Antenna radiation pattern predictions with machine learning

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference contributionScientificvertaisarvioitu

Abstrakti

A machine-learning based method to characterize integrated antennas is presented. The technique allows fast characterization with significantly reduced complexity compared to the previous antenna tests with separate scanned probe and receiver. The broadband reflection from a quasirandom target conveys the antenna characteristics in the reflection coefficient or S11-parameter measurement. A neural network is trained to retrieve the beam characteristics from the measured reflection coefficient S11. The antenna measurement setup is simulated as a reflection measurement with the antenna under test (AUT) facing quasirandom reflective mask. The reflection coefficient is calculated as the coupling coefficient between the AUT radiated field and the back-reflected field at 75-110 GHz, and it is fed to a fully-connected neural network and trained to the beam-steering angles and beamwidths. The predicted median beam direction error is 4.1° and beamwidth error is 2.2°. The technique is promising, as it allows for antenna characterization without scanned or rotated antennas, yet providing sufficient accuracy for antennas with moderate directivity.

AlkuperäiskieliEnglanti
Otsikko2021 IEEE Conference on Antenna Measurements and Applications, CAMA 2021
KustantajaIEEE
Sivut434-437
Sivumäärä4
ISBN (elektroninen)9781728196978
DOI - pysyväislinkit
TilaJulkaistu - 2021
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaIEEE International Conference on Antenna Measurements and Applications - Antibes Juan-les-Pins, Ranska
Kesto: 15 marrask. 202117 marrask. 2021

Conference

ConferenceIEEE International Conference on Antenna Measurements and Applications
LyhennettäCAMA
Maa/AlueRanska
KaupunkiAntibes Juan-les-Pins
Ajanjakso15/11/202117/11/2021

Sormenjälki

Sukella tutkimusaiheisiin 'Antenna radiation pattern predictions with machine learning'. Ne muodostavat yhdessä ainutlaatuisen sormenjäljen.

Siteeraa tätä