Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Low-Complexity and High-Accuracy Semi-blind Joint Channel and Symbol Estimation for Massive MIMO-OFDM

  • Published:
Circuits, Systems, and Signal Processing Aims and scope Submit manuscript

Abstract

In order to fully exploit the scarce spectrum, antenna arrays are incorporated into wireless communication devices in 4G and 5G communication networks to deploy MIMO-OFDM systems. Recently, the least squares Khatri–Rao factorization has been applied to MIMO-OFDM systems for semi-blind joint channel and symbol estimation. Its cubic computational complexity is prohibitive when the number of transmit and receive antennas is very large. Therefore, the average vector and Hadamard ratio rank one approximation has been proposed for MIMO-OFDM systems, showing a linear complexity, but being limited to channels and transmitted symbols with offsets. In this paper, we present four novel MIMO-OFDM algorithms for massive antenna array systems that outperform the state-of-the-art approaches in terms of complexity and/or accuracy. The four proposed schemes are the alternating least squares with vector selection initialization, the vector projection rank one approximation including vector selection rank one initialization, the factorization based on eigenvalue decomposition with eigenvector projection and the factorization based on sectional truncated singular value decomposition and vector projection. Our analytical complexity analysis and numerical results corroborate the trade-offs offered by the different receiver algorithms in terms of complexity, parallelism and performance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

Notes

  1. A simple and suboptimal choice of \(\phi \) is given by \(\phi _m=2\pi (m-1)/M\). However, it can be optimized. See [24].

References

  1. S. Alamouti, A simple transmit diversity technique for wireless communications. IEEE J. Sel. Areas Commun. 16, 1451–1458 (1998)

    Article  Google Scholar 

  2. J.D. Carroll, J. Chang, Analysis of individual differences in multidimensional scaling via an \(n\)-way generalization of “Eckart–Young” decomposition. Psychometrika 35(3), 283–319 (1970)

    Article  MATH  Google Scholar 

  3. A.L.F. de Almeida, G. Favier, J.C.M. Mota, PARAFAC-based unified tensor modeling for wireless communication systems with application to blind multiuser equalization. Sig. Process. 87, 337–351 (2007)

    Article  MATH  Google Scholar 

  4. A.L.F. de Almeida, G. Favier, J.C.M. Mota, Space–time spreading-multiplexing for MIMO wireless communication systems using the PARATUCK2 tensor model. Sig. Process. 89(11), 2103–2116 (2009)

    Article  MATH  Google Scholar 

  5. A.L.F. de Almeida, G. Favier, J.C.M. Mota, C.C. Cavalcante, Tensor-based space–time multiplexing codes for MIMO–OFDM systems with blind detection, in Proc. IEEE Symp. Pers. Ind. Mob. Radio Commun. (PIMRC) (Helsinki, Sept. 2006)

  6. J.P.C.L. da Costa, A.L.F. de Almeida, W.C. Freitas, D.V. de Lima, Low complexity closed form solution to semi-blind joint channel and symbol estimation in MIMO–OFDM, in Proc. 19th International Conference on OFDM and Frequency Domain Techniques(ICOF 2016) (Essen, 2016)

  7. J.P.C.L. da Costa, F. Roemer, M. Weis, M. Haardt, Robust R–D parameter estimation via closed-form PARAFAC, in Proc. ITG Workshop on Smart Antennas (WSA’10) (Bremen, 2010)

  8. C. Desset, L.V. der Perre, Validation of low-accuracy quantization in massive MIMO and constellation EVM analysis, in Proc. 2015 European Conference on Networks and Communications (EuCNC) (2015)

  9. A.B. Ericsson, Ericsson mobility report. Ericsson AB, Revision A, Stockholm, Sweden (Nov. 2016)

  10. W.C. Freitas, A.L.F. de Almeida, J.P.C.L. da Costa, Blind Joint Channel Estimation and Data Detection for Precoded Multi-layered Space–Frequency MIMO Schemes. Circuits, Systems and Signal Processing (Elsevier, Amsterdam, 2013)

    Google Scholar 

  11. G.H. Golub, C.F.V. Loan, Matrix Computations, Fourth edn. (The Johns Hopkins University Press, Baltimore, 2013)

    MATH  Google Scholar 

  12. N. Halko, P. Martinsson, J.A. Tropp, Finding structure with randomness: stochastic algorithms for constructing approximate matrix decomposition. SIAM Rev. 53, 217–288 (2009)

    Article  MATH  Google Scholar 

  13. R.A. Harshman, Foundations of the PARAFAC procedure: model and conditions for an “explanatory” multi-mode factor analysis. UCLA Working papers in phonetics, vol. 16(1) (1970)

