Smart Antenna Design Using Neural Networks Theodoros N. Kapetanakis 1,2 , Ioannis O. Vardiambasis 1 , George S. Liodakis 1,2 , Melina P. Ioannidou 3 , and Andreas M. Maras 2 1 Department of Electronic Engineering Faculty of Applied Sciences Technological Educational Institute of Crete Chania, Crete 73100, Greece {todokape@chania.teicrete.gr, ivardia@chania.teicrete.gr, gsl@chania.teicrete.gr} 2 Department of Telecommunications Science & Technology University of Peloponnese Tripolis, 22100, Arcadia, Greece {todokape@chania.teicrete.gr, gsl@chania.teicrete.gr, amaras@uop.gr} 3 Department of Electronic Engineering Alexander Technological Educational Institute of Thessaloniki Thessaloniki 57400, Greece {melina@el.teithe.gr} Abstract: Optimizing antenna arrays to approximate desired far field radiation patterns is of exceptional interest in smart antenna technology. This paper shows how to apply artificial intelligence, in the form of neural networks, to achieve specific beam-forming with linear antenna arrays. Multilayer feed-forward neural networks are used to maximize multiple main beams’ radiation of a linear antenna array. In particular, a triple beam radiation pattern is presented in order to demonstrate the effectiveness and the reliability of the proposed approach. The results show that multilayer feed-forward neural networks are robust and can solve complex antenna problems. Keywords: Neural Networks, Smart antennas, Antenna arrays, Linear arrays, Beamforming. 1. INTRODUCTION Smart antennas have been widely used in mobile and wireless communication systems to increase signal quality, improve system capacity, enhance spectral efficiency, and upgrade system performance. Since the design of smart antenna arrays strongly affects their performance [1]-[2], in this paper we consider multiple main beams as the design criterion for the evaluation of smart antenna array’ performance. The synthesis of an antenna array with a specific radiation pattern is a nonlinear optimization problem, which cannot be effectively treated by traditional optimization techniques using gradients or random guesses [2]-[4]. Especially in complex cases of radiation shapes with multiple main beams and nulls at given directions, there are too many possible excitations and exhaustive checking of the best solution is very difficult [2]. However neural networks (NNs) are capable of solving this kind of complicated and nonlinear search problems [2], [5]-[10], especially in wireless communications. In general, Multilayer Perceptron (MLP), Radial Basis Function (RBF) and Hopfield-type NNs are the most suitable for use in various smart antenna applications [9]-[10]. Therefore, selection of the appropriate NN configuration parameters, such as the number of neurons, the number of layers, and the training algorithm, is crucial in NN design. Certain characteristics of the NN must be defined before its use, as an adequate structure must be chosen for the network and then trained and tested with a broad dataset for the required application [10]. 130