Design of Nonstandard Waveguide Slot Array Antenna for High‐Power Applications
Abstract
Waveguide slot array antennas are extensively used in radar systems, satellite communications, and wireless networks due to their high gain, low side lobe levels, and good impedance matching. Conversely, designing these antennas for high‐power scenarios with non‐standard configurations presents challenges. This paper proposes a novel Mode Matching Based Deep Neural Method (MMbDNM) to optimize the design and performance of waveguide slot array antennas. By integrating deep learning with mode‐matching techniques, the method streamlines antenna design, reducing development time and cost while maintaining high performance across multiple frequency bands. The proposed (MMbDNM) method achieves a frequency of 10.4 GHz; the antenna achieves a high coupling coefficient of 0.99, indicating efficient energy transfer between slots. The power handling capability of 0.99 W ensures robust performance under high‐power conditions, while the radiation pattern with a gain of 0.95 dB suggests effective signal propagation. Additionally, a bandwidth of 1.1 GHz allows for versatile application across a range of frequencies, contributing to the antenna's overall efficiency and adaptability. The results demonstrate improved antenna efficiency and adaptability, making it ideal for waveguide junctions used in complex electromagnetic systems.
Graphical Abstract
The proposed Mode Matching Based Deep Neural Method (MMbDNM) optimizes waveguide slot array antenna designs, enhancing coupling efficiency, and minimizing signal loss.
By integrating deep learning techniques, this approach streamlines the design process, reduces costs, and improves performance in high‐power applications compared to traditional methods.
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Published: 24 December 2024
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