Skip to main content
SpringerLink
Log in

MIMO blind equalization algorithm based on successive interference cancellation

  • Published:
Telecommunication Systems Aims and scope Submit manuscript

Abstract

Modified Constant Modulus Algorithm (MCMA) is widely used in the SISO blind equalization for its simplicity. For MIMO systems MCMA can only equalize one of the source signals. Through the combination of channel estimation and successive interference cancellation, source signals can be equalized in turn. However the recovery of the first source and the channel estimation are the key points, which directly affects the recovery of the subsequent sources. This paper proposes a channel estimation method with a small amount of calculation, and can accurately estimate the channel vector. Meanwhile, a new blind equalization algorithm is put forward to reliably recovery the first source signal. Simulation results verify the effectiveness of the proposed algorithm.

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Li, Y., & Ray, K. J. (1998). Adaptive blind source separation and equalization for multiple-input/multiple-output systems. IEEETransactions on Information Theory, 44(7), 2864–2876.

    Article  Google Scholar 

  2. de Lamare, R. C., & Sampaio-Neto, R. (2011). Adaptive reduced-rank equalization algorithms based on alternating optimization design techniques for MIMO systems. IEEE Transactions on Vehicular Technology, 60(6), 2482–2494.

    Article  Google Scholar 

  3. Azim, A. W., et al. (2015). Steady-state performance of multimodulus blind equalizers. Signal Processing, 108, 509–520.

    Article  Google Scholar 

  4. Papadias, C. B., & Paulraj, A. (1996). A space-time constant modulus algorithm for SDMA systems. In Proceedings IEEE 46th vehicular technology conference, (pp. 86–90).

  5. Papadias, C. B., & Paulraj, A. (1997). A constant modulus algorithm for multiuser signal separation in presence of delay spread using antenna arrays. IEEE Signal Processing Letters, 6(4), 178–181.

    Article  Google Scholar 

  6. Lee, Z. H., & Lim, G. W. (2010). Hybrid methods in MIMO blind equalization and source separation. In IEEE symposium on industrial electronics and application, (pp. 5–9).

  7. Gu, N., Guan, Z., Nahavandi, S., & Xiang, Y. (2006). A new blind signal separation algorithm for instantaneous MIMO system. In IEEE GLOBECOM 2006 proceedings.

  8. Shi, K., & Zhang, X. (2006). Blind equalization of MIMO systems based on orthogonal constant modulus algorithm. Journal of Electronics, 23(2), 181–183.

  9. Moazzami, F., & Cole-Rhodes, A. (2011). Multistage blind equalization in time-varying MIMO systems. In 2011 45th Annual conference on information sciences and systems (CISS) (pp. 2–3).

  10. Moazzami, F., & Cole-Rhodes, A. (2008). An adaptive blind equalizer with signal separation for a MIMO system transmitting QAM signals. In Military communications conference, MILCOM 2008. IEEE (pp. 1–5).

  11. Fki, S., et al. (2014). Blind equalization based on pdf fitting and convergence analysis. Signal Processing, 101, 266–277.

  12. Lazaro, M., Santamaria, I., Erdogmus, D., Hild, K., Pantaleon, C., & Principe, J. (2005). Stochastic blind equalization based on pdf fitting using parzon estimator. IEEE Transactions on Signal Processing, 53(2), 696–704.

    Article  Google Scholar 

  13. Fki, S., Messai, M., & Chonavel, T. (2014). New criteria for blind equalization based on pdf fitting. In IEEE 15th international workshop on signal processing advances in wireless communications, (pp. 489–493).

  14. Tong, L., & Perreau, S. (1998). Multichannel blind identification: From subspace to maximum likelihood methods. In Proceedings of the IEEE, 1998, 86:1951–1968.

  15. Bay, B. (2004). Blind channel estimation via combination autocorrelation and blind phase estimation. IEEE Transactions on Circuits and Systems, 6(51), 1125–1131.

    Google Scholar 

  16. Moazzami, F., & Cole, A. (2009). Blind source separation with CMA + AMA equalization over correlation MIMO channel, Department of electrical and computer engineering, (pp. 1–7).

Download references

Author information

Authors and Affiliations

  1. Xidian University, Shannxi, 710071, China

    Yongjun Sun, Fanli Wang & Zujun Liu

  2. Qindao Topscomm Communication Co. Ltd, Qingdao, 266024, China

    Dongmin Li

Authors
  1. Yongjun Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fanli Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dongmin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zujun Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yongjun Sun.

Additional information

This work was supported in part by the National Natural Science Foundation of China under Grants 61571340 and the program of Introducing Talents of Discipline to Universities under Granted B0803.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sun, Y., Wang, F., Li, D. et al. MIMO blind equalization algorithm based on successive interference cancellation. Telecommun Syst 68, 145–150 (2018). https://doi.org/10.1007/s11235-017-0347-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11235-017-0347-7

Keywords

Search

Navigation