Adaptive Beamforming Based on Artificial Neural Networks

Rajaa Amellal

doi:doi:10.11648/j.ajcst.20240701.13

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Adaptive Beamforming Based on Artificial Neural Networks

Rajaa Amellal^*

Published in American Journal of Computer Science and Technology (Volume 7, Issue 1)

Received: 15 August 2023 Accepted: 15 September 2023 Published: 20 February 2024

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Abstract

Adaptive beamforming is technique of signal processing play important role to increasing capacity of the wireless communication and radar systems by configured the steerable of radiation pattern and maximize gain and directivity in a direction of arrival (DoA) of desired users in order to minimizing side lobe and reducing signal to interference. We review recently the classic technique of adaptive algorithms; we specified tow method for this preprocessing beam former LMS and RLS. The least Mean Square (LMS) operate the weight vectors of antenna array elements for beamforming by iterative process as well need to be continuously adapted to the ever-changing environment. Moreover recursive least square (RLS) give advantage for fast convergence beamforming. In this paper we proved the performance of this algorithms by updating the weights in addition process based on estimated vectors using neural network, The first phase for smart beam former are used by direction of arrival (DoA) estimated using radial basis neural network (RBFNN). In next step the targets is generated from the optimum weight calculated using Minimum Variance Distortion less method (MVDLM). Finally, the simulation result for the new process is synthetized and shows using Matlab application.

Published in	American Journal of Computer Science and Technology (Volume 7, Issue 1)
DOI	10.11648/j.ajcst.20240701.13
Page(s)	13-23
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Adaptive Algorithms, Smart Beam Former, Artificial Neural Network (NN), Optimum Weights, Direction of Arrival (DoA)

References

[1]	Rawat, Abhishek Yadav, R. N. Shrivastava, S. C.(2012), “Neural network applications in smart antenna arrays: A review”, AEU - International Journal of Electronics and Communications Volume 66, Issue 11, Pages 903-912.
[2]	H., AdheedAhmed, Sulaiman. (2015), International Journal of Computer Applications “Elman Recurrent Neural Network Application in Adaptive Beamforming of Smart Antenna System”, Volume 129, Issue 11, Pages 38-43.
[3]	Mallioras, Ioannis Zaharis, Zaharias D. Lazaridis, Pavlos I. Pantelopoulos, Stelios. (2022), IEEE Transactions on Antennas and Propagation “A Novel Realistic Approach of Adaptive Beamforming Based on Deep Neural Networks”, Volume 70, Issue 10. Pages 8833-8848.
[4]	Liu, Zhang Meng, Zhang, Chenwei, Yu, Philip S. (2018), “Direction-of-Arrival Estimation Based on Deep Neural Networks with Robustness to Array Imperfections” IEEE Transactions on Antennas and Propagation, Volume 66, Issue 12, Pages 7315-7327.
[5]	Potocnik. (2012), “Neural Networks: MATLAB examples” Mathworks, Pages 1-91.
[6]	Toscano et al. (1999), "G ( ) = e + (1 0) e 0", Society Volume 46, Issue 12, Pages 1997-1999.
[7]	Saffah & Hreshee. (2016), “LMS Algorithm for Optimizing the Phased LMS Algorithm for Optimizing the Phased Array Antenna Radiation Pattern”, Volume 22, Issue 2013, Pages 1-6.
[8]	C. Liu & Helgert. (2020), “An Improved Adaptive Beamforming-based Machine Learning Method for Positioning in Massive MIMO Systems”, International Journal on Advances in Internet Technology Volume 13, Issue 1, Pages 21-34.
[9]	Albanee. (2016), “Smart Antenna Adaptive Beam Forming Base on Neural Network With Different Training”, Volume 20, Issue 3, Pages 60-74.
[10]	Zaharis et al. (2012), “Comparative study of neural network training applied to adaptive beamforming of antenna arrays”, EEE Transactions on Antennas and Propagation Volume 126, Issue 2014, Pages 269-283.
[11]	Mok atren et al Mokatren, Lubna Shibly, Cetin, Ahmet Enis, Ansari, Rashid. (2019), “Deep Layered LMS Predictor”, Pages 1-5.
[12]	Sarevska & Salem. (2006), “Antenna Array Beamforming using Neural Network”, Engineering and Technology Volume 1, Issue 7, Pages 115-119.
[13]	Dakulagi & Alagirisamy.(2020), “Adaptive Beamformers for High-Speed Mobile Communication”, Wireless Personal Communications Volume 113, Issue 4, Pages 1691-1707.

