Abstract
Antenna arrays are capable of reducing co-channel interference and multipath fading. Beamwidth parameters have key role in the performance of an antenna array. Estimation of the first null beamwidth (FNBW) is important for the design of the array. Artificial neural networks (ANNs), also known as neural networks (NNs), use simple mathematical tools. The ability of trained ANNs to predict results for the unseen inputs makes them suitable for real-time applications. They can map the nonlinear behaviour of antenna arrays easily. This paper presents the neural estimations of the FNBW parameter for the broadside and end-fire uniform linear antenna arrays (ULAs), using radial basis function neural networks (RBF-NNs). Precise estimation of FNBW helps in achieving desired accuracy in array design and operation.
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