Skip to main content
SpringerLink
Log in

A Low Complexity Correlation Algorithm for Compressive Channel Estimation in Massive MIMO System

  • Published:
International Journal of Wireless Information Networks Aims and scope Submit manuscript

A Correction to this article was published on 07 June 2018

This article has been updated

Abstract

Channel state information is essential for base station (BS) to fully exploit the merits of massive multiple input multiple output, which consumes large amount of pilot overhead attributed to tremendous number of BS antennas. Accordingly, huge computational complexity of correlation operation tends to be an obstacle for the implementation of compressive channel estimation algorithms, especially for greedy algorithms. In this paper, pilot overhead problem lightens by exploiting common support property due to the close space of BS antenna array. Furthermore, a low complexity correlation algorithm is proposed for each iteration of greedy algorithm, which exploits the inherent of pilot distribution and sensing matrix composed of pilot sequence. Complexity of proposed algorithm related to pilot distribution is also investigated. Performance analysis and simulation results prove that the proposed algorithm maintains the same performance, while achieves much less computational complexity than the original greedy 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.

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

Change history

  • 07 June 2018

    The original version of this article unfortunately contained a mistake in “Algorithm 1” under Section 3. The ellipsis in Algorithm 1 appears as alpha “L” in the published article

References

  1. E. Larsson, O. Edfors, F. Tufvesson, et al., Massive MIMO for next generation wireless systems, IEEE Communications Magazine, Vol. 52, No. 2, pp. 186–195, 2014.

    Article  Google Scholar 

  2. L. Lu, G. Y. Li, A. L. Swindlehurst, et al., An overview of massive MIMO: benefits and challenges, IEEE Journal of Selected Topics in Signal Processing, Vol. 8, No. 5, pp. 742–758, 2014.

    Article  Google Scholar 

  3. B. Amin, B. Mansoor, S. J. Nawaz, et al., Compressed sensing of sparse multipath MIMO channels with superimposed training sequence, Wireless Personal Communications, 2016. https://doi.org/10.1007/s11277-016-3778-7.

    Article  Google Scholar 

  4. W. U. Bajwa, J. Haupt, A. M. Sayeed, et al., Compressed channel sensing : a new approach to estimating sparse multipath channels, Proceedings of the IEEE, Vol. 98, No. 6, pp. 1058–1076, 2010.

    Article  Google Scholar 

  5. Y. Barbotin, A. Hormati, S. Rangan, et al., Estimation of Sparse MIMO channels with common support, Communications IEEE Transactions on, Vol. 60, No. 12, pp. 3705–3716, 2012.

    Article  Google Scholar 

  6. F. Yu, D. Li, Q. Guo, et al., Block-FFT based OMP for compressed channel estimation in underwater acoustic communications, Communications Letters IEEE, Vol. 19, No. 11, pp. 1937–1940, 2015.

    Article  Google Scholar 

  7. C. Qi, G. Yue, L. Wu, et al., Pilot design schemes for sparse channel estimation in OFDM systems, IEEE Transactions on Vehicular Technology, Vol. 64, No. 4, pp. 1493–1505, 2015.

    Article  Google Scholar 

  8. R. Mohammadian, A. Amini and B. H. Khalaj, Deterministic pilot design for sparse channel estimation in MISO/multi-user OFDM systems, IEEE Transactions on Wireless Communications, Vol. 16, No. 1, pp. 129–140, 2017.

    Article  Google Scholar 

  9. W. Hou and C. W. Lim, Structured compressive channel estimation for large-scale MISO-OFDM Systems, IEEE Communications Letters, Vol. 18, No. 5, pp. 765–768, 2014.

    Article  Google Scholar 

  10. Z. Gao, L. Dai, W. Dai, et al., Structured compressive sensing-based spatio-temporal joint channel estimation for FDD massive MIMO, IEEE Transactions on Communications, Vol. 64, No. 2, pp. 601–617, 2016.

    Article  Google Scholar 

  11. J. Choi, B. Shim and S. Chang, Downlink pilot reduction for massive MIMO systems via compressed sensing, IEEE Communications Letters, Vol. 19, No. 11, p. 1, 2015.

    Article  Google Scholar 

  12. Y. H. Nam, Y. Akimoto, Y. Kim, et al., Evolution of reference signals for LTE-advanced systems, IEEE Communications Magazine, Vol. 50, No. 50, pp. 132–138, 2012.

    Article  Google Scholar 

  13. W. Ding, F. Yang, S. Liu, et al., Non-orthogonal time-frequency training sequence based CSI acquisition for MIMO systems, IEEE Transactions on Vehicular Technology, Vol. 65, No. 7, pp. 5714–5719, 2015.

    Article  Google Scholar 

  14. Y. Eldar and G. Kutyniok, Compressed Sensing: Theory and Applications, Cambridge University PressCambridge, 2012.

    Book  Google Scholar 

Download references

Acknowledgements

The authors wish to thank reviewers. This work was supported by the Fundamental Research Funds for the Center Universities (Grant No. HIT.MKSTISP.2016 13).

Author information

Authors and Affiliations

  1. Communication Research Center, Harbin Institute of Technology, Harbin, 150080, China

    Ruoyu Zhang, Honglin Zhao, Chengzhao Shan & Shaobo Jia

Authors
  1. Ruoyu Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Honglin Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chengzhao Shan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shaobo Jia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Honglin Zhao.

Additional information

The original version of this article was revised: In Algorithm 1, the alpha "L" has been corrected as ellipsis.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, R., Zhao, H., Shan, C. et al. A Low Complexity Correlation Algorithm for Compressive Channel Estimation in Massive MIMO System. Int J Wireless Inf Networks 25, 371–381 (2018). https://doi.org/10.1007/s10776-018-0398-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10776-018-0398-z

Keywords

Search

Navigation