Skip to main content
SpringerLink
Log in

Adaptive Beamformers for High-Speed Mobile Communication

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

In this work, we propose a fast conjugate gradient method (CGM) for beamforming, after thoroughly analyzing the performances of the least mean square (LMS), the recursive least square (RLS), and the sample matrix inversion (SMI) adaptive beamforming algorithms. Various experiments are carried out to analyze the performances of each beamformer in detail. The proposed conjugate gradient method does not use the Eigen spread of the signal correlation matrix as in the case of the LMS and the RLS methods. It computes antenna array weights orthogonally for each iteration. Hence the convergence rate and the null depths of the proposed method are much better than the LMS, the SMI the RLS and the classical CGM. Also, the simulation results confirm that this method has a speed improvement of about 60% over the classical conjugate gradient method. This aspect significantly reduces the processor burden and saves a lot of power during the beamforming process. Hence the proposed method is superior compared to the LMS, the RLS, the SMI, and classical CGM and most suitable for high-speed mobile communication.

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
Fig. 11

Similar content being viewed by others

References

  1. Huang, W., & Sheng, X. M. (2012). Modified projection approach for robust adaptive array beamforming. Signal Processing,92(7), 1758–1763.

    Article  Google Scholar 

  2. Gu, A. L. (2012). Robust adaptive beamforming based on interference covariance matrix reconstruction and steering vector estimation. IEEE Transactions on Signal Processing,60(7), 3881–3885.

    Article  MathSciNet  MATH  Google Scholar 

  3. Huang, L., Zhang, J., Xu, X., & Ye, Z. (2015). Robust adaptive beamforming with a novel interference-plus-noise covariance matrix reconstruction method. IEEE Transactions on Signal Processing,63(7), 1643–1650.

    Article  MathSciNet  MATH  Google Scholar 

  4. Khabbazibasmenj, S. A., & Vorobyov, A. H. (2012). Robust adaptive beamforming based on steering vector estimation with as little as possible prior information. IEEE Transactions on Signal Processing,60(6), 2974–2987.

    Article  MathSciNet  MATH  Google Scholar 

  5. Mukil, A. (2019). Kaiser window based blind beamformers for radar application. IETE Journal of Research. https://doi.org/10.1080/03772063.2019.1689188.

    Article  Google Scholar 

  6. Saxena, P., & Kothari, A. G. (2014). Performance analysis of adaptive beamforming algorithms for smart antennas. IERI Procedia,10, 131–137.

    Article  Google Scholar 

  7. Bakhar, Md. (2019). Advances in smart antenna systems for wireless communication. Springer Wireless Personal Communications.. https://doi.org/10.1007/s11277-019-06764-6.

    Article  Google Scholar 

  8. Jiang, X., Zeng, W.-J., Yasotharan, A., So, H. C., & Kirubarajan, T. (2015). Quadratically constrained minimum dispersion beamforming via gradient projection. IEEE Transactions on Signal Processing,63(1), 192–205.

    Article  MathSciNet  MATH  Google Scholar 

  9. Tian, Z., Bell, K. L., & Van Trees, H. L. (2001). A recursive least squares implementation for lcmp beamforming under quadratic constraint. IEEE Transactions on Signal Processing,49(6), 1138–1145.

    Article  Google Scholar 

  10. de Lamare Ruan, R. C. (2016). Robust adaptive beamforming based on low-rank and cross-correlation techniques. IEEE Transactions on Signal Processing,64(15), 3919–3932.

    Article  MathSciNet  MATH  Google Scholar 

  11. Chang, P. S., & Willson, A. N. (2000). Analysis of conjugate gradient algorithms for adaptive filtering. IEEE Transactions on Signal Processing,48(2), 409–418.

    Article  MATH  Google Scholar 

  12. de Lamare Wang, R. (2012). Set-membership constrained conjugate gradient adaptive algorithm for beamforming. IET Signal Processing,6(8), 789–797.

    Article  MathSciNet  Google Scholar 

  13. Vani, (2018). Efficient blind beamfoming algorithms for phased array and MIMO radar. IETE Journal of Research,64(2), 241–246. https://doi.org/10.1080/03772063.2017.1351319.

    Article  Google Scholar 

  14. Jiang, H., & Li, M. R. (2012). On the conjugate gradient matched filter. IEEE Transactions on Signal Processing,60(5), 2660–2666.

    Article  MathSciNet  MATH  Google Scholar 

  15. Schlosser, R., Heckler, M. V. T., Sperandio, M., & Machado, R. (2013). Synthesis of linear antenna arrays for radio base stations. Orlando: IEEE Antennas Propagation Society International Symposium.

