Multiband Antenna Design for Wlan and Wimax Applications

Antenna is a basic and a component of importance in all communication modules. Different techniques are involved in the design of an antenna for various applications. Handpicked machine learning algorithms like Random Forest, Decision Tree, and Artificial Neural Network (ANN) are utilized to optimize the parameters of an antenna that is proposed using the High Frequency Structure Simulator (HFSS) software. The objective is to find the optimal antenna configuration that maximizes its performance which shall be validated using antenna parameters. The performance parameters considered for the design of the antenna include the gain, bandwidth, and directivity. The non-optimized antenna parameters can negatively affect its performance. Therefore, it is essential to optimize the parameters involved in the design of antenna for optimal performance of the communication devices. Machine learning algorithms are trained by information obtained from the simulation solutions that arises through the HFSS. The generated antenna dataset includes various antenna configurations and their corresponding performance parameters. The trained models are then used to predict the optimal antenna parameters for a given set of performance requirements. The outcomes acquired from the machine learning models remain assessed with the performance of the antenna designed using HFSS without optimization. The comparison shows that the optimized antenna design yields better performance than the non-optimized design. This paper demonstrates the effectiveness of using machine learning algorithms to optimize antenna parameters, which can lead to improved performance and reduced design time in antenna engineering.