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Statistical Linearization of Phased Arrays Using Power Adaptive Power Amplifier Model

Phased arrays used in millimeter-wave systems challenge the concept of power amplifier (PA) linearization by digital predistortion (DPD). This is due to the shared digital path and inaccuracies in analog beamforming and other component variations. However, the group behavior of multiple parallel nonlinear branches can be expected to be more predictable due to averaging effect compared to a single branch behavior. In this paper, we use a power adaptive nonlinear model to mimic the average behavior of a single PA and utilize the probability distribution of the input power of each individual PA to approximate the expected nonlinear behavior of the array over-the-air. The approximated array response is used for the DPD training. The simulation results indicate that the proposed approach provides good linearization performance for large arrays that have varying amplitude and phase weights.