AI-Driven Design of a Quasi-Digitally-Coded Wideband Microstrip Patch Antenna Array
2024 18th European Conference on Antennas and Propagation (EuCAP)(2024)
摘要
Artificial intelligence (AI) is enabling the automated design of contemporary antennas for numerous applications. Specifically, the use of machine learning (ML)-assisted global optimization techniques for the efficient design of modern antennas is now fast becoming a popular method. In this work, we demonstrate for the first time, the ML-assisted global optimization of a high-dimensional non-uniform overlapping quasi-digitally coded microstrip patch antenna array using a new AI-driven antenna design technique, called TR-SADEA (the training cost-reduced surrogate model-assisted hybrid differential evolution for complex antenna optimization). The TR-SADEA-generated array showed very promising simulated frequency responses for potential wideband applications with a -10 dB impedance bandwidth of 5.75 GHz to 10 GHz, a minimum in-band realized gain of 5.82 dBi, and a minimum in-band total radiation efficiency of 87.84%.
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关键词
AI,Antenna Optimization,TR-SADEA
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