ISSN: 2277-3754 ISO 9001:2008 Certified International Journal of Engineering and Innovative Technology (IJEIT) Volume 2, Issue 5, November 2012 84 Performance Analysis of Linear Antenna Array Using Genetic Algorithm Shraddha Shrivastava, Kanchan Cecil Abstract- The genetic algorithm optimization method used in this paper for the synthesis of antenna array radiation pattern in adaptive beam forming. In this paper optimum value of weights of each antenna element is determined which produces radiation pattern with minimum side lobe level .Unlike Simple GA (SGA), real coded Genetic algorithm is used. Optimization is done using MATLAB. Adaptive feasible mutation is used which enables search in broader space along randomly generated directions to produce new generations. This improves the performance greatly to achieve the maximum reduction in side lobe level with minimum function calls. Keywords-Adaptive Beam Forming, Side Lobe Level, Genetic Algorithm, Linear Antenna Array, Array Factor. I. INTRODUCTION In many communication systems, point to point communication is used, for this highly directive beam of radiation is required. By arranging several dipoles in the form of an array or other antenna elements this can be achieved. Consider a linear array of n isotropic elements of equal amplitude and separated by distance d. The total field E at a far field point P in the given direction φ is given by, …. 1 Where Ψ is the total phase difference of the fields from adjacent sources. It is given by; One method to achieve a highly directional beam is to use adaptive beam forming. Adaptive beam forming is an adaptive signal processing technique in which an array of antenna is exploited to achieve maximum reception in a look direction in which the signal of interest is present, while signal of same frequency from other directions which are not desired (signal of not interest) are rejected. Adaptive beam forming enhances the desired signal while suppressing noise and interference at the output of array thereby improving the signal to interference plus noise ratio. The characteristics of the antenna array can be controlled by the geometry of the element and array excitation. But side lobe reduction in the radiation pattern [28],[31] should be performed to avoid degradation of total power efficiency and the interference suppression [2],[9] must be done to improve the Signal to noise plus interference ratio (SINR). Side lobe reduction and interference suppression can be obtained using the following techniques: 1) amplitude only control 2) phase only control 3) position only control and 4) complex weights (both amplitude and phase control). The process of choosing the antenna parameters to obtain desired radiation characteristics, such as the specific position of the nulls, the desired sidelobe level [4] and beam width of antenna pattern is known as pattern synthesis. Analytical studies by Stone who proposed binominal distribution, Dolph the Dolph- Chebyshev amplitude distribution, Taylor, Elliot, Villeneuve Hansen and Woodyard, Bayliss laid the strong foundation on antenna array synthesis[20]-[24]. Iterative Numerical method became popular in 1970s to shape the main beam. Today a lot of research on antenna array [2] – [12], is being carried out using various optimization techniques to solve electromagnetic problems due to their robustness and easy adaptively. One among them is Genetic algorithm [13]. R.L.Haupt has done much research on electromagnetics and antenna arrays using Genetic Algorithm [13]-[22]. In this paper, it is assumed that the array is uniform, where all the antenna elements are identical and equally spaced. The design criterion considered here is to minimize the sidelobe level [7] at a fixed main beam width. Hence the synthesis problem is, finding the weights that are optimum to provide the radiation pattern with maximum reduction in the sidelobe level. II. GENETIC ALGORITHM Genetic Algorithms are a family of computational models inspired by evolution [13],[25],[26]. These are global optimizers of objective functions. The GA will evolve a population of solutions towards a goal by stochastically searching out the best characteristics of individuals in a population and using them to create a „superior‟ species. Interestingly enough, there is no general GA theory to prove population convergence to a global or even a local optimum. Genetic algorithms reach an optimal solution by following the concepts and parameters set by Darwin. GAs utilizes known concepts such as chromosomes, genes, alleles, mating, and mutation. Many authors have covered the setup of GAs. The flowchart shown in Figure 1 outlines this technique. The genetic algorithm was first introduced in 1975 by Holland [25]. This algorithm has been realized and widely used after Goldberg‟s studies [26]. GA consists of a data structure of individuals called Population. The individuals in the population are then exposed to the process of evolution. Initial population is generated randomly. The consecutive generations (children) are created using the parents from the previous generation. Two parents are selected for reproduction using recombination. Recombination consists of two genetic operators namely 1) crossover and 2) mutation. Newly generated individuals are tested for their fitness based on the cost function and the best survives for the next generation. Genes from good individuals propagate throughout the population thus making the successive generation more suited to its