Microstrip Antenna Design Using A Genetic Algorithm

As wireless communication systems continue to enjoy increased application and gain wider acceptance, performance and cost constraints on the antennas employed by these systems become more difficult to meet. Since the antenna is at the front end of different communication system and it is the initial signal reception apparatus, the performance of which directly determines the quality of communication system. For example, the radio frequency identification systems, data are transferred without contact to a local querying system (reader) from a remote transponder (tag), including an antenna and a microstrip transmitter. A suitable antenna for the tag must have low cost, low profile, and especially small size. Microstrip antenna meets this requirement greatly, which holds the advantage of small size, light weight, easy integration. However, for microstrip antenna, the improvement of bandwidth normally results in the degradation of size, which is a great challenge in the design of antenna.Genetic algorithm (GA) is a random search optimization algorithm, genetic algorithm is famous for its simple, universal, robust, and suitable for parallel processing, which attracts the attentions of many researchers.A method to microstrip antenna design and optimization using a genetic algorithm (GA) is presented in this article. Firstly, the algorithm is based on the structure of antenna and it is applied to the optimization of antenna successfully by combining the different structural parameters. Then, inspired by slotted patch optimization method, we start with ground plane and combine with the novel antenna structure, using the defected ground plane for improvement of the antenna performance with the width and length of the slots used for optimal selection. After the optimization, 43.5%bandwidth is achieved based on the original18.5%bandwidth structure while the gain remains the same. The antenna prototype is fabricated and measured, excellent agreement between simulated and measured results is observed. This proves, by means of genetic algorithm combined with electromagnetic simulation software, it can be well applied to the optimization of antenna design and bring convenience to our project as well as improve the efficiency.