Skip to main content
SpringerLink
Log in

Recursive least squares semi-blind beamforming for MIMO using decision directed adaptation and constant modulus criterion

  • Research Article
  • Published:
International Journal of Automation and Computing Aims and scope Submit manuscript

Abstract

A new semi-blind adaptive beamforming scheme is proposed for multi-input multi-output (MIMO) induced and space-division multiple-access based wireless systems that employ high order phase shift keying signaling. A minimum number of training symbols, very close to the number of receiver antenna elements, are used to provide a rough initial least squares estimate of the beamformer’s weight vector. A novel cost function combining the constant modulus criterion with decision-directed adaptation is adopted to adapt the beamformer weight vector. This cost function can be approximated as a quadratic form with a closed-form solution, based on which we then derive the recursive least squares (RLS) semi-blind adaptive beamforming algorithm. This semi-blind adaptive beamforming scheme is capable of converging fast to the minimum mean-square-error beamforming solution, as demonstrated in our simulation study. Our proposed semi-blind RLS beamforming algorithm therefore provides an efficient detection scheme for the future generation of MIMO aided mobile communication systems.

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

Similar content being viewed by others

References

  1. Cisco Visual networking index: Global mobile data traffic forecast update, 2016-2021 White Paper. [Online], Available: http: www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-indexvni/mobile-white-paper-c11-520862.html, February 09, 2017.

  2. J. H. Winters, J. Salz, R. D. Gitlin. The impact of antenna diversity on the capacity of wireless communication systems. IEEE Transactions on Communications, vol. 42, no. 2–4, pp. 1740–1751, 1994.

    Article  Google Scholar 

  3. J. Litva, T. K. Y. Lo, Digital Beamforming in Wireless Communications, London, UK: Artech House, 1996.

    Google Scholar 

  4. R. Kohno. Spatial and temporal communication theory using adaptive antenna array. IEEE Personal Communications, vol. 5, no.1, pp. 28–35, 1998.

    Article  Google Scholar 

  5. J. H. Winters. Smart antennas for wireless systems. IEEE Personal Communications, vol. 5, no.1, pp. 23–27, 1998.

    Article  Google Scholar 

  6. J. S. Blogh, L. Hanzo, Third Generation Systems and Intelligent Wireless Networking: Smart Antennas and Adaptive Modulation, Chichester, UK: John Wiley, 2002.

    Book  Google Scholar 

  7. P. Vandenameele, L. Van Der Perre, M. Engels. Space Division Multiple Access for Wireless Local Area Networks, Boston, USA: Kluwer Academic Publishers, 2001.

    Google Scholar 

  8. A. Paulraj, R. Nabar, D. Gore, Introduction to Space-Time Wireless Communications, Cambridge, UK: Cambridge University Press, 2003.

    Google Scholar 

  9. A. J. Paulraj, D. A. Gore, R. U. Nabar, H. Bölcskei. An overview of MIMO communications-a key to gigabit wireless. Proceedings of the IEEE, vol. 92, no.2, pp. 198–218, 2004.

    Article  Google Scholar 

  10. S. Sugiura, S. Chen, L. Hanzo. A unified MIMO architecture subsuming space shift keying, OSTBC, BLAST and LDC. In Proceedings of the 72nd Vehicular Technology Conference Fall, IEEE, Ottawa, Canada, pp. 1–5, 2010.

    Google Scholar 

  11. S. Sugiura, S. Chen, L. Hanzo. MIMO-aided near-capacity turbo transceivers: Taxonomy and performance versus complexity. IEEE Communications Surveys & Tutorials, vol. 14, no.2, pp. 421–442, 2012.

    Article  Google Scholar 

  12. 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 Signal Processing Magazine, vol. 30, no.1, pp. 40–60, 2013.

    Article  Google Scholar 

  13. E. G. Larsson, O. Edfors, F. Tufvesson, T. L. Marzetta. Massive MIMO for next generation wireless systems. IEEE Communications Magazine, vol. 52, no.2, pp. 186–195, 2014.

    Article  Google Scholar 

  14. T. L. Marzetta. Massive MIMO: An introduction. Bell Labs Technical Journal, vol. 20, pp. 11–22, 2015.

    Article  Google Scholar 

  15. S. Chen, N. N. Ahmad, L. Hanzo. Adaptive minimum biterror rate beamforming. IEEE Transactions on Wireless Communications, vol. 4, no.2, pp. 341–348, 2005.

    Article  Google Scholar 

  16. S. Chen, L. Hanzo, N. N. Ahmad, A. Wolfgang. Adaptive minimum bit error rate beamforming assisted receiver for QPSK wireless communication. Digital Signal Processing, vol. 15, no.6, pp. 545–567, 2005.

