Skip to main content
SpringerLink
Log in

Deterministic compressed sensing based channel estimation for MIMO OFDM systems

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

In most of the existing compressed sensing (CS) based channel estimation schemes for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems, the randomly allocated pilot is difficult to be implemented in real applications and introduces additional pilot overhead for transmitting the information on pilot locations which is required in channel reconstruction at receiver. In this paper, a channel estimation scheme based on deterministic compressed sensing is proposed to cut down the pilot overhead in MIMO OFDM systems. To be specific, a deterministic pilot placement scheme is proposed to select the subset of the subcarriers for pilot transmission. Since this deterministic pilot placement leads a new kind of deterministic measurement matrices in CS model, the mutual coherence property of the deterministic matrix is verified to establish theoretical guarantee for the pilot placement scheme. Then an improved reconstruction algorithm is proposed to match the structure of the deterministic matrix. Numerical results demonstrate that even without the pilot locations information, the proposed channel estimation scheme based on deterministic compressed sensing achieves similar estimation accuracy as conventional estimator with random pilot placement.

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

Similar content being viewed by others

References

  1. Larsson, E.G., TUFVESSON, F., EDFORS, O.: MIMO OFDM for next generation wireless systems. IEEE Commun. Mag. 52(2), 186–195 (2014)

    Article  Google Scholar 

  2. Lu, L., Li, G.Y., SWINDLEHURST, A.L.: An overview of MIMO OFDM: benefits and challenges. IEEE J. Sel. Top. Signal Process. 8(5), 742–758 (2014)

    Article  Google Scholar 

  3. Dai, L., Wang, Z., Yanag, Z.: Spectrally efficient time-frequency training OFDM for mobile large-scale MIMO systems. IEEE J. Sel. Areas Commun. 31(2), 251–263 (2013)

    Article  Google Scholar 

  4. Candes, E., Romberg, J., Tao, T.: Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Inf. Theory 52(2), 489–509 (2006)

    Article  MathSciNet  Google Scholar 

  5. Donoho, D.L.: Compressed sensing. IEEE Trans. Inf. Theory 52(12), 1289–1306 (2006)

    Article  MathSciNet  Google Scholar 

  6. Noh, S., Zoltowski, M., Sung, Y.: Pilot beam pattern design for channel estimation in MIMO OFDM systems. IEEE J. Sel. Top. Signal Process. 8(5), 781–801 (2014)

    Article  Google Scholar 

  7. Choi, J., Love, D., Bidigare, P.: Downlink training techniques for FDD MIMO OFDM systems: open-loop and closed-loop training with memory. IEEE J. Sel. Top. Signal Process. 8(5), 802–814 (2014)

    Article  Google Scholar 

  8. Xiongbin, R., Vincent, K.N.: Compressive sensing with prior support quality information and application to MIMO OFDM channel estimation with temporal correlation. IEEE Trans. Signal Process. 63(18), 4914–4924 (2015)

    Article  MathSciNet  Google Scholar 

  9. Wenbo, D., Fang, Y., Wei, D., Jian, S.: Time-frequency joint sparse channel estimation for MIMO-OFDM systems. IEEE Commun. Lett. 19(1), 58–61 (2015)

    Article  Google Scholar 

  10. Candes, E.J., Tao, T.: Near-optimal signal recovery from random projections: universal encoding strategies? IEEE Trans. Inf. Theory 52(12), 5406–5425 (2006)

    Article  MathSciNet  Google Scholar 

  11. Pakrooh, P., Amini, A., Marvasti, F.: OFDM pilot allocation for sparse channel estimation. EURASIP J. Adv. Signal Process. 59, 1–9 (2012)

    Google Scholar 

  12. Mário, L., Renato, C., Paulo, M.: A multicarrier digital communication system for an underwater acoustic environment. Procedia Technol. 17, 625–631 (2014)

    Article  Google Scholar 

  13. Nisha, S., Paresh, R., Nashrah, F.: Sparse channel estimation using hybrid approach for OFDM transceiver. Int. J. Comput. Appl. 128(1), 7–11 (2015)

    Google Scholar 

  14. Candes, E., Romberg, J., Tao, T.: Stable signal recovery from incomplete and inaccurate measurements. Commun. Pure Appl. Math. 59(8), 1207–1223 (2006)

    Article  MathSciNet  Google Scholar 

  15. Soomin, L., Angelia, N.: Distributed random projection algorithm for convex optimization. IEEE J. Sel. Top. Signal Process. 7(2), 221–229 (2013)

    Article  Google Scholar 

  16. Dennis, S., Saikat, C., Mikael, S.: Distributed greedy pursuit algorithms. Signal Process. 105, 298–315 (2014)

    Article  Google Scholar 

  17. Qun, M., Yi, S.: A remark on the restricted isometry property in orthogonal matching pursuit. IEEE Trans. Inf. Theory 58(6), 3654–3656 (2012)

    Article  MathSciNet  Google Scholar 

  18. Andreas, M.T., Marc, E.P.: The computational complexity of the restricted isometry property, the nullspace property, and related concepts in compressed sensing. IEEE Trans. Inf. Theory 60(2), 1248–1259 (2014)

    Article  MathSciNet  Google Scholar 

  19. Ben-Haim, Z., Eldar, Y.C., Elad, M.: Coherence-based near oracle performance guarantees. IEEE Trans. Wirel. Commun. 6(5), 1743–1763 (2007)

    Article  Google Scholar 

  20. Gang, L., Zhihui, Z., Dehui, Y., Liping, C., Huang, B.: On projection matrix optimization for compressive sensing systems. IEEE Trans. Signal Process. 61(11), 2887–2898 (2013)

    Article  MathSciNet  Google Scholar 

  21. Dossal, C.: A necessary and sufficient condition for exact sparse recovery by \(\ell \)1 minimization. C. R. Math. 350(2), 117–120 (2012)

    Article  MathSciNet  Google Scholar 

  22. Davenport, M.A., Needell, D., Wakin, M.B.: Signal space CoSaMP for sparse recovery with redundant dictionaries. IEEE Trans. Inf. Theory 59(10), 6820–6829 (2013)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

This research is funded by the Program for New Century Excellent Talents in University (No. NCET -11 - 0873), the Program for Innovative Research Team in University of Chongqing (No. KJTD 201343), Program for Fundamental Research of Chongqing Communication Institute (No. TZ –CQTY–Y–C–2016-023) and the open subject of the Chongqing key laboratory of emergency communication (No. IRT1299).

Author information

Authors and Affiliations

  1. Communication Sergeant Institute, PLA Army Engineering University, Chongqing, 400035, China

    Kai Wang, Jingzhi Liu, Wei He & Shun Xu

  2. Information Communication Institute, National University of Defence Technology, Wuhan, 430010, China

    Kai Wang & Zhichun Gan

Authors
  1. Kai Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhichun Gan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jingzhi Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wei He
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shun Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kai Wang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, K., Gan, Z., Liu, J. et al. Deterministic compressed sensing based channel estimation for MIMO OFDM systems. Cluster Comput 22 (Suppl 2), 2971–2980 (2019). https://doi.org/10.1007/s10586-018-1712-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-018-1712-3

Keywords

Search

Navigation