Estimation of Small Antenna Performance Using a Machine Learning Approach

Julian Roqui (1) , Luca Santamaria (1) , Lyes Khacef (1) , Alain Pegatoquet (1) , Leonardo Lizzi (1)

1 LEAT - Laboratoire d'Electronique, Antennes et Télécommunications

This paper presents an approach based on Machine Learning (ML) to estimate the performance of small antennas in practical applications. As a proof of concept, the case of printed antennas integrated into compact terminals, such as for Internet of Things (IoT) or 5G applications, is considered.

Mots clés: Maximum likelihood estimation; 5G mobile communication; Impedance matching; Meetings; Machine learning; Internet of Things; Antennas

Domaines: Electromagnétisme; Intelligence artificielle [cs.AI]

Julian Roqui, Luca Santamaria, Lyes Khacef, Alain Pegatoquet, Leonardo Lizzi. Estimation of Small Antenna Performance Using a Machine Learning Approach. 2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, IEEE, Jul 2020, Montreal, Canada. pp.581-582, ⟨10.1109/IEEECONF35879.2020.9330405⟩. ⟨hal-02515496⟩