    Article  Google Scholar 

  17. S. Chen, L. Hanzo, A. Livingstone. MBER space-time decision feedback equalization assisted multiuser detection for multiple antenna aided SDMA systems. IEEE Transactions on Signal Processing, vol. 54, no.8, pp. 3090–3098, 2006

    Article  Google Scholar 

  18. S. Chen. Adaptive beamforming assisted receiver. Handbook on Advancements in Smart Antenna Technologies for Wireless Networks, C. Sun, J. Cheng, T. Ohira, Eds., London, UK: Information Science Reference, pp. 68–92, 2008.

    Google Scholar 

  19. X. Hong, S. Chen. A minimum approximate-BER beamforming approach for PSK modulated wireless systems. International Journal of Automation and Computing, vol.5, no.3, pp. 284–289, 2008.

    Article  Google Scholar 

  20. B. R. Vojčić, W. M. Jang. Transmitter precoding in synchronous multiuser communications. IEEE Transactions on Communications, vol. 46, no.10, pp. 1346–1355, 1998.

    Article  Google Scholar 

  21. W. Yao, S. Chen, S. Tan, L. Hanzo. Minimum bit error rate multiuser transmission designs using particle swarm optimisation. IEEE Transactions on Wireless Communications, vol. 8, no.10, pp. 5012–5017, 2009.

    Article  Google Scholar 

  22. W. Yao, S. Chen, L. Hanzo. Generalized MBER-based vector precoding design for multiuser transmission. IEEE Transactions on Vehicular Technology, vol. 60, no.2, pp. 739–745, 2011.

    Article  Google Scholar 

  23. W. Yao, S. Chen, L. Hanzo. Particle swarm optimisation aided MIMO multiuser transmission designs. Journal of Computational and Theoretical Nanoscience, vol. 9, no.2, pp. 266–275, 2012.

    Article  Google Scholar 

  24. X. D. Zhu, Z. C. Wang, C. Qian, L. L. Dai, J. H. Chen, S. Chen, L. Hanzo. Soft pilot reuse and multicell block diagonalization precoding for massiveMIMO systems. IEEE Transactions on Vehicular Technology, vol. 65, no.5, pp. 3285–3298, 2016.

    Article  Google Scholar 

  25. M. Biguesh, A. B. Gershman. Training-based MIMO channel estimation: A study of estimator tradeoffs and optimal training signals. IEEE Transactions on Signal Processing, vol. 54, no.3, pp. 884–893, 2006.

    Article  Google Scholar 

  26. B. Widrow, P. E. Mantey, L. J. Griffiths, B. B. Goode. Adaptive antenna systems. Proceedings of the IEEE, vol. 55, no.12, pp. 2143–2159, 1967.

    Article  Google Scholar 

  27. L. J. Griffiths. A simple adaptive algorithm for real-time processing in antenna arrays. Proceedings of the IEEE, vol. 57, no.10, pp. 1696–1704, 1969.

    Article  Google Scholar 

  28. S. Haykin, Adaptive Filter Theory, 3rd ed., Upper Saddle River, USA: Prentice Hall, 1996.

    MATH  Google Scholar 

  29. J. J. Shynk, R. P. Gooch. The constant modulus array for cochannel signal copy and direction finding. IEEE Transactions on Signal Processing, vol. 44, no.3, pp. 652–660, 1996.

    Article  Google Scholar 

  30. J. Sheinvald. On blind beamforming for multiple non- Gaussian signals and the constant-modulus algorithm. IEEE Transactions on Signal Processing, vol. 46, no.7, pp. 1878–1885, 1998.

    Article  Google Scholar 

  31. K. H. Yang, T. Ohira, Y. M. Zhang, C. Y. Chi. Superexponential blind adaptive beamforming. IEEE Transactions on Signal Processing, vol. 52, no.6, pp. 1549–1563, 2004.

    Article  Google Scholar 

  32. E. de Carvalho, D. T. M. Slock. Blind and semi-blind FIR multichannel estimation: (Global) identifiability conditions. IEEE Transactions on Signal Processing, vol. 52, no.4, pp. 1053–1064, 2004.

    Article  MathSciNet  Google Scholar 

  33. C. Shin, R. W. Heath, E. J. Powers. Blind channel estimation for MIMO-OFDM systems. IEEE Transactions on Vehicular Technology, vol. 56, no.2, pp. 670–685, 2007.

    Article  Google Scholar 

  34. L. Tong, R. W. Liu, V. C. Soon, Y. F. Huang. Indeterminacy and identifiability of blind identification. IEEE Transactions on Circuits and Systems, vol. 38, no.5, pp. 499–509, 1991.

