TY - GEN
T1 - Optimization of the Far-Field Pattern of an X-band Electronically Controlled Reflectarray Antenna Assisted by Machine Learning
AU - Carignan, Louis Philippe
AU - Constantin, Nicolas
AU - Laurin, Jean Jacques
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In this paper, we use neural network (NN) machine learning (ML) to optimize the far field (FF) radiation pattern of an electronically reconfigurable reflectarray antenna (ERRA). ML enables us to predict the voltage set to apply on the unit cells of the ERRA to orient the main lobe in a desired direction and lower the sidelobe level.
AB - In this paper, we use neural network (NN) machine learning (ML) to optimize the far field (FF) radiation pattern of an electronically reconfigurable reflectarray antenna (ERRA). ML enables us to predict the voltage set to apply on the unit cells of the ERRA to orient the main lobe in a desired direction and lower the sidelobe level.
KW - Reflectarray
KW - machine learning
KW - neural network
UR - https://www.scopus.com/pages/publications/105002141777
U2 - 10.1109/EMTS57498.2023.10925225
DO - 10.1109/EMTS57498.2023.10925225
M3 - Contribution to conference proceedings
AN - SCOPUS:105002141777
T3 - 2023 URSI International Symposium on Electromagnetic Theory, EMTS 2023
SP - 7
EP - 9
BT - 2023 URSI International Symposium on Electromagnetic Theory, EMTS 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 URSI International Symposium on Electromagnetic Theory, EMTS 2023
Y2 - 22 May 2023 through 26 May 2023
ER -