Skip to main content

Advertisement

Log in

Efficient Near-Optimum Detectors for Large MIMO Systems Under Correlated Channels

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Recently, high spectral and energy efficiencies multiple antennas wireless systems under scattered environments have attracted an increasing interest due to their intrinsically benefits. This work focuses on the analysis of MIMO equalizers, improved MIMO detection techniques and their combinations, allowing a good balance between complexity and performance in Rayleigh channel environment. Primarily, the MIMO linear equalizers combined with detection techniques such as ordering (via sorted QR decomposition), successive interference cancellation, list reduction and lattice reduction were investigated. An important aspect invariably present in practical systems taken into account in the analysis has been the channel correlation effect, which under certain realistic conditions could result in a strong negative impact on the MIMO system performance. The goal of this paper consists in construction a framework on sub-optimum MIMO detection techniques, pointing out a MIMO detection architecture able to attain low or moderate complexity, suitable performance and full diversity.

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.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Bai, L., & Choi, J. (2012). Low complexity MIMO detection. Berlin: Springer.

    Book  Google Scholar 

  2. Biglieri, E. (2007). MIMO wireless communications. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  3. Boccardi, F., Heath, R., Lozano, A., Marzetta, T., & Popovski, P. (2014). Five disruptive technology directions for 5g. IEEE Communications Magazine, 52(2), 74–80. doi:10.1109/MCOM.2014.6736746.

    Article  Google Scholar 

  4. Bolcskei, H. (2006). Mimo-ofdm wireless systems: Basics, perspectives, and challenges. IEEE Wireless Communications, 13(4), 31–37. doi:10.1109/MWC.2006.1678163.

    Article  Google Scholar 

  5. Choi, J., & Nguyen, H. (2009). SIC-based detection with list and lattice reduction for mimo channels. IEEE Transactions on Vehicular Technology, 58(7), 3786–3790.

    Article  Google Scholar 

  6. Cox, A., Higham, N. & Manchester Centre for Computational Mathematics. (1997). Stability of householder QR factorization for weighted least squares problems. Numerical analysis report, Manchester Centre for Computational Mathematics.

  7. Fischer, R. F. H., & Windpassinger, C. (2003). Real versus complex-valued equalisation in V-BLAST systems. Electronics Letters, 39(5), 470–471. doi:10.1049/el:20030331.

    Article  Google Scholar 

  8. Gan, Y. H., Ling, C., & Mow, W. H. (2009). Complex lattice reduction algorithm for low-complexity full-diversity MIMO detection. IEEE Transactions on Signal Processing, 57(7), 2701–2710. doi:10.1109/TSP.2009.2016267.

    Article  MathSciNet  Google Scholar 

  9. Gander, W. (1980). Algorithms for the QR decomposition. Technical report, Eidgenössische Technische Hochschule, Zürich. www.inf.ethz.ch/personal/gander/papers/qrneu.pdf.

  10. Goldsmith, A. (2005). Wireless communications. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  11. Golub, G. H., & Van Loan, C. F. (1996). Matrix computations (3rd ed.). Baltimore, MD: Johns Hopkins University Press.

    MATH  Google Scholar 

  12. Hassibi, B., & Vikalo, H. (2005). On the sphere-decoding algorithm. I. Expected complexity. IEEE Transactions on Signal Processing, 53(8), 2806–2818. doi:10.1109/TSP.2005.850352.

    Article  MathSciNet  Google Scholar 

  13. Jalden, J., & Ottersten, B. (2005). On the complexity of sphere decoding in digital communications. IEEE Transactions on Signal Processing, 53(4), 1474–1484. doi:10.1109/TSP.2005.843746.

    Article  MathSciNet  Google Scholar 

  14. Kim, I. M. (2006). Exact BER analysis of OSTBCs in spatially correlated MIMO channels. IEEE Transactions on Communications, 54(8), 1365–1373. doi:10.1109/TCOMM.2006.878823.

    Article  Google Scholar 

  15. Kühn, V. (2006). Wireless communications over MIMO channels—Applications to CDMA and multiple antenna systems. Chichester: Wiley.