  14. H. Jedda, J.A. Nossek, A. Mezghani, Minimum BER precoding in 1-bit massive MIMO systems, in Proc. 2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM) (Rio de Janeiro, Sept 2016)

  15. T.G. Kolda, B.W. Bader, Tensor decompositions and applications. Soc. Ind. Appl. Math. (SIAM) 51(3), 455–500 (2009)

    MathSciNet  MATH  Google Scholar 

  16. K. Liu, J.P.C.L. da Costa, A.L.F. de Almeida, H.C. So, A closed form solution to semi-blind joint symbol and channel estimation in MIMO–OFDM systems, in Proc. 2012 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC’12) (Hong Kong, Aug. 2012)

  17. K. Liu, J.P.C.L. da Costa, H.C. So, A.L.F. de Almeida, Semi-blind receivers for joint symbol and channel estimation in space-time-frequency diversity based MIMO-OFDM systems. IEEE Trans. Sig. Process. 61(21), 5444–5457 (2013)

    Article  Google Scholar 

  18. F. Roemer, M. Haardt, Tensor-based channel estimation (TENCE) for two-way relaying with multiple antennas and spatial reuse, in Proc. IEEE Int. Conf. Acoustics, Speech and Sig. Proc. (ICASSP 2009) (Taipei, Apr. 2009)

  19. F. Rusek, D. Persson, B.K. Lau, E.G. Larsson, T.L. Marzetta, O. Edfors, F. Tufvesson, Scaling up MIMO: opportunities and challenges with very large arrays. IEEE Sig. Process. Mag. 30(1), 40–60 (2013)

    Article  Google Scholar 

  20. A.M. Sayeed, Deconstructing multiantenna fading channels. IEEE Trans. Sig. Process. 50(10), 2563–2579 (2002)

    Article  Google Scholar 

  21. A.M. Sayeed, V. Raghavan, Maximizing MIMO capacity in sparse multipath with reconfigurable antenna arrays. IEEE J. Sel. Top. Sig. Process. 1(1), 156–166 (2007)

    Article  Google Scholar 

  22. A. Smilde, R. Bro, P. Geladi, Multi-way Analysis: Applications in the Chemical Science (Wiley, London, 2004)

    Book  Google Scholar 

  23. G.W. Stewart, Matrix Algorithms (Society for Industrial and Applied Mathematics (SIAM), Philadelphia, 1998)

    Book  MATH  Google Scholar 

  24. Y. Xin, Z. Wang, G.B. Giannakis, Space–time diversity systems based on linear constellation precoding. IEEE Trans. Wirel. Commun. 2(2), 294–309 (2003)

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the Research Support Foundation of the Brazilian Federal District (FAPDF) for their funding under the calls 03/2015 and 01/2017, the Brazilian Coordination for the Improvement of Higher Education Personnel (CAPES) for the PNPD, PVE and PDE scholarships, and CAPES/PROBAL Grant 88887.144009/2017-00. João P. C. L. da Costa and André L. F. de Almeida are partially supported by the Brazilian National Council for Scientific and Technological Development (CNPq).

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, University of Brasília, Campus Darcy Ribeiro, Asa Norte, Brasília, 70910-900, Brazil

    Ricardo Kehrle Miranda, João Paulo C. L. da Costa & Rafael T. de Sousa Jr.

  2. Institute for Information Technology, Ilmenau University of Technology, Helmholtzpl. 2, Helmholtzbau, 98684, Ilmenau, Germany

    Binghua Guo & Giovanni Del Galdo

  3. Fraunhofer Institute for Integrated Circuits IIS, Erlangen, Germany

    Giovanni Del Galdo

  4. Department of Teleinformatics Engineering, Federal University of Ceará, Av. Mister Hull, P.O. Box 6005, Fortaleza, CE, 60455-760, Brazil

    André L. F. de Almeida

Authors
  1. Ricardo Kehrle Miranda
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. João Paulo C. L. da Costa
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Binghua Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. André L. F. de Almeida
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Giovanni Del Galdo
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Rafael T. de Sousa Jr.
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ricardo Kehrle Miranda.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kehrle Miranda, R., C. L. da Costa, J.P., Guo, B. et al. Low-Complexity and High-Accuracy Semi-blind Joint Channel and Symbol Estimation for Massive MIMO-OFDM. Circuits Syst Signal Process 38, 1114–1136 (2019). https://doi.org/10.1007/s00034-018-0898-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00034-018-0898-1

Keywords

Search

Navigation