This article investigates the effects of the average power variation and the ambient temperature on the behavioral modeling and predistortion of high-power amplifiers concurrently. The measurement results showed that in addition to the average power variation, the behavior of a high-power PA is a function of ambient temperature as well. This article proposes a novel smart digital predistortion (DPD) model that considers both average power variation and temperature (PTI-DPD). The PTI-DPD is implemented using artificial intelligence-based model and it benefits from sets of data formats containing the signal, its delayed versions, average power variation, and ambient temperature. The developed architecture provides an uninterrupted linearization across the ambient temperature and average power variation span without need for a continues feedback path or multiple models. A series of measurements have been conducted to study the PTI-DPD performance across the temperature and average power range. According to the results, the PTI-DPD achieves an average of −48.5 dBc adjacent channel power ratio across the average power and temperature span.

The proposed angle-inclusive Digital Predistortion (DPD) technique mitigates angle-dependent distortions and load modulation effects in beamforming transmitters. Using baseband signals and steering angles as inputs, angle-inclusive DPD utilizes a 2D Convolutional Neural Network (2D-CNN) to pre-distort signals, achieving continuous linearization across the beam steering range. Training involves organizing input data into 2D images, each representing a specific steering angle. Measurements with a 60 MHz 64-QAM 5G NR signal on a 4-element linear array at 2.35 GHz show that angle-inclusive DPD achieves average ACPR and NMSE values of -48.6 dBc and -34.3 dB, respectively.