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Adaptive Beamforming for Linear Antenna Arrays Using Gravitational Search Algorithm

  • Abhinav Sharma
  • Sanjay Mathur
  • R. Gowri
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 624)

Abstract

Smart antenna is one of the leading innovations in the area of mobile communication which has drawn the attention of researchers as it fulfills the requirement of wireless services such as higher data rates and channel capacities. Adaptive beamforming (ABF) is one of the primary signal processing aspects of smart antenna. The problem of ABF is formulated as an optimization problem for linear antenna arrays. A novel gravitational search algorithm (GSA) is explored for optimizing the function which will effectively fit to the condition such as to direct the main lobe toward the desired direction of signal of concern (DS) and zero output (null) in the undesired direction of signals (UDS). The optimization algorithm shows good steering ability, and the simulation result verifies that the algorithm presents radiation pattern with reduced side lobe level (SLL) as compared to well-known minimum variance distortionless response (MVDR) technique. The simulations are carried out at different power levels of the incoming signals for analyzing overall performance of algorithms.

Keywords

Smart antenna ABF GSA SLL MVDR 

References

  1. 1.
    Gross, F. B.: Smart Antennas for Wireless Communications with MATLAB, Mc Graw-Hill, (2005).Google Scholar
  2. 2.
    Godara, L. C.: Application of antenna arrays to mobile communications. II. Beam-forming and direction-of-arrival considerations, IEEE Proceedings of the, vol. 85, no. 8, pp: 1195–1245, (1997).Google Scholar
  3. 3.
    Sharma, A., Mathur, S.: Performance Analysis of Adaptive Array Signal Processing Algorithms, IETE, Taylor & Francis, vol. 33, no. 5, pp: 472–491, (2016).Google Scholar
  4. 4.
    Zaharis, Z. D., Yiouitsis, T. V.: A novel adaptive beamforming technique applied on linear antenna arrays using adaptive mutated boolean PSO, Progress In Electromagnetic Research, vol. 117, pp: 165–179, (2011).Google Scholar
  5. 5.
    Zaharis, Z. D., Gotsis, K. A., Sahalos, J. N.: Adaptive beamforming with low side lobe level using neural networks trained by mutated boolean PSO, Progress In Electromagnetic Research, vol. 127, pp: 139–154, (2012).Google Scholar
  6. 6.
    Zaharis, Z. D., Skeberis, C., Xenos, T. D.: Improved antenna array adaptive beamforming with low side lobe level using a novel adaptive invasive weed optimization method, Progress In Electromagnetic Research, vol. 124, pp: 137–150, (2012).Google Scholar
  7. 7.
    Rashedi, E., Nezamabadi, S., Saryazdi, S.: A Gravitational Search Algorithm, Information Sciences, vol. 179, no. 13, pp. 2232–2248, (2009).Google Scholar
  8. 8.
    Sharma, A., Mathur, S.: Deterministic maximum likelihood direction of arrival estimation using GSA. International Conference on Electrical, Electronics, and Optimization Techniques, pp: 415–419, (2016).Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of EICUPESDehradunIndia
  2. 2.Department of ECECollege of Technology, GBPUA&TPantnagarIndia

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