Skip to main content
SpringerLink
Log in

Modified Multiple Feedback QR Aided Successive Interference Cancellation Algorithm for Large MIMO Detection

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Multiple-input multiple-output (MIMO) systems have attracted increased interest due to their capability to achieve higher multiplexing and diversity gains. In MIMO systems, reliable symbol detection in one of the major challenges. Maximum likelihood (ML) detection is one such technique which achieves minimum error rate performance for MIMO systems, however due to its exponential complexity ML detection is practically infeasible for large number of antennas. Therefore, in this contribution, we propose a low-complexity modified multiple feedback QR aided successive interference cancellation (MMF-SIC) algorithm which is capable of achieving near optimal performance. The effect of error propagation in SIC can be reduced by using multiple constellation points in decision feedback loops based on the reliability criteria. In MMF-SIC, an enhanced detection diversity is achieved by considering the decision feedback loops in multiple layers of QR aided SIC algorithm. Furthermore, we employ two different ordering schemes in parallel for QR decomposition in MMF-SIC algorithm. Through simulations, it is observed that the MMF-SIC algorithm performs superior over the conventional SIC and other SIC based techniques for detection in MIMO systems, and approach near optimal performance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Foschini, G. J., & Gans, M. J. (1998). On limits of wireless communications in a fading environment when using multiple antennas. Wireless Personal Communications, 6, 311–335.

    Article  Google Scholar 

  2. Teletar, I. E. (1999). Capacity of multi-antenna Gaussian channels. European Transaction on Telecommunications, 10, 585–595.

    Article  MathSciNet  Google Scholar 

  3. Chockalingam, A., & Rajan, B. S. (2014). Large MIMO systems. Cambridge: Cambridge University Press.

    Google Scholar 

  4. Paulraj, A., Nabar, R., & Gore, D. (2003). Introduction to space-time wireless communications. Cambridge: Cambridge University Press.

    Google Scholar 

  5. Viterbo, E., & Boutros, J. (1999). A universal lattice code decoder for fading channels. IEEE Transactions on Information Theory, 45(5), 1639–1642.

    Article  MathSciNet  MATH  Google Scholar 

  6. Wolniansky, P. W., Foschini, G. J., Golden, G. D., & Valenzuela, R. A. (1998). V-BLAST: An architecture for realizing very high data rates over the rich-scattering wireless channel. In International symposium on signals, systems, and electronics (pp 295–300).

  7. Zhou, Q., & Ma, X. (2013). Element-based lattice reduction algorithms for large MIMO detection. IEEE Journal on Selected Areas in Communications, 31(2), 274–286.

    Article  Google Scholar 

  8. Singhal, K. A., Datta, T., & Chockalingam, A. (2013). Lattice reduction aided detection in large-MIMO systems. In IEEE 14th workshop on signal processing advances in wireless communications (SPAWC) (pp. 594–598).

  9. Srinidhi, N., Datta, T., Chockalingam, A., & Sundar Rajan, B. (2011). Layered tabu search algorithm for large-MIMO detection and a lower bound on ML performance. IEEE Transactions on Communications, 59(11), 2955–2963.

    Article  Google Scholar 

  10. Lakshmi Narasimhan, T., & Chockalingam, A. (2014). Channel hardening-exploiting message passing (CHEMP) receiver in large-scale MIMO systems. IEEE Journal on Selected Topics in Signal Processing: Special Issue on Signal Processing for Large-Scale MIMO Communications, 8(5), 847–860.

    Article  Google Scholar 

  11. Fa, R., & de Lamare, R. C. (2009). Multi-branch successive interference cancellation for MIMO spatial multiplexing systems: Design, analysis and adaptive implementation. IET Communications, 5(4), 484–494.

    Article  Google Scholar 

  12. Li, P., de Lamare, R. C., & Fa, R. (2011). Multiple feedback successive interference cancellation detection for multiuser MIMO systems. IEEE Transactions on Wireless Communications, 10(8), 2434–2439.

    Article  Google Scholar 

  13. Peng, X., Wu, W., Sun, J., & Liu, Y. (2015). Sparsity-boosted detection for large MIMO systems. IEEE Communication Letters, 19(2), 191–194.

    Article  Google Scholar 

  14. Mandloi, M., & Bhatia, V. (2015). Congestion control based ant colony optimization algorithm for large MIMO detection. Expert Systems with Applications, 42(7), 3662–3669.

    Article  Google Scholar 

  15. Marinello, J. C., & Abrao, T. (2014). Lattice reduction aided detector for MIMO communication via ant colony optimisation. Wireless Personal Communications, 77(1), 6385.

    Article  Google Scholar 

  16. Mandloi, M. & Bhatia, V. (2015). Ordered iterative successive interference cancellation algorithm for large MIMO detection. In IEEE international conference on signal processing, informatics, communication and energy systems (SPICES) (pp. 1–5).

  17. Wubben, D., Bohnke, R., Kuhn, V., & Kammeyer, K. D. (2003). MMSE extension of V-BLAST based on sorted QR decomposition. In 2003 IEEE 58th vehicular technology conference, 2003. VTC 2003-Fall (pp. 508–512).

  18. Wubben, D., Bohnke, R., Kuhn, V., & Kammeyer, K. D. (2004). Near-maximum likelihood detection of MIMO systems using MMSE-based lattice reduction. In IEEE International Conference on Communications, 2, 798–802.

    Google Scholar 

  19. Wubben, D., Bohnke, R., Rinas, J., Kuhn, V., & Kammeyer, K. D. (2001). Efficient algorithm for decoding layered spacetime codes. Electronics Letters, 37(22), 1348–1350.

    Article  Google Scholar 

  20. Lee, H., Jeon, H., Choi, J., Kim, W., Cha, J., & Lee, H. (2006). A novel detection algorithm using the sorted QR decomposition based on log-likelihood ratio in V-BLAST systems. In International conference on wireless communication, networking and mobile computing, 2006. WiCOM 2006 (pp. 1–4). IEEE.

  21. Kobayashi, R. T., Ciriaco, F., & Abrao, T. (2015). Efficient near-optimum detectors for large MIMO systems under correlated channels. Wireless Personal Communications, 83, 1287–1311.

    Article  Google Scholar 

  22. Valente, R. A., Marinello, J. C., & Abrao, T. (2013). LR-aided MIMO detectors under correlated and imperfectly estimate channels. Wireless Personal Communications, 77, 173–196.

    Article  Google Scholar 

  23. Golub, G. H., & Loan, C. F. V. (1996). Matrix computations (3rd ed.). Baltimore: The Johns Hopkins University Press.

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Telecommunications Engineering, SVKM’s NMIMS University, Shirpur, 425405, India

    Manish Mandloi

  2. Discipline of Electrical Engineering, IIT Indore, Indore, 453552, India

    Vimal Bhatia

Authors
  1. Manish Mandloi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vimal Bhatia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Manish Mandloi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mandloi, M., Bhatia, V. Modified Multiple Feedback QR Aided Successive Interference Cancellation Algorithm for Large MIMO Detection. Wireless Pers Commun 98, 3393–3408 (2018). https://doi.org/10.1007/s11277-017-5020-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-017-5020-7

Keywords

Search

Navigation