Compact wearable microstrip antenna design using hybrid quasi-Newton and Taguchi optimization

Abstract

A novel approach is introduced for designing a miniaturized wearable antenna. Utilizing Taguchi’s philosophy typically entails numerous experimentations runs, but our method significantly reduces these by employing a quasi-Newton approach with gradient descent to estimate process parameter ranges. This hybrid technique expedites convergence by streamlining experiments. Additionally, the Taguchi array ensures a balanced design, equalizing factor weights. Unlike conventional Taguchi methods, which risk trapping optimized results at local minima with increased repetitions, our modified technique mitigates this issue by adjusting level differences, aiming for global minima. Antenna design often involves competing objectives, such as size, impedance matching, cross-polarization, directivity, and frequency range. This study addresses these multiobjective challenges using a hybrid approach. The proposed method is applied to design and fabricate a biosafe miniaturized antenna for integration into clothing. The comparison of computed and measured antenna parameters confirms the accuracy of our solution while demonstrating a reduction in the required number of experiments. This innovative approach significantly advances the efficient design of wearable antennas. The biosafe wearable antenna demonstrated compliant specific absorption rate (SAR) (1.2 W/kg), robust mechanical performance (up to 40° bending), and underwent human body effect investigation. Comparison of computed and measured antenna parameters confirms solution accuracy. By implementing the proposed hybrid approach, computational time is significantly reduced by 98%, outperforming electromagnetic (EM) solvers’ built-in optimization.