Skip to main content
Log in

Mitigating Empty Vector Set Using Enlarged QRLRL-M Soft SM-MIMO Detector

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

An enlargement of the candidate vector set of QR-least reliable layer (QR-LRL) based MIMO detector for efficient soft output generation is proposed. Previous work (Bahng et al. in IEICE Trans Commun, E89–B(10):2956–2960, 2008) shows that the QR-LRL based MIMO detector approaches hard decision output ML performance, but does not match soft output ML performance due to empty candidate vector set problem. Performance degradation is more severe when modulation order is low. Some of the previous methods have provided solutions to empty vector set (EVS) problem (Kawai et al. in IEICE Trans Commun, E88–B(1):47–57, 2005; Bahng et al. in IEICE Trans Commun, E89–B(10):2956–2960, 2008; Kim et al. in IEICE Trans Commun, E92–B(11):3512–3515, 2009), but are not efficient in terms of performance or computation complexity. In this paper, we enlarge the candidate vector set of QR-LRL detector by applying every constellation point at each layer. The proposed detector thus effectively solves the EVS problem and achieves soft ML performance while keeping the computation complexity low, especially at low modulation order.

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
Fig. 8

Similar content being viewed by others

References

  1. van Etten, W. (1976). Maximum likelihood receiver for multiple channel transmission systems. IEEE Transactions on Communications, 24(2), 276–283.

    Article  MATH  Google Scholar 

  2. Wolniansky, P. W., Foschini, G. J., Golden, G. D., & R.A. Valenzuela. (1998). V-BLAST: An architecture for realizing very high data rates over the rich-scattering wireless channel. In Proc. URSI ISSSE, pp. 295–300.

  3. Foscchini, G. J., Golden, G. D., & Valenzuela, R. A. (1999). Simplified processing for high spectral efficiency wireless communication employing multi-element arrays. IEEE Journal on Selected Areas in Communications, 17(11), 1841–1852.

    Article  Google Scholar 

  4. Kawai, H., Higuchi, K., Maeda, N., sawahashi, M., Ito, T., Kaakura, Y., et al. (2005). Likelihood function for QRM-MLD suitable for soft-decision turbo decoding and its performance for OFCDM MIMO multiplexing in multipath fading channel. IEICE Transactions on Communications, E88–B(1), 47–57.

    Article  Google Scholar 

  5. Kim, K. J., Yue, J., Iltis, R. A., & Gibson, J. D. (2005). A QRD-M/Kalman filter based detection and channel estimation algorithm for MIMO-OFDM systems. IEEE Transactions on Wireless Communications, 4(2), 710–721.

    Article  Google Scholar 

  6. Kawai, H., Higuchi, K., Maeda, N., & Sawahashi, M. (2006). Adaptive control of surviving symbol replica candidates in QRM-MLD for OFDM MIMO multiplexing. IEEE Journal on Selected Areas in Communication, 24(6), 1130–1140.

    Article  Google Scholar 

  7. Kim, J., Kim, D., & Yun, S. (2006). Mitigating error propagation in successive interference cancellation. IEICE Transactions on Communications, E89–B(10), 2956–2960.

    Article  MathSciNet  Google Scholar 

  8. Shin, W., Kim, H., Son, M., & Park, H. (2007). An improved LLR computation for QRM-MLD in coded MIMO systems. In Proc. VTC, pp. 447–451 Fall.

  9. Bahng, S., Park, Y. K., & Kim, J. (2008). QR-LRL detection for spatially multiplexed MIMO systems. IEICE Transactions on Communications, E91–B(10), 3383–3386.

    Article  Google Scholar 

  10. Kim, J., Park, Y. K., & Bahng, S. (2009). Efficient soft-output generation method for spatially multiplexed MIMO systems. IEICE Transactions on Communications, E92–B(11), 3512–3515.

    Article  Google Scholar 

  11. Kim, K., Jung, Y., Lee, S., & Kim, J. (2012). Efficient list extension algorithm using multiple detection orders for soft-output MIMO detection. IEICE Transactions on Communications, E95-B(3).

  12. Cho, Y. S., Kim, J., Yang, Won Y., & Kang, C. G. (2010). MIMO-OFDM wireless communications with MATLAB. Hoboken: Wiley.

    Book  Google Scholar 

  13. Rawal, D., Chakka, V., Park, Y.-O., Bahng, S., & Park, H. S. (2014). Enlarged QR-LRL based SM-MIMO detector for efficient soft output generation. In International Conference on TENCON-2014, Bangkok.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Divyang Rawal.

Additional information

The work has been carried out as part of the activity under the project Mobile Broadband Service Support Over Cognitive Radio Networks sponsored by Information Technology Research Agency (ITRA), Department of Electronics and Information Technology (DeitY), Govt. of India.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rawal, D., Chakka, V., Park, YO. et al. Mitigating Empty Vector Set Using Enlarged QRLRL-M Soft SM-MIMO Detector. Wireless Pers Commun 83, 1341–1358 (2015). https://doi.org/10.1007/s11277-015-2454-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-015-2454-7

Keywords

Navigation