Accurate and Efficient Pattern Synthesis Using Generative Adversarial Network for Series-Fed Microstrip Antenna Array With New Elements

Due to the unique geometry-dependent control of element amplitudes and phases, accurate and flexible pattern synthesis remains complex and challenging for series-fed microstrip antennas (SFMAs). In this communication, a modified physics-guided generative adversarial network is developed to synthesize SFMA geometries for given pattern objectives. This synthesis framework offers higher efficiency and better synthesized patterns compared to traditional metaheuristic algorithms. Additionally, it does not require network pretraining. Moreover, new trapezoidal radiating elements acquiring ultra-low reflection over a wide tuning range of coupling coefficients are proposed to maintain the traveling-wave mode, which is essential for accurate pattern control. Unlike other designs, these elements do not require additional reflection-canceling structures, thereby simplifying the antenna structure and synthesis process. Measured results of prototypes with a cosecant-squared pattern and a low-sidelobe pattern show excellent agreement with their respective pattern objective across 79–81 GHz, which validate the effectiveness of the synthesis method and the proposed element.