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APA Style

Amellal, R. (2024). Adaptive Beamforming Based on Artificial Neural Networks. American Journal of Computer Science and Technology, 7(1), 13-23. https://doi.org/10.11648/j.ajcst.20240701.13

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ACS Style

Amellal, R. Adaptive Beamforming Based on Artificial Neural Networks. Am. J. Comput. Sci. Technol. 2024, 7(1), 13-23. doi: 10.11648/j.ajcst.20240701.13

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AMA Style

Amellal R. Adaptive Beamforming Based on Artificial Neural Networks. Am J Comput Sci Technol. 2024;7(1):13-23. doi: 10.11648/j.ajcst.20240701.13

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@article{10.11648/j.ajcst.20240701.13,
  author = {Rajaa Amellal},
  title = {Adaptive Beamforming Based on Artificial Neural Networks},
  journal = {American Journal of Computer Science and Technology},
  volume = {7},
  number = {1},
  pages = {13-23},
  doi = {10.11648/j.ajcst.20240701.13},
  url = {https://doi.org/10.11648/j.ajcst.20240701.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajcst.20240701.13},
  abstract = {Adaptive beamforming is technique of signal processing play important role to increasing capacity of the wireless communication and radar systems by configured the steerable of radiation pattern and maximize gain and directivity in a direction of arrival (DoA) of desired users in order to minimizing side lobe and reducing signal to interference. We review recently the classic technique of adaptive algorithms; we specified tow method for this preprocessing beam former LMS and RLS. The least Mean Square (LMS) operate the weight vectors of antenna array elements for beamforming by iterative process as well need to be continuously adapted to the ever-changing environment. Moreover recursive least square (RLS) give advantage for fast convergence beamforming. In this paper we proved the performance of this algorithms by updating the weights in addition process based on estimated vectors using neural network, The first phase for smart beam former are used by direction of arrival (DoA) estimated using radial basis neural network (RBFNN). In next step the targets is generated from the optimum weight calculated using Minimum Variance Distortion less method (MVDLM). Finally, the simulation result for the new process is synthetized and shows using Matlab application.
},
 year = {2024}
}

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TY  - JOUR
T1  - Adaptive Beamforming Based on Artificial Neural Networks
AU  - Rajaa Amellal
Y1  - 2024/02/20
PY  - 2024
N1  - https://doi.org/10.11648/j.ajcst.20240701.13
DO  - 10.11648/j.ajcst.20240701.13
T2  - American Journal of Computer Science and Technology
JF  - American Journal of Computer Science and Technology
JO  - American Journal of Computer Science and Technology
SP  - 13
EP  - 23
PB  - Science Publishing Group
SN  - 2640-012X
UR  - https://doi.org/10.11648/j.ajcst.20240701.13
AB  - Adaptive beamforming is technique of signal processing play important role to increasing capacity of the wireless communication and radar systems by configured the steerable of radiation pattern and maximize gain and directivity in a direction of arrival (DoA) of desired users in order to minimizing side lobe and reducing signal to interference. We review recently the classic technique of adaptive algorithms; we specified tow method for this preprocessing beam former LMS and RLS. The least Mean Square (LMS) operate the weight vectors of antenna array elements for beamforming by iterative process as well need to be continuously adapted to the ever-changing environment. Moreover recursive least square (RLS) give advantage for fast convergence beamforming. In this paper we proved the performance of this algorithms by updating the weights in addition process based on estimated vectors using neural network, The first phase for smart beam former are used by direction of arrival (DoA) estimated using radial basis neural network (RBFNN). In next step the targets is generated from the optimum weight calculated using Minimum Variance Distortion less method (MVDLM). Finally, the simulation result for the new process is synthetized and shows using Matlab application.

