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Cluster Computing

, Volume 22, Supplement 2, pp 2971–2980 | Cite as

Deterministic compressed sensing based channel estimation for MIMO OFDM systems

  • Kai WangEmail author
  • Zhichun Gan
  • Jingzhi Liu
  • Wei He
  • Shun Xu
Article
  • 149 Downloads

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.

Keywords

MIMO OFDM Channel estimation Compressed sensing Deterministic Mutual coherence 

Notes

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).

References

  1. 1.
    Larsson, E.G., TUFVESSON, F., EDFORS, O.: MIMO OFDM for next generation wireless systems. IEEE Commun. Mag. 52(2), 186–195 (2014)CrossRefGoogle Scholar
  2. 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)CrossRefGoogle Scholar
  3. 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)CrossRefGoogle Scholar
  4. 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)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Donoho, D.L.: Compressed sensing. IEEE Trans. Inf. Theory 52(12), 1289–1306 (2006)MathSciNetCrossRefGoogle Scholar
  6. 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)CrossRefGoogle Scholar
  7. 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)CrossRefGoogle Scholar
  8. 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)MathSciNetCrossRefGoogle Scholar
  9. 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)CrossRefGoogle Scholar
  10. 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)MathSciNetCrossRefGoogle Scholar
  11. 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. 12.
    Mário, L., Renato, C., Paulo, M.: A multicarrier digital communication system for an underwater acoustic environment. Procedia Technol. 17, 625–631 (2014)CrossRefGoogle Scholar
  13. 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. 14.
    Candes, E., Romberg, J., Tao, T.: Stable signal recovery from incomplete and inaccurate measurements. Commun. Pure Appl. Math. 59(8), 1207–1223 (2006)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Soomin, L., Angelia, N.: Distributed random projection algorithm for convex optimization. IEEE J. Sel. Top. Signal Process. 7(2), 221–229 (2013)CrossRefGoogle Scholar
  16. 16.
    Dennis, S., Saikat, C., Mikael, S.: Distributed greedy pursuit algorithms. Signal Process. 105, 298–315 (2014)CrossRefGoogle Scholar
  17. 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)MathSciNetCrossRefGoogle Scholar
  18. 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)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Ben-Haim, Z., Eldar, Y.C., Elad, M.: Coherence-based near oracle performance guarantees. IEEE Trans. Wirel. Commun. 6(5), 1743–1763 (2007)CrossRefGoogle Scholar
  20. 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)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Dossal, C.: A necessary and sufficient condition for exact sparse recovery by \(\ell \)1 minimization. C. R. Math. 350(2), 117–120 (2012)MathSciNetCrossRefGoogle Scholar
  22. 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)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Kai Wang
    • 1
    • 2
    Email author
  • Zhichun Gan
    • 2
  • Jingzhi Liu
    • 1
  • Wei He
    • 1
  • Shun Xu
    • 1
  1. 1.Communication Sergeant InstitutePLA Army Engineering UniversityChongqingChina
  2. 2.Information Communication InstituteNational University of Defence TechnologyWuhanChina

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