Skip to main content
SpringerLink
Log in

Wireless channel estimation and beamforming by using block sparse adaptive filtering

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Channel estimation normally provides information about indoor and outdoor fading channel statistics. The adaptive channel estimation models play an important role to generate the required channel state information (CSI) using the estimated channel coefficient vector. The CSI can be utilized to generate an angle vector that controls the steering mechanism of a beamformer. The beamformer provides better directive gain for linear antenna array and helps to improve the signal to noise ratio of the wireless receiver. The proposed estimation model process the transmitted quadrature amplitude modulation (QAM) data samples in the frequency domain. The adaptive design incorporates norm-based sparsity through block recursive least square (BRLS) algorithm to develop a computationally efficient model. The proposed sparse-FBRLS (Fast BRLS) model has simultaneously addressed the problems of channel estimation and beamforming in case of indoor and outdoor communication. The performance of the model is tested by different performance measures under practical mobility conditions.

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. Haykin, S.S.: Adaptive Filter Theory, 3 edn. Pearson Education India, p. 989 (1996)

  2. Yang, K.-J., Wang, C.-L., Tsai, Y.-R.: Joint channel and Doppler spread estimation over time varying flat fading channels. In: Vehicular Technology Conference (VTC Spring) (2012)

  3. Madni, H.M.F.A., Ali, B., Butt, M.F.U.: Study and analysis of channel estimation techniques for OFDM based wireless communication systems. In: 12th International Conference on Frontiers of Information Technology, Islamabad, pp. 56–60 (2014). https://doi.org/10.1109/fit.2014.20

  4. Zhang, Z., Xiao, L., Su, X., Zeng, J., Xu, X.: A channel estimation method based on the improved LMS algorithm for MIMO-OFDM systems. In: 2018 12th International Symposium on Medical Information and Communication Technology (ISMICT), Sydney, NSW, pp. 1–5 (2018). https://doi.org/10.1109/ismict.2018.8573728

  5. Acar, Y., Dogati, H., Panayirci, E.: Iterative channel estimation for spatial modulation systems over fast fading channels. In: Signal Processing and Communications Applications Conference (2014)

  6. Mohanty, B., Sahoo, H.K., Patnaik, B.: Block NLMS/F based equalizer design and channel capacity analysis for indoor IEEE 802.11 fading wireless channels. Signal Image Video Process. 13(4), 693–701 (2019)

    Article  Google Scholar 

  7. Sahoo, H.K., Mohanty, B., Patnaik, B.: Block and fast block sparse adaptive filtering for outdoor wireless channel estimation and equalization. Wirel. Pers. Commun. 98(3), 3003–3019 (2017)

    Article  Google Scholar 

  8. Dakulagi, V., Bakhar, M.: Advances in smart antenna systems for wireless communication. Wirel. Pers. Commun. 110, 931–957 (2020). https://doi.org/10.1007/s11277-019-06764-6

    Article  Google Scholar 

  9. Van Veen, B.D., Buckley, K.M.: Beam forming: a versatile approach to spatial filtering. IEEE ASSP Mag. 5(2), 4–24 (1998)

    Article  Google Scholar 

  10. Xi, Z., Zhang, W.: DOA estimation and self-calibration in partially calibrated subarray-based uniform linear arrays. SIViP 14, 233–239 (2020)

    Article  Google Scholar 

  11. Gao, Q., Stewart, R.: Improved double angle complex rotation QRD-RLS. In: Proceedings of the 19th ACM/SIGDA International Symposium on Field Programmable Gatearrays-FPGA’, pp. 79–82 (2011)

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

    Article  Google Scholar 

  13. Poormohammad, S., Farzaneh, F.: Precision of direction of arrival (DOA)estimaiton using novel three dimensional array geometries. AEU Int. J. Electron. Commun. 75, 35–45 (2017)

    Article  Google Scholar 

  14. Li, Y., Wang, Y., Jiang, T.: Sparse-aware set-membership NLMS algorithms and their application for sparse channel estimation and echo cancelation. AEÜ Int. J. Electron. Commun. 70(7), 895–902 (2016)

    Article  Google Scholar 

  15. Babadi, B., Kalouptsidis, N., Tarokh, V.: Comparison of SPARLS and RLS algorithms for adaptive filtering. In: Proceedings of the IEEE Sarnoff Symposium (2009)

  16. Berraki, D.E., Armour, S.M.D., Nix, A.R.: Benefits of the sparsity of mmWave outdoor spatial channels for beamforming and interference cancellation. Phys. Commun. 27, 170–180 (2018)

    Article  Google Scholar 

  17. Andersen, J.B., Rappaport, T.S., Yoshida, S.: Propagation measurements and models for wireless communication channel. IEEE Commun. Mag. 33(1), 42–49 (1995)

    Article  Google Scholar 

  18. Raza, H., Khan, N.M.: Tracking performance of robust RLS algorithm for MIMO channel estimation. Wirel. Pers. Commun. 111, 395–409 (2020)

    Article  Google Scholar 

  19. Datta, A., Hari, K.V.S., Hanzo, L.: Channel estimation relying on the minimum bit error ratio criterion for BPSK and QPSK signals. IET Commun. 8, 69–76 (2014)

    Article  Google Scholar 

  20. Shrestha, Deep, Mestre, Xavier, Payaro, Miquel: On channel estimation for power line communication system in the presence of impulsive noise. Comput. Electr. Eng. 72, 406–419 (2018)

    Article  Google Scholar 

  21. Dakulagi, V., Bakhar, M.: Smart antenna system for DOA estimation using single snapshot. Wirel. Pers. Commun. 107(1), 81–93 (2019)

    Article  Google Scholar 

  22. Srar, J.A., Chung, K.-S., Mansour, J.A.: Adaptive array beamforming using a combined LMS–LMS algorithm. IEEE Trans. Antennas Propag. 58(11), 3545–3557 (2010)

    Article  Google Scholar 

  23. Lathi, B.P.: Modern Digital and Analog Communication Systems. Oxford University Press, Inc., Oxford (1995)

    Google Scholar 

Download references

Acknowledgements

We express our sincere thanks to VSSUT Burla and IIIT Bhubaneswar for providing necessary research facilities to complete our research work.

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, IIIT, Bhubaneswar, India

    Basabadatta Mohanty & Bijayananda Patnaik

  2. Department of Electronics and Telecommunication Engineering, Veer Surendra Sai University of Technology, Burla, Sambalpur, India

    Harish Kumar Sahoo

Authors
  1. Basabadatta Mohanty
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Harish Kumar Sahoo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bijayananda Patnaik
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Harish Kumar Sahoo.

Ethics declarations

Conflict of interest

On behalf of all authors, the corresponding author states that there is 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

Mohanty, B., Sahoo, H.K. & Patnaik, B. Wireless channel estimation and beamforming by using block sparse adaptive filtering. SIViP 15, 769–777 (2021). https://doi.org/10.1007/s11760-020-01795-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-020-01795-0

Keywords

Search

Navigation