    Article  MATH  Google Scholar 

  35. C. Cozzo, B. L. Hughes. Joint channel estimation and data detection in space-time communications. IEEE Transactions on Communications, vol. 51, no.8, pp. 1266–1270, 2003.

    Article  Google Scholar 

  36. S. Buzzi, M. Lops, S. Sardellitti. Performance of iterative data detection and channel estimation for singleantenna and multiple-antennas wireless communications. IEEE Transactions on Vehicular Technology, vol. 53, no.4, pp. 1085–1104, 2004.

    Article  Google Scholar 

  37. S. Chen, S. Sugiura, L. Hanzo. Semi-blind joint channel estimation and data detection for space-time shift keying systems. IEEE Signal Processing Letters, vol. 17, no.12, pp. 993–996, 2010.

    Article  Google Scholar 

  38. P. C. Zhang, S. Chen, L. Hanzo. Reduced-complexity nearcapacity joint channel estimation and three-stage turbo detection for coherent space-time shift keying. IEEE Transactions on Communications, vol. 61, no.5, pp. 1902–1913, 2013.

    Article  Google Scholar 

  39. P. C. Zhang, S. Chen, L. Hanzo. Embedded iterative semi-blind channel estimation for three-stage-concatenated MIMO-aided QAM turbo transceivers. IEEE Transactions on Vehicular Technology, vol. 63, no.1, pp. 439–446, 2014.

    Article  Google Scholar 

  40. S. Chen, W. Yao, L. Hanzo. Semi-blind adaptive beamforming for high-throughput quadrature amplitude modulation systems. International Journal of Automation and Computing, vol. 7, no.4, pp. 565–570, 2010.

    Article  Google Scholar 

  41. Y. X. Chen, L. Le-Ngoc, B. Champagne, C. J. Xu. Recursive least squares constant modulus algorithm for blind adaptive array. IEEE Transactions on Signal Processing, vol. 52, no.5, pp. 1452–1456, 2004.

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, School of Mathematical, Physical and Computational Sciences, University of Reading, Reading, RG6 6AY, UK

    Xia Hong

  2. School of Electronics and Computer Science, University of Southampton, Southampton, SO17 1BJ, UK

    Sheng Chen

  3. King Abdulaziz University, Jeddah, 21589, Saudi Arabia

    Sheng Chen

Authors
  1. Xia Hong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sheng Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xia Hong.

Additional information

Recommended by Associate Editor Qing-Long Han

Xia Hong received the B. Sc. and M. Sc. degrees from the National University of Defense Technology, China, in 1984 and 1987, respectively, and received the Ph.D. degree from The University of Sheffield, UK in 1998, all in automatic control. She was a research assistant with the Beijing Institute of Systems Engineering, China, from 1987 to 1993. She was a research fellow with the Department of Electronics and Computer Science, University of Southampton, UK from 1997 to 2001. She is currently a professor with the Department of Computer Science, School of Mathematical, Physical and Computational Sciences, University of Reading, UK. She has authored over 200 research papers, and co-authored a research book. She received the Donald Julius Groen Prize from IMechE in 1999.

Her research interests include nonlinear systems identification, data modelling, estimation and intelligent control, neural networks, pattern recognition, learning theory and their applications.

Sheng Chen received the B.Eng. degree in control engineering from the East China Petroleum Institute, China in 1982, received the Ph.D. degree in control engineering from City University, UK in 1986, and received the D. Sc. degree from the University of Southampton, UK in 2005. From 1986 to 1999, he held research and academic appointments at The University of Sheffield, UK, the University of Edinburgh, UK, and the University of Portsmouth, UK. Since 1999, he has been with the Electronics and Computer Science, University of Southampton, UK, where he is currently a professor in intelligent systems and signal processing. He has authored over 550 research papers. He is a Fellow of IET, a Distinguished Adjunct Professor with King Abdulaziz University, Saudi Arabia, and an ISI highly cited researcher in engineering in 2004. He was elected to a Fellow of the United Kingdom Royal Academy of Engineering in 2014.

His research interests include adaptive signal processing, wireless communications, modeling and identification of nonlinear systems, neural network and machine learning, intelligent control system design, evolutionary computation methods, and optimisation.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hong, X., Chen, S. Recursive least squares semi-blind beamforming for MIMO using decision directed adaptation and constant modulus criterion. Int. J. Autom. Comput. 14, 442–449 (2017). https://doi.org/10.1007/s11633-017-1087-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11633-017-1087-6

Keywords

Search

Navigation