VL  - 7
IS  - 1
ER  -

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Author Information

Rajaa Amellal

Physic Department, Laboratory Electronic Systems, Information Processing, Mechanic and Energetic, Ibn Tofail University, Kenitra, Morocco

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APA Style

Amellal, R. (2024). Adaptive Beamforming Based on Artificial Neural Networks. American Journal of Computer Science and Technology, 7(1), 13-23. https://doi.org/10.11648/j.ajcst.20240701.13

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ACS Style

Amellal, R. Adaptive Beamforming Based on Artificial Neural Networks. Am. J. Comput. Sci. Technol. 2024, 7(1), 13-23. doi: 10.11648/j.ajcst.20240701.13

Copy | Download

AMA Style

Amellal R. Adaptive Beamforming Based on Artificial Neural Networks. Am J Comput Sci Technol. 2024;7(1):13-23. doi: 10.11648/j.ajcst.20240701.13

Copy | Download

@article{10.11648/j.ajcst.20240701.13,
  author = {Rajaa Amellal},
  title = {Adaptive Beamforming Based on Artificial Neural Networks},
  journal = {American Journal of Computer Science and Technology},
  volume = {7},
  number = {1},
  pages = {13-23},
  doi = {10.11648/j.ajcst.20240701.13},
  url = {https://doi.org/10.11648/j.ajcst.20240701.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajcst.20240701.13},
  abstract = {Adaptive beamforming is technique of signal processing play important role to increasing capacity of the wireless communication and radar systems by configured the steerable of radiation pattern and maximize gain and directivity in a direction of arrival (DoA) of desired users in order to minimizing side lobe and reducing signal to interference. We review recently the classic technique of adaptive algorithms; we specified tow method for this preprocessing beam former LMS and RLS. The least Mean Square (LMS) operate the weight vectors of antenna array elements for beamforming by iterative process as well need to be continuously adapted to the ever-changing environment. Moreover recursive least square (RLS) give advantage for fast convergence beamforming. In this paper we proved the performance of this algorithms by updating the weights in addition process based on estimated vectors using neural network, The first phase for smart beam former are used by direction of arrival (DoA) estimated using radial basis neural network (RBFNN). In next step the targets is generated from the optimum weight calculated using Minimum Variance Distortion less method (MVDLM). Finally, the simulation result for the new process is synthetized and shows using Matlab application.
},
 year = {2024}
}

Copy | Download

TY  - JOUR
T1  - Adaptive Beamforming Based on Artificial Neural Networks
AU  - Rajaa Amellal
Y1  - 2024/02/20
PY  - 2024
N1  - https://doi.org/10.11648/j.ajcst.20240701.13
DO  - 10.11648/j.ajcst.20240701.13
T2  - American Journal of Computer Science and Technology
JF  - American Journal of Computer Science and Technology
JO  - American Journal of Computer Science and Technology
SP  - 13
EP  - 23
PB  - Science Publishing Group
SN  - 2640-012X
UR  - https://doi.org/10.11648/j.ajcst.20240701.13
AB  - Adaptive beamforming is technique of signal processing play important role to increasing capacity of the wireless communication and radar systems by configured the steerable of radiation pattern and maximize gain and directivity in a direction of arrival (DoA) of desired users in order to minimizing side lobe and reducing signal to interference. We review recently the classic technique of adaptive algorithms; we specified tow method for this preprocessing beam former LMS and RLS. The least Mean Square (LMS) operate the weight vectors of antenna array elements for beamforming by iterative process as well need to be continuously adapted to the ever-changing environment. Moreover recursive least square (RLS) give advantage for fast convergence beamforming. In this paper we proved the performance of this algorithms by updating the weights in addition process based on estimated vectors using neural network, The first phase for smart beam former are used by direction of arrival (DoA) estimated using radial basis neural network (RBFNN). In next step the targets is generated from the optimum weight calculated using Minimum Variance Distortion less method (MVDLM). Finally, the simulation result for the new process is synthetized and shows using Matlab application.

VL  - 7
IS  - 1
ER  -

Copy | Download