Machine Learning Supported Antenna Design for UHF RFID Applications
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Keywords:
UHF, RFID, Antenna Design, Machine Learning, Gradient BoostingAbstract
RFID (Radio Frequency Identification) systems are crucial for automatic identification and
tracking processes in various industries, including logistics, supply chain management, retail, healthcare,
and security. This study primarily aims to design antennas for UHF (Ultra High Frequency) RFID
applications and subsequently enhance these designs using machine learning algorithms. For UHF band
antenna design, the frequency range of 860-960 MHz is utilized. Traditional antenna design methods
often rely on iterative and time consuming processes, which may not fully exploit the potential design
space. In this paper, machine learning algorithms are integrated to improve the efficiency and
effectiveness of antenna design. The research applies machine learning algorithms to determine optimal
values for factors such as the dimensions of the RFID antenna and the location of the feed. Models are
trained on a dataset to rapidly predict and optimize new designs. The optimized design achieved an
optimal S11 value of -35.80 dB at 870 MHz using the Gradient Boosting algorithm, showing a significant
improvement in resonant frequency. Multiple machine learning models were used, with Gradient
Boosting and Random Forest showing the best performance, achieving RMSE values of 1.24 and 1.52,
and R2 values of 0.87 and 0.88, respectively. The findings highlight the potential of machine learning to
optimize antenna design, offering more efficient and cost-effective solutions for UHF RFID applications.
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