Abstract
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 S<inf>11</inf>-parameter measurement. A neural network is trained to retrieve the beam characteristics from the measured reflection coefficient S<inf>11</inf>. 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.
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