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Improving BER Performance of Uplink LTE by Using Turbo Equalizer

  • Aleksandr GelgorEmail author
  • Anton Gorlov
  • Pavel Ivanov
  • Evgenii Popov
  • Andrey Arkhipkin
  • Tatiana Gelgor
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9247)

Abstract

The potential of turbo-equalization technique applied to uplink (UL) LTE signals detection is analyzed in this paper. The turbo equalizer, which is also called iterative receiver, represents a popular approach for detection of signals passed through a fading channel. The receiver performs equalization and decoding of error-correcting code in a loop. For implementation of the iterative receiver we performed two frequency-domain equalizers: the approximate MMSE SISO-equalizer and the soft interference canceller (SIC) SISO-equalizer. During the simulation, we analyzed several configurations of UL LTE with QPSK, 16-QAM, 64-QAM signal constellations and allocation of 25 and 100 resource blocks. All considered modes used rate 2/3 parallel concatenated convolutional code and single input single output antennas pattern. Bit error rate (BER) performance was estimated during the simulation with the extended vehicular A (EVA) model of multipath fading channel.

Keywords

Uplink LTE Turbo equalizer SISO-equalizer Approximate MMSE Soft interference canceller 

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References

  1. 1.
    Proakis, J., Salehi, M.: Digital Communications, 5th edn. McGraw-Hill, New York (2008)Google Scholar
  2. 2.
    Myung, H.G., Goodman, D.: Single Carrier FDMA: A new air interface for long term evolution. John Wiley & Sons Ltd., Chichester (2008)Google Scholar
  3. 3.
    Douillard, C., Jezequel, M., Berrou, C.: Iterative correction of intersymbol interference: Turbo equalization. Eur. Trans. Telecommun. 6(5), 507–511 (1995)CrossRefGoogle Scholar
  4. 4.
    Shannon, C.E.: A mathematical theory of communication. The Bell System Technical Journal 27, 379–423, 623–656 (1948)Google Scholar
  5. 5.
    Viterbi, A.J.: Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Trans. Inform. Theory IT-13, 260–269 (1967)CrossRefGoogle Scholar
  6. 6.
    Bahl, L., Cocke, J., Jelinek, F., Raviv, J.: Optimal decoding of linear codes for minimizing symbol error rate. IEEE Trans. Inf. Theory IT-20, 284–287 (1974)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Berrou, C., Glavieux, A., Thitimajshima, P.: Near shannon limit error-correcting coding: turbo codes. In: Proc. IEEE Int. Conf. Commun., Geneva, Switzerland, pp. 1064–1070 (1993)Google Scholar
  8. 8.
    Benedetto, S., Montorsi, G., Divsalar, D., Pollara, F.: Serial concatenation of intereleaved codes: Performance analysis, design and iterative decoding. In: TDA Progr. Rep. 42–126, Jet Propulsion Lab., Pasadena, CA, pp. 1–26 (1996)Google Scholar
  9. 9.
    Benedetto, S., Divsalar, D., Montorsi, G., Pollara, F.: Serial concatenation of intereleaved codes: Performance analysis, design and iterative decoding. IEEE Trans. Inform. Theory 44, 909–926 (1998)zbMATHMathSciNetCrossRefGoogle Scholar
  10. 10.
    Hagenauer, J., Hoeher, P.: A Viterbi algorithm with soft-decision outputs and its applications. In: Proc. IEEE GLOBECOM, Dallas, TX, pp. 47.1.1−47.1.7 (1989)Google Scholar
  11. 11.
    Tüchler, M., Koetter, R., Singer, A.: Turbo equalization: principles and new results. IEEE Trans. Commun. 50(5), 754–767 (2002)CrossRefGoogle Scholar
  12. 12.
    Tüchler, M., Singer, A., Koetter, R.: Minimum mean squared error equalization using a priori information. IEEE Trans. Signal Process. 50(3), 673–683 (2002)CrossRefGoogle Scholar
  13. 13.
    Glavieux, A., Laot, C., Labat, J.: Turbo equalization over a frequency selective channel. In: Proc. Int. Symp. Turbo Codes Related Topics, Brest, France, pp. 96–102, September 1997Google Scholar
  14. 14.
    Raphaeli, D., Saguy, A.: Linear equalizers for turbo equalization: A new optimization criterion for determining the equalizer taps. In: Proc. Int. Symp. Turbo Codes Related Topics, Brest, France, pp. 371–374, September 2000Google Scholar
  15. 15.
    Trajkovic, V.D.: Novel exact low complexity MMSE turbo equalization. In: IEEE 19th Int. Symp. Personal, Indoor and Mobile Radio Commun., pp. 1–5, September 15–18, 2008Google Scholar
  16. 16.
    Ampeliotis, D., Berberidis, K.: A linear complexity turbo equalizer based on a modified soft interference canceller. In: IEEE 7th WS Signal Proc. Advances in Wireless Commun., pp. 1–5, July 2–5, 2006Google Scholar
  17. 17.
    Benvenuto, N., Tomasin, S.: On the comparison between OFDM and single-carrier modulation with a DFE using a frequency-domain feed forward filter. IEEE Trans. Commun. 50(6), 947–955 (2002)CrossRefGoogle Scholar
  18. 18.
    Huang, G., Nix, A., Armour, S.: Decision feedback equalization in SC-FDMA. In: IEEE 19th Int. Symp. Personal, Indoor and Mobile Radio Commun., pp. 1–5, September 15–18, 2008Google Scholar
  19. 19.
    Wang, Q., Yuan, C., Zhang, J., Li, Y.: A robust low complexity frequency domain iterative block DFE for SC-FDMA system. In: IEEE Int. Conf. on Commun. pp. 5042–5046, June 9–13, 2013Google Scholar
  20. 20.
    Tuchler, M., Singer, A.C.: Turbo equalization: an overview. IEEE Trans. Inf. Theory 57(2), 920–952 (2011)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Wu, B., Niu, K., Gong, P., Sun, S.: An improved MMSE turbo equalization algorithm in frequency domain. In: IEEE 14th Int. Conf. Commun. Technology (ICCT), pp. 444–448, November 9–11, 2012Google Scholar
  22. 22.
    Jar, M., Bouton, E., Schlegel, C.: Frequency domain iterative equalization for single-carrier FDMA. In: IEEE 12th Int. WS Signal Proc. Advances in Wireless Commun., pp. 301–305, June 26–29, 2011Google Scholar
  23. 23.
    3GPP TS 36.211 3rd Generation Partnership Project; Technical Specification Group Radio Access Network; Evolved Universal Terrestrial Radio Access (E-UTRA). Physical Channels and ModulationGoogle Scholar
  24. 24.
    3GPP TS 36.212 3rd Generation Partnership Project; Technical Specification Group Radio Access Network; Evolved Universal Terrestrial Radio Access (E-UTRA). Multiplexing and channel codingGoogle Scholar
  25. 25.
    Sherman, J., Morrison, W.: Adjustment of an Inverse Matrix Corresponding to a Change in One Element of a Given Matrix. Annals of Mathematical Statistics 21(1), 124–127 (1950)zbMATHMathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Aleksandr Gelgor
    • 1
    Email author
  • Anton Gorlov
    • 1
  • Pavel Ivanov
    • 2
  • Evgenii Popov
    • 1
  • Andrey Arkhipkin
    • 2
  • Tatiana Gelgor
    • 1
  1. 1.Peter the Great St. Petersburg Polytechnic UniversitySt. PetersburgRussia
  2. 2.ZAO SBTMoscowRussia

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