Advertisement

Low-Complexity Iterative MIMO Detection Based on Turbo-MMSE Algorithm

  • Mikhail Bakulin
  • Vitaly Kreyndelin
  • Andrey Rog
  • Dmitry PetrovEmail author
  • Sergei Melnik
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10531)

Abstract

Next generation Multiple Input Multiple Output (MIMO) communication systems should meet strict performance requirements and keep reasonable complexity. This paper presents a new low-complexity approach for iterative MIMO detection which is based on enhanced Turbo procedure. In the algorithm such components as linear Minimum Mean Square Error (MMSE) detection and soft symbol estimation based on the MMSE solution are utilized. A new original procedure of getting extrinsic data essentially improves the receiver performance and reduces its complexity. The results of the study are validated by link-level simulations.

Keywords

MIMO Turbo decoding Iterative detection MMSE ML 

Notes

Acknowledgment

The publication was financially supported by the Ministry of Education and Science of the Russian Federation (the Agreement number 02.a03.21.0008).

References

  1. 1.
    Vannithamby, R., Talwar, S.: Towards 5G: Applications, Requirements and Candidate Technologies. Wiley, Chichester (2017)Google Scholar
  2. 2.
    Luo, F.-L., Zhang, C.: Signal Processing for 5G: Algorithms and Implementations. Wiley, Chichester (2016)CrossRefGoogle Scholar
  3. 3.
    Wolniansky, P.W., Foschini, G.J., Golden, G.D., Valenzuela, R.A.: V-BLAST: an architecture for realizing very high data rates over the rich-scattering wireless channel. In: International Symposium on Signals, Systems, and Electronics (ISSSE 1998), Pisa, Italy (1998)Google Scholar
  4. 4.
    Golden, G.D., Foschini, G.J., Valenzuela, R.A., Wolniansky, P.W.: Detection algorithm and initial laboratory results using V-BLAST space-time communication architecture. Electron. Lett. 35(1), 14–16 (1999)CrossRefGoogle Scholar
  5. 5.
    Hassibi, B., Vikalo, H.: On the sphere-decoding algorithm I. Expected complexity. IEEE Trans. Sig. Process. 53(8), 2806–2818 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Dai, Y., Sun, S., Lei, Z.: A comparative study of QRD-M detection and sphere decoding for MIMO-OFDM systems. In: Personal, Indoor and Mobile Radio Communications (PIMRC), pp. 186–190, 11–14 September 2005Google Scholar
  7. 7.
    Wubben, D., Bonke, R., Kuhn, V., Kammeyer, K.-D.: MMSE extension of V-BLAST based on sorted QR decomposition. In: IEEE 58th Vehicular Technology Conference (VTC-Fall), vol. 1, pp. 508–512 (2003)Google Scholar
  8. 8.
    Lee, H., Lee, I.: New approach for coded layered space-time OFDM systems. In: IEEE International Conference on Communications (ICC), vol. 1, pp. 608–612, May 2005Google Scholar
  9. 9.
    Wang, J., Li, S.: Soft versus hard interference cancellation in MMSE OSIC MIMO detector: a comparative study. In: 4th International Symposium on Wireless Communication Systems (ISWCS), pp. 642–646, October 2007Google Scholar
  10. 10.
    Wang, J., Wen, O.Y., Li, S.: Soft-output MMSE-OSIC MIMO detector with reduced-complexity approximations. In: IEEE 8th Workshop on Signal Processing Advances in Wireless Communications (SPAWC), pp. 1–5, June 2007Google Scholar
  11. 11.
    Rog, A., Goreinov, S., Wu, D.S., Kim, D.: Method of MIMO signal decoding. Patent RU 2007114146, 16 April 2007Google Scholar
  12. 12.
    Iqbal, A., Kabir, M.H., Kwak, K.S.: Enhanced zero forcing ordered successive interference cancellation scheme for MIMO system. In: 2013 International Conference on ICT Convergence (ICTC), pp. 979–980, October 2013Google Scholar
  13. 13.
    Studer, C., Fateh, S., Seethaler, D.: ASIC implementation of soft-input soft-output MIMO detection using MMSE parallel interference cancellation. IEEE J. Solid-State Circ. 46(7), 1754–1765 (2011)CrossRefGoogle Scholar
  14. 14.
    3GPP TS 36.211 v12.2.0, Evolved Universal Terrestrial Radio Access (E-UTRA); Physical Channels and Modulation (Release 12)Google Scholar
  15. 15.
    3GPP TS 36.212 v12.1.0, Evolved Universal Terrestrial Radio Access (E-UTRA); Multiplexing and channel coding (Release 12)Google Scholar
  16. 16.
    3GPP TS 36.213 v12.2.0, Evolved Universal Terrestrial Radio Access (E-UTRA); Physical layer procedures (Release 12)Google Scholar
  17. 17.
    3GPP TS 36.104 v12.4.0, Evolved Universal Terrestrial Radio Access (E-UTRA); Base Station (BS) radio transmission and reception (Release 12)Google Scholar
  18. 18.
    Gentle, J.E.: Matrix Algebra: Theory, Computations, and Applications in Statistics, p. 95. Springer, New York (2007)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Mikhail Bakulin
    • 1
  • Vitaly Kreyndelin
    • 1
  • Andrey Rog
    • 2
  • Dmitry Petrov
    • 3
    Email author
  • Sergei Melnik
    • 4
  1. 1.IT DepartmentMoscow Technical University of Communications and Informatics (MTUSI)MoscowRussian Federation
  2. 2.GlobalInformServiceMoscowRussian Federation
  3. 3.Peoples’ Friendship University of Russia (RUDN University)MoscowRussian Federation
  4. 4.Central Scientific Research Institute of CommunicationMoscowRussian Federation

Personalised recommendations