An approach based on machine learning is proposed for the global linearization of microwave active beamforming arrays. The method allows for the low-complexity real-time update of the digital predistortion (DPD) coefficients by exploiting order-reduced model features, hence avoiding the need for repeated local DPD identification steps across the various operating conditions of the beamformer (e.g., different beam angles or RF power levels). The validation is performed by over-the-air (OTA) measurements of a 1×4 array operating at 28 GHz across 100-MHz modulation bandwidth (BW).
Mengozzi, M., Gibiino, G.P., Angelotti, A.M., Florian, C., Santarelli, A. (2023). Beam-Dependent Active Array Linearization by Global Feature-Based Machine Learning. IEEE MICROWAVE AND WIRELESS TECHNOLOGY LETTERS, 33(6), 895-898 [10.1109/LMWT.2023.3269140].
Beam-Dependent Active Array Linearization by Global Feature-Based Machine Learning
Mengozzi, Mattia
;Gibiino, Gian Piero;Angelotti, Alberto M.;Florian, Corrado;Santarelli, Alberto
2023
Abstract
An approach based on machine learning is proposed for the global linearization of microwave active beamforming arrays. The method allows for the low-complexity real-time update of the digital predistortion (DPD) coefficients by exploiting order-reduced model features, hence avoiding the need for repeated local DPD identification steps across the various operating conditions of the beamformer (e.g., different beam angles or RF power levels). The validation is performed by over-the-air (OTA) measurements of a 1×4 array operating at 28 GHz across 100-MHz modulation bandwidth (BW).| File | Dimensione | Formato | |
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