An experimentally calibrated multiport-network model enables optimization of dynamic metasurface antenna configuration sequences for joint DoA-polarization estimation, delivering the largest gains over random sequences in the intermediate-SNR and intermediate-length regime.
Learned integrated sensing pipeline: reconfigurable metasurface transceivers as trainable physical layer in an artificial neural network,
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Optimizing Dynamic Metasurface Antenna Configurations for Direction-of-Arrival and Polarization Estimation Using an Experimentally Calibrated Multiport-Network Model
An experimentally calibrated multiport-network model enables optimization of dynamic metasurface antenna configuration sequences for joint DoA-polarization estimation, delivering the largest gains over random sequences in the intermediate-SNR and intermediate-length regime.