Leveraging Artificial Intelligence for Enhanced Performance Prediction in Micro Strip Antenna Arrays

Abstract

Micro strip antenna arrays play a pivotal role in modern communication systems due to their compact size, lightweight design, and versatile applications. Despite these advantages, accurately predicting their performance poses significant challenges due to the complex interdependencies of design parameters and environmental factors. This research explores the integration of Artificial Intelligence techniques, emphasizing the potential of artificial intelligence (AI) and neural networks, to enhance the accuracy of performance prediction for microstrip antenna arrays. The proposed methodology employs a deep neural network (DNN) model that learns intricate patterns and nonlinear relationships among design variables, including substrate materials, geometries, and operational frequencies. By leveraging supervised learning on an extensive dataset of antenna configurations, the model demonstrates exceptional predictive accuracy for critical performance metrics such as gain, bandwidth, radiation efficiency, and beam steering capabilities. Simulation results underscore the effectiveness of the DNN approach, achieving prediction accuracies that outperform traditional analytical and empirical methods. Additionally, comparative evaluations with other Artificial Intelligence techniques, such as support vector machines and decision trees, highlight the superiority of neural networks in handling high-dimensional parameter spaces and complex nonlinearities. The results further reveal the computational efficiency of the proposed model, making it suitable for real-time performance optimization in practical applications. This study also presents a detailed analysis of simulation outcomes, showcasing the alignment between predicted and measured results. The visualizations of antenna patterns and performance metrics provide deeper insights into the predictive capabilities of the model. By integrating AI-driven solutions, this research contributes to advancing antenna design workflows, enabling engineers to develop high-performance and cost-effective antenna systems with reduced prototyping cycles. The findings affirm the transformative potential of machine learning, particularly neural networks, in addressing longstanding challenges in microstrip antenna design, paving the way for innovation in communication technology.