    Book  Google Scholar 

  16. Kwong, R. H., & Johnston, E. W. (1992). A variable step size LMS algorithm. IEEE Transactions on Signal Processing,40, 1633–1642.

    Article  MATH  Google Scholar 

  17. Slock, T. M. (1993). On the convergence behavior of the LMS and the normalized LMS algorithms. IEEE Transactions on Signal Processing,41, 2811–2825.

    Article  MATH  Google Scholar 

  18. Rupp, M. (1993). The behavior of LMS and NLMS algorithms in the presence of spherically invariant processes. IEEE Transactions on Signal Processing,41, 1149–1160.

    Article  MATH  Google Scholar 

  19. Srar, J. A., Chung, K. S., & Mansour, A. (2010). Adaptive array beamforming using a combined LMS–LMS algorithm. IEEE Transactions on Antennas and Propagation,58, 3545–3557.

    Article  Google Scholar 

  20. Lopes, P. A. C., Tavares, G., & Gerald, J. B. (2007). A new type of normalized LMS algorithm based on the Kalman filter. In Proceedings of the IEEE international conference on acoustics, speech and signal processing, Hawaii, U.S.A. (pp. 1345–1348).

  21. Veerendra, D. (2019). Adaptive beamforming algorithms using 2D-novel ULA for wireless communications. SN Applied Sciences,1(9), 1–9. https://doi.org/10.1007/s42452-019-1009-z.

    Article  Google Scholar 

  22. Xiao, Xiao, & Yilong, Lu. (2019). Data-Based Model For Wide Nulling Problem In Adaptive Digital Beamforming Antenna Array. Antennas and Wireless Propagation Letters IEEE,18(11), 2249–2253.

    Article  Google Scholar 

  23. Mandyam, G. D. (1997). Adaptive beamforming based on the conjugate gradient algorithm. IEEE Trans on AES,33(1), 343–347.

    Google Scholar 

  24. Boray, G. K., & Srinath, M. D. (1992). Conjugate gradient techniques for adaptive filtering. IEEE Transactions on Circuits and Systems,39(1), 1–10.

    Article  Google Scholar 

  25. Vani, R. M. (2014). Implementation and optimization of modified MUSIC algorithm for high resolution DOA estimation. In Proceedings of IEEE international microwave and RF conference, December 2014 (pp. 190–193). https://doi.org/10.1109/imarc.2014.7038985.

  26. Liu, Y., & Cuia, H. (2015). Antenna array signal direction of arrival estimation on digital signal processor (DSP). Procedia Computer Science,55, 782–791.

    Article  Google Scholar 

  27. Ali, R. L. (2012). A robust least mean square algorithm for adaptive array signal processing. Wireless Personal Communications,68(4), 1449–1461.

    Article  Google Scholar 

  28. Rana, M. M. (2011). Performance comparison of LMS and RLS channel estimation algorithms for 40 MIMO OFDM systems. In Proceedings of IEEE international conference on computer and information technology, December.

  29. Bakhar, M. (2019). Smart antenna system for DOA estimation using single snapshot. Springer Wireless Personal Communications.,107(1), 81–93. https://doi.org/10.1007/s11277-019-06241-0.

    Article  Google Scholar 

  30. Wang, L., & de Lamare, R. C. (2010). Constrained adaptive filtering algorithms based on conjugate gradient techniques for beamforming. IET Signal Processing,4, 686–697.

    Article  Google Scholar 

  31. Chang, P. S., & Willson, A. N. (2000). Analysis of conjugate gradient algorithms for adaptive filtering. IEEE Transactions on Signal Processing,48, 409–418.

    Article  MATH  Google Scholar 

  32. Shubair, R., Merri, A., & Jessmi. (2018). Improved adaptive beamforming using a hybrid LMS/SMI approach. In Proceedings of IEEE wireless and optical communications networks conference (pp. 603–606). https://doi.org/10.1109/wocn.2005.1436097.

  33. Khalaf, A. A. M., El-Daly, A.-R. B. M., & Hamed, H. F. A. (2018). A hybrid NLMS/RLS algorithm to enhance the beamforming process of smart antenna systems. Journal of Telecommunication, Electronic and Computer Engineering,10(1–4), 15–22.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Lincoln University College, 47301, Petaling Jaya, Kuala Lumpur, Malaysia

    Veerendra Dakulagi & Mukil Alagirisamy

Authors
  1. Veerendra Dakulagi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mukil Alagirisamy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Veerendra Dakulagi.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dakulagi, V., Alagirisamy, M. Adaptive Beamformers for High-Speed Mobile Communication. Wireless Pers Commun 113, 1691–1707 (2020). https://doi.org/10.1007/s11277-020-07287-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-020-07287-1

Keywords

Search

Navigation