volume | PIER Journals

Abstract

This paper presents a comparative study of neural network (NN) training. The trained NNs are used as adaptive beamformers of antenna arrays. The training is performed either by a recently developed method called Mutated Boolean PSO (MBPSO) or by a well known beamforming method called Minimum Variance Distortionless Response (MVDR). The training procedure starts by applying the MBPSO and the MVDR to a set of random cases where a linear antenna array receives a signal of interest (SOI) and several interference signals at random directions of arrival (DOA) different from each other in the presence of additive Gaussian noise. For each case, the MBPSO and the MVDR are independently applied to estimate respective excitation weights that make the array steer the main lobe towards the DOA of the SOI and form nulls towards the DOA of the interference signals. The lowest possible value of side lobe level (SLL) is additionally required. The weights extracted by the MBPSO and the weights extracted by the MVDR are used to train respectively two different NNs. Then, the two trained NNs are independently applied to a new set of cases, where random DOA are chosen for the SOI and the interference signals. Finally, the radiation patterns extracted by the two NNs are compared to each other regarding the steering ability of the main lobe and the nulls as well as the side lobe level. The comparison exhibits the superiority of the NN trained by the MBPSO.

1. Li, J. and P. Stoica, Robust Adaptive Beamforming, John Wiley & Sons, Inc., Hoboken, New Jersey, 2006.

2. Castaldi, G., V. Galdi, and G. Gerini, "Evaluation of a neural-network-based adaptive beamforming scheme with magnitude-only constraints," Progress In Electromagnetics Research B, Vol. 11, 1-14, 2009.
doi:10.2528/PIERB08092303 Google Scholar

3. Umrani, A. W., Y. Guan, and F. A. Umrani, "Effect of steering error vector and angular power distributions on beamforming and transmit diversity systems in correlated fading channel ," Progress In Electromagnetics Research, Vol. 105, 383-402, 2010.
doi:10.2528/PIER10042902 Google Scholar

4. Byrne, D., M. O'Halloran, M. Glavin, and E. Jones, "Data independent radar beamforming algorithms for breast cancer detection," Progress In Electromagnetics Research, Vol. 107, 331-348, 2010.
doi:10.2528/PIER10061001 Google Scholar

5. Byrne, D., M. O'Halloran, E. Jones, and M. Glavin, "Transmittergrouping robust capon beamforming for breast cancer detection," Progress In Electromagnetics Research, Vol. 108, 401-416, 2010.
doi:10.2528/PIER10090205 Google Scholar

6. Lee, J.-H., Y.-S. Jeong, S.-W. Cho, W.-Y. Yeo, and K. S. J. Pister, "Application of the newton method to improve the accuracy of toa estimation with the beamforming algorithm and the music algorithm," Progress In Electromagnetics Research, Vol. 116, 475-515, 2011. Google Scholar

7. Zaharis, Z. D. and T. V. Yioultsis, "A novel adaptive beamforming technique applied on linear antenna arrays using adaptive mutated boolean PSO," Progress In Electromagnetics Research, Vol. 117, 165-179, 2011. Google Scholar

8. Mallipeddi, R., J. P. Lie, P. N. Suganthan, S. G. Razul, and C. M. S. See, "A differential evolution approach for robust adaptive beamforming based on joint estimation of look direction and array geometry," Progress In Electromagnetics Research, Vol. 119, 381-394, 2011.
doi:10.2528/PIER11052205 Google Scholar

9. Mallipeddi, R., J. P. Lie, P. N. Suganthan, S. G. Razul, and C. M. S. See, "Near optimal robust adaptive beamforming approach based on evolutionary algorithm," Progress In Electromagnetics Research B, Vol. 29, 157-174, 2011.
doi:10.2528/PIERB10110810 Google Scholar

10. Jabbar, A. N., "A novel ultra-fast ultra-simple adaptive blind beamforming algorithm for smart antenna arrays," Progress In Electromagnetics Research B, Vol. 35, 329-348, 2011.
doi:10.2528/PIERB11091504 Google Scholar

11. Wang, W., R. Wu, and J. Liang, "A novel diagonal loading method for robust adaptive beamforming," Progress In Electromagnetics Research C, Vol. 18, 245-255, 2011. Google Scholar

12. Liu, F., J. Wang, C. Y. Sun, and R. Du, "Robust mvdr beamformer for nulling level control via multi-parametric quadratic programming," Progress In Electromagnetics Research C, Vol. 20, 239-254, 2011. Google Scholar

13. Mallipeddi, R., J. P. Lie, S. G. Razul, P. N. Suganthan, and C. M. S. See, "Robust adaptive beamforming based on covariance matrix reconstruction for look direction mismatch," Progress In Electromagnetics Research Letters, Vol. 25, 37-46, 2011. Google Scholar

14. Zooghby, A. H. E., C. G. Christodoulou, and M. Georgiopoulos, "Neural network-based adaptive beamforming for one- and two-dimensional antenna arrays," IEEE Transactions on Antennas and Propagation, Vol. 46, No. 12, 1891-1893, Dec. 1998.
doi:10.1109/8.743843 Google Scholar

15. Song, X., J. Wang, and X. Niu, "Robust adaptive beamforming algorithm based on neural network," IEEE International Conference on Automation and Logistics (ICAL), 1844-1849, 2008.

