Abstract
Linear-to-circular polarization converters with wide-and multi-band capabilities can simplify antenna systems where circular polarization is desired. Multi-band solutions are attractive in satellite communication systems, which commonly have the additional requirement that the sense of polarization is reversed between bands. However, the design of these structures using conventional methods is ad hoc and relies heavily on empirical methods. Here, we employ a data-driven approach integrated with a generative adversarial network to explore the space of polarizer unit cells thoroughly. A triple-band reflective surface with stable performance over incident angles up to and including 30° is proposed.
Original language | English |
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Title of host publication | 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting |
Publisher | IEEE |
Pages | 447-448 |
Number of pages | 2 |
ISBN (Electronic) | 9781665496582 |
DOIs | |
Publication status | Published - 21 Sept 2022 |
Event | 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting - Denver, United States Duration: 10 Jul 2022 → 15 Jul 2022 |
Conference
Conference | 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting |
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Abbreviated title | AP-S/URSI 2022 |
Country/Territory | United States |
City | Denver |
Period | 10/07/22 → 15/07/22 |
Keywords
- machine-learning
- Polarizer
- satellite communications
ASJC Scopus subject areas
- Computer Networks and Communications
- Signal Processing
- Instrumentation