Artificial Intelligence-Based Power-Temperature Inclusive Digital Predistortion

Abstract

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.