16. Godara, L. C., Smart Antennas, CRC Press, Boca Raton, FL, 2004.

17. Gross, F. B., Smart Antennas for Wireless Communications with Matlab , McGraw-Hill, New York, 2005.

18. Ho, M.-H., S.-H. Liao, and C.-C. Chiu, "A novel smart UWB antenna array design by PSO," Progress In Electromagnetics Research C, Vol. 15, 103-115, 2010.
doi:10.2528/PIERC10051106 Google Scholar

19. Viani, F., L. Lizzi, M. Donelli, D. Pregnolato, G. Oliveri, and A. Massa, "Exploitation of parasitic smart antennas in wireless sensor networks," Journal of Electromagnetic Waves and Applications, Vol. 24, No. 7, 993-1003, 2010.
doi:10.1163/156939310791285227 Google Scholar

20. Christodoulou, C. and M. Georgiopoulos, Applications of Neural Networks in Electromagnetics, Artech House, Boston, London, 2001.

21. Yang, P., F. Yang, and Z.-P. Nie, "DOA estimation with subarray divided technique and interporlated esprit algorithm on a cylindrical conformal array antenna," Progress In Electromagnetics Research, Vol. 103, 201-216, 2010.
doi:10.2528/PIER10011904 Google Scholar

22. Park, G. M., H. G. Lee, and S. Y. Hong, "Doa resolution enhancement of coherent signals via spatial averaging of virtually expanded arrays," Journal of Electromagnetic Waves and Applications, Vol. 24, No. 1, 67-70, 2010.
doi:10.1163/156939310790322127 Google Scholar

23. Lui, H. S. and H. T. Hui, "Effective mutual coupling compensation for direction-of-arrival estimations using a new, accurate determination method for the receiving mutual impedance," Journal of Electromagnetic Waves and Applications, Vol. 24, No. 2-3, 271-281, 2010.
doi:10.1163/156939310790735598 Google Scholar

24. Liang, J. and D. Liu, "Two L-shaped array-based 2-D DOAs estimation in the presence of mutual coupling," Progress In Electromagnetics Research, Vol. 112, 273-298, 2011. Google Scholar

25. Kim, Y. and H. Ling, "Direction of arrival estimation of humans with a small sensor array using an artificial neural network," Progress In Electromagnetics Research B, Vol. 27, 127-149, 2011. Google Scholar

26. Xie, J., Z.-S. He, and H.-Y. Li, "A fast DOA estimation algorithm for uniform circular arrays in the presence of unknown mutual coupling," Progress In Electromagnetics Research C, Vol. 21, 257-271, 2011. Google Scholar

27. Bencheikh, M. L. and Y. Wang, "Combined esprit-rootmusic for DOA-dod estimation in polarimetric bistatic MIMO radar," Progress In Electromagnetics Research Letters, Vol. 22, 109-117, 2011. Google Scholar

28. Gotsis, K. A., K. Siakavara, and J. N. Sahalos, "On the direction of arrival (DoA) estimation for a switched-beam antenna system using neural networks," IEEE Transactions on Antennas and Propagation, Vol. 57, No. 5, 1399-1411, May 2009.
doi:10.1109/TAP.2009.2016721 Google Scholar

29. Fonseca, N., M. Coudyser, J.-J. Laurin, and J.-J. Brault, "On the design of a compact neural network-based DOA estimation system," IEEE Transactions on Antennas and Propagation, Vol. 58, No. 2, 357-366, Feb. 2010.
doi:10.1109/TAP.2009.2037766 Google Scholar

30. Zooghby, A. H., C. G. Christodoulou, and M. Georgiopoulos, "A neural network-based smart antenna for multiple source tracking," IEEE Transactions on Antennas and Propagation, Vol. 48, No. 5, 768-776, May 2000.
doi:10.1109/8.855496 Google Scholar

31. Patnaik, A., D. E. Anagnostou, R. K. Mishra, C. G. Christodoulou, and J. C. Lyke, "Applications of neural networks in wireless communications," IEEE Antennas and Propagation Magazine, Vol. 46, No. 3, 130-137, Jun. 2004.
doi:10.1109/MAP.2004.1374125 Google Scholar

32. Luo, M. and K.-M. Huang, "Prediction of the electromagnetic field in metallic enclosures using artificial neural networks," Progress In Electromagnetics Research, Vol. 116, 171-184, 2011. Google Scholar

33. O'Halloran, M., B. McGinley, R. C. Conceição, F. Morgan, E. Jones, and M. Glavin, "Spiking neural networks for breast cancer classification in a dielectrically heterogeneous breast," Progress In Electromagnetics Research, Vol. 113, 413-428, 2011. Google Scholar

34., Neural Network Toolbox^TM User's Guide R2010a, MATLAB The MathWorks, Inc..

Dr. Zaharias D. Zaharis

Dr. Konstantinos A. Gotsis

Prof. John Sahalos