    Book  Google Scholar 

  16. Lenstra, H., Lenstra, A., & Lovász, L. (1982). Factoring polynomials with rational coefficients. Mathematische Annalen, 261, 515–534.

    Article  MathSciNet  MATH  Google Scholar 

  17. Ma, X., & Zhang, W. (2008). Performance analysis for MIMO systems with lattice-reduction aided linear equalization. IEEE Transactions on Communications, 56(2), 309–318. doi:10.1109/TCOMM.2008.060372.

    Article  Google Scholar 

  18. Paulraj, A., Gore, D., Nabar, R., & Bolcskei, H. (2004). An overview of MIMO communications—A key to gigabit wireless. Proceedings of the IEEE, 92(2), 198–218. doi:10.1109/JPROC.2003.821915.

    Article  Google Scholar 

  19. Rusek, F., Persson, D., Lau, B. K., Larsson, E., Marzetta, T., Edfors, O., et al. (2013). Scaling up MIMO: Opportunities and challenges with very large arrays. IEEE Signal Processing Magazine, 30(1), 40–60. doi:10.1109/MSP.2011.2178495.

    Article  Google Scholar 

  20. Szczecinski, L., & Massicotte, D. (2005). Low complexity adaptation of MIMO MMSE receivers, implementation aspects. In Global telecommunications conference, 2005. GLOBECOM ’05. IEEE (vol. 4, pp. 6–2332). doi:10.1109/GLOCOM.2005.1578079.

  21. Valente, R., Marinello, J.C., & Abrão, T. (2013). LR-aided MIMO detectors under correlated and imperfectly estimated channels. Wireless Personal Communications, 1–24. doi:10.1007/s11277-013-1500-6.

  22. Waters, D., & Barry, J.: The sorted-QR chase detector for multiple-input multiple-output channels. In Wireless Communications and Networking Conference, 2005 IEEE (vol. 1, pp. 538–543). doi:10.1109/WCNC.2005.1424558.

  23. Waters, D., & Barry, J. (2008). The chase family of detection algorithms for multiple-input multiple-output channels. IEEE Transactions on Signal Processing, 56(2), 739–747. doi:10.1109/TSP.2007.907904.

    Article  MathSciNet  Google Scholar 

  24. Wolniansky, P., Foschini, G., Golden, G., & Valenzuela, R. (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, 1998. ISSSE 98. 1998 URSI (pp. 295–300). doi:10.1109/ISSSE.1998.738086.

  25. 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 (vol. 1, pp. 508–512).

  26. Wubben, D., Bohnke, R., Kuhn, V., & Kammeyer, K.D.: Near-maximum-likelihood detection of MIMO systems using MMSE-based lattice reduction. In IEEE international conference on communications (vol. 2, pp. 798–802). doi:10.1109/ICC.2004.1312611.

  27. Wubben, D., Bohnke, R., Rinas, J., Kuhn, V., & Kammeyer, K. D. (2001). Efficient algorithm for decoding layered space–time codes. Electronics Letters, 37(22), 1348–1350. doi:10.1049/el:20010899.

    Article  Google Scholar 

  28. Zelst, A. V., & Hammerschmidt, J. S. (2002). A single coefficient spatial correlation model for multiple-input multiple-output MIMO radio channels. In in Proceedings of URSI XXVIIth general assembly (pp. 1–4).

Download references

Acknowledgments

This work was supported in part by the National Council for Scientific and Technological Development (CNPq) of Brazil under Grants 202340/2011-2, 303426/2009-8 and in part by State University of Londrina – Paraná State Government (UEL), scholarship PROIC-2013-14.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Taufik Abrão.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kobayashi, R.T., Ciriaco, F. & Abrão, T. Efficient Near-Optimum Detectors for Large MIMO Systems Under Correlated Channels. Wireless Pers Commun 83, 1287–1311 (2015). https://doi.org/10.1007/s11277-015-2450-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-015-2450-y

Keywords

Navigation