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PIER PIER B PIER C PIER M PIER Letters
2012-04-11
Adaptive Beamforming with Low Side Lobe Level Using Neural Networks Trained by Mutated Boolean PSO
By
Zaharias D. Zaharis,

    Dr. Zaharias D. Zaharis

    Telecommunications Centre

    Aristotle University of Thessaloniki

    Greece

    Homepage

Konstantinos A. Gotsis,

    Dr. Konstantinos A. Gotsis

    Department of Physics

    Aristotle University of Thessaloniki

    Greece

    Homepage

John Sahalos

    Prof. John Sahalos

    Department of Physics

    Aristotle University of Thessaloniki

    Greece

    Homepage

Progress In Electromagnetics Research, Vol. 127, 139-154, 2012
doi:10.2528/PIER12022806
Abstract
A new adaptive beamforming technique based on neural networks (NNs) is proposed. The NN training is accomplished by applying a novel optimization method called Mutated Boolean PSO (MBPSO). In the beginning of the procedure, the MBPSO is repeatedly applied to a set of random cases to estimate the excitation weights of an antenna array that steer the main lobe towards a desired signal, place nulls towards several interference signals and achieve the lowest possible value of side lobe level. The estimated weights are used to train efficiently a NN. Finally, the NN is applied to a new set of random cases and the extracted radiation patterns are compared to respective patterns extracted by the MBPSO and a well-known robust adaptive beamforming technique called Minimum Variance Distortionless Response (MVDR). The aforementioned comparison has been performed considering uniform linear antenna arrays receiving several interference signals and a desired one in the presence of additive Gaussian noise. The comparative results show the advantages of the proposed technique.
Citation
Zaharias D. Zaharis, Konstantinos A. Gotsis, and John Sahalos, "Adaptive Beamforming with Low Side Lobe Level Using Neural Networks Trained by Mutated Boolean PSO," Progress In Electromagnetics Research, Vol. 127, 139-154, 2012.
doi:10.2528/PIER12022806
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