, 1(3), 264–280.

    Article  Google Scholar 

  5. Ibañez, R., Abisset-Chavanne, E., Aguado, J. V., Gonzalez, D., Cueto, E., & Chinesta, F. (2018). A manifold learning approach to data-driven computational elasticity and inelasticity. Archives of Computational Methods in Engineering, 25(1), 47–57.

    Article  MathSciNet  Google Scholar 

  6. Pinto, R. N., Afzal, A., D’Souza, L. V., & Ansari, Z. (2017). Computational fluid dynamics in turbomachinery: A review of state of the art. Archives of Computational Methods in Engineering, 24(3), 467–479.

    Article  MathSciNet  Google Scholar 

  7. Miñano, M., & Montáns, F. J. (2018). WYPiWYG damage mechanics for soft materials: A data-driven approach. Archives of Computational Methods in Engineering, 25(1), 165–193.

    Article  MathSciNet  Google Scholar 

  8. Neggers, J., Allix, O., Hild, F., & Roux, S. (2018). Big data in experimental mechanics and model order reduction: Today’s challenges and tomorrow’s opportunities. Archives of Computational Methods in Engineering, 25(1), 143–164.

    Article  MathSciNet  Google Scholar 

  9. Mishra, P., & Ul Amin, M. (2020). Performance evaluation for low complexity cascaded Sphere Decoders using best detection algorithm. ICT Express, vol. In Press.

  10. Nguyen, T. T. B., & Lee, H. (2019). Low-complexity multi-mode multi-way split-row layered LDPC decoder for gigabit wireless communications. Integration, 65, 189–200.

    Article  Google Scholar 

  11. Kizil, C. H., Diou, C., Rabiai, M., & Tanougast, C. (2020) FPGA implementation of UWB-IR impulse generator and its corresponding decoder based on discrete wavelet packet. AEU—-International Journal of Electronics and Communications, 114.

  12. Roberts, M. K. (2019). Simulation and implementation design of multi-mode decoder for WiMAX and WLAN applications. Measurement, 131, 28–34.

    Article  Google Scholar 

  13. Mageswari, N., Mahadevan, K., & Kumar, M. K. (2019). An α-factor architecture for RS decoder implemented on 90 nm CMOS technology for computer computing applications devices. Microprocessors and Microsystems, 71.

  14. Suaganthy, M., Karthikeyan, A., & Kuppusamy, P. G. (2019). Investigation of turbo decoding techniques based on lottery arbiter in 3D network on chip. Microprocessors and Microsystems, 71.

  15. Son, J., Cheun, K., & Yang, K. (2017). Low-complexity decoding of block turbo codes based on the chase algorithm. IEEE Communications Letters, 21(4), 706–709.

    Article  Google Scholar 

  16. Fayyaz, U. U., & Barry, J. R. (2013). A low-complexity soft-output decoder for polar codes. In IEEE global communications conference (GLOBECOM) , Atlanta, GA, USA.

  17. Fayyaz, U. U., & Barry, J. R. (2014). Low-complexity soft-output decoding of polar codes. EEE Journal on Selected Areas in Communications, 32(5), 958–966.

    Article  Google Scholar 

  18. Li, J., Wang, X., He, J., Su, C., & Shan, L. (2019). Turbo decoder design based on an LUT-normalized Log-MAP algorithm. Entropy, 21(8), 1–13.

    MathSciNet  Google Scholar 

  19. Wu, P.H.-Y. (2001). On the complexity of turbo decoding algorithms. In 53rd IEEE vehicular technology conference, Rhodes, Greece.

  20. Yoon, S., Ahn, B., & Heo, J. (2020). An advanced low-complexity decoding algorithm for turbo product codes based on the syndrome. EURASIP Journal on Wireless Communications and Networking, 2020(126), 1–21.

    Google Scholar 

  21. Hassan, A., Dessouky, M. I., Abouelazm, A. E., & Shokair, M. (2012). Evaluation of complexity versus performance for turbo code and LDPC under different code rates, in The fourth international conference on advances in satellite and space communications (SPACOMM), Chamonix/Mont Blanc, France.

  22. Wu, Z., Gong, C., & Liu, D. (2017). Computational complexity analysis of FEC decoding on SDR platforms. Journal of Signal Processing Systems, 89(2), 209–224.

    Article  Google Scholar 

  23. Mano, M. M. (2002). Digital design (3rd ed.). India: Pearson Prentice Hall.

    Google Scholar 

  24. Balasubramonian, R. (2015). Lecture 8: Binary Multiplication & Division. Available: https://www.cs.utah.edu/~rajeev/cs3810/slides/3810-08.pdf. [Accessed 14 April 2018].

  25. Topsoe, F. (2004). Some bounds for the logarithmic function. Research Report Collection, 7(2), 1–20.

    MathSciNet  Google Scholar 

  26. Berrou, C., Glavieux, A., & Thitimajshima, P. (1993). Near Shannon limit error-correcting coding and decoding: Turbo codes, In IEEE Trans., 1993.

  27. Beeharry, Y., Fowdur, T. P., & Soyjaudah, K. M. S. (2017). Performance Analysis of bit-level decoding algorithms for binary LTE turbo codes with early stopping. Istanbul University—Journal of Electrical and Electronics Engineering (IU-JEEE), 17(2), 3399–3415.

    Google Scholar 

  28. Lin, Y., Hung, W., Lin, W., Chen, T., & Lu, E. (2006) An efficient soft-input scaling scheme for turbo decoding” in IEEE international conference on sensor networks, ubiquitous, and trustworthy computing, Taichung, Taiwan.

  29. Fowdur, T. P., Beeharry, Y., & Soyjaudah, K. M. S. (2016). A novel scaling and early stopping mechanism for LTE turbo code based on regression analysis. Annals of Telecommunications, 71, 369–388.

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the University of Mauritius for providing the necessary facilities in conducting this work.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Y. Beeharry.

Ethics declarations

Conflicts of Interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Beeharry, Y., Fowdur, T.P. & Soyjaudah, K.M.S. On the Logical Computational Complexity Analysis of Turbo Decoding Algorithms for the LTE Standards. Wireless Pers Commun 118, 1591–1619 (2021). https://doi.org/10.1007/s11277-021-08106-x

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-021-08106-x

Keywords

Navigation