Neural network applications in smart antenna arrays: A review
Techniques employed in the synthesis of antenna arrays vary from complex analytical methods to iterative numerical methods based on optimisation algorithms. The drawback of these techniques is that they usually consider the array factor but not the interaction between array elements and real-time problems. This omission induces an error in the resultant radiation pattern; therefore, the physical relations between the array feeding details and the corresponding radiation patterns are taken into account to improve the accuracy. The behaviour of an antenna array is nonlinear in nature, resulting in an extremely high complexity using this approach, and it is usually disregarded. A neural-network-based solution can avoid complexity by establishing a relation between the desired radiation patterns and feeding details such as voltage and spacing in the real antenna array and can help convert the real array into a smart array. Several neural network applications in smart antenna array synthesis are reviewed in this paper.
smart antenna neural network (nn) radial basis function neural network (rbfnn) multi-layer perceptron neural network (mlpnn) hopfield neural network (hnn)