Skip to main content
SpringerLink
Log in
Book cover

International Conference on Knowledge-Based and Intelligent Information and Engineering Systems

KES 2003: Knowledge-Based Intelligent Information and Engineering Systems pp 311–317Cite as

Performance of Adaptive Beamforming by Using Complex-Valued Neural Network

  • Conference paper

  • 995 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2774))

Abstract

This paper presents a performance analysis of adaptive beamforming (ABF) by using complex-valued neural network (CVNN). We compare the performance of conventional complex-valued Least Mean Square (CLMS)-based ABF with that of multilayer CVNN’s, using the beamforming results of exact matrix method as a reference. Experiments for multiple beam-pointing and multiple null-steering shows that the CVNN-based ABF outperform the CLMS-based ABF in terms of convergence speed and interferences suppression level. Additionally, the solution of CVNN-based ABF is closer to the exact solution, than the CLMS is.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Widrow, B., Stearns, S.D.: Adaptive Signal Processing. Prentice Hall, Englewood Cliffs (1985)

    MATH  Google Scholar 

  2. Hudson, J.E.: Adaptive Array Principles. IEE EM Waves Series 11 (1991)

    Google Scholar 

  3. Johnson, D.H., Dudgeon, D.E.: Array Signal Processing-Concepts and Techniques. Prentice Hall, Englewood Cliffs (1993)

    MATH  Google Scholar 

  4. Krim, H., Viberg, M.: Two Decades of Array Signal Processing: The Parametric Approach. IEEE Signal Processing Magazine, 87–94 (July 1996)

    Google Scholar 

  5. Godara, L.C.: Application of Antenna Arrays to Mobile Communications, Part II: Beam-forming and Direction-of-Arrival Considerations. Proceedings of the IEEE, 1195–1245 (August 1997)

    Google Scholar 

  6. Van Veen, B.D., Buckley, K.M.: Beamforming: A Versatile Approach to Spatial Filtering. IEEE ASSP Magazine, 4–24 (April 1998)

    Google Scholar 

  7. Widrow, B., Mantey, P.E., Griffiths, L.J., Goode, B.B.: Adaptive Antenna Systems. Proc. of the IEEE 55, 2143–2159 (1967)

    Article  Google Scholar 

  8. Nikoonahad, M., Liu, D.C.: Medical Ultrasound Imaging Using Neural Networks. Electronics Letter 26(8), 545–546 (1990)

    Article  Google Scholar 

  9. Hirose, A.: Coherent Neural Networks and Their Applications to Control and Signal Processing. In: Tzafestas, S.G. (ed.) Soft Computing in Systems and Control Technology, pp. 397–422. World Scientific Pub. Co., Singapore (1999)

    Google Scholar 

  10. Suksmono, A.B., Hirose, A.: Interferometric SAR Image Restoration Using Monte-Carlo Metropolis Algorithm. IEEE Trans. on Signal Processing 50(2), 290–298 (2002)

    Article  Google Scholar 

  11. Leung, H.L., Haykin, S.: The Complex Backpropagation Algorithm. IEEE Trans. on Signal Processing 39(9), 2101–2104 (1991)

    Article  Google Scholar 

  12. Benvenuto, N., Piazza, F.: On the Complex Bacpropagation Algorithm. IEEE Trans. on Signal Processing 40(4), 967–969 (1992)

    Article  Google Scholar 

  13. Georgiou, G.M., Koutsougeras, C.: Complex Domain Backpropagation. IEEE Trans. on Circuits and Systems II 39(5), 330–334 (1992)

    Article  MATH  Google Scholar 

  14. Hirose, A.: Continuous Complex-valued Backpropagation Learning. Electronics Letters 28(20), 1854–1855 (1992)

    Article  Google Scholar 

  15. Widrow, B., McCool, J., Ball, M.: The Complex LMS algorithm. Proc. of the IEEE, 719–720 (April 1975)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dept. of Electrical Engineering, Institut Teknologi Bandung, Jl. Ganesha No. 10, Bandung, 40132, Indonesia

    Andriyan Bayu Suksmono

  2. Grad. School of Frontier Sciences, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan

    Akira Hirose

Authors
  1. Andriyan Bayu Suksmono
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Akira Hirose
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Computing Laboratory, Oxford University, Parks Road, OXI 3QD, Oxford, United Kingdom

    Vasile Palade

  2. Centre for SMART Systems, School of Environment and Technology, University of Brighton, BN2 4GJ, Brighton, UK

    Robert J. Howlett

  3. Knowledge-Based Intelligent Engineering Systems Centre, University of South Australia, Mawson Lakes, SA 5095, Adelaide, Australia

    Lakhmi Jain

Rights and permissions

Reprints and permissions

Copyright information

© 2003 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Suksmono, A.B., Hirose, A. (2003). Performance of Adaptive Beamforming by Using Complex-Valued Neural Network. In: Palade, V., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2003. Lecture Notes in Computer Science(), vol 2774. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45226-3_43

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-45226-3_43

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40804-8

  • Online ISBN: 978-3-540-45226-3

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation