Power Amplifier Predistortion Using Reduced Sampling Rates in the Forward and Feedback Paths.

The feasibility of implementing digital predistortion for next-generation wireless communication is faced with a dilemma due to the ever-increasing demand for faster data rates. This causes the utilized bandwidth to increase significantly, as seen in the 5G NR standard in which bandwidths as high as 400 MHz are utilized. Hence, the development of new predistortion techniques in which the forward and feedback paths operate at lower sampling rates is of utmost importance to realize efficient and practical predistortion solutions. In this work, a novel predistortion technique is presented by which the predistortion is divided between the digital and analog domains. The predistorter is composed of a memoryless AM/AM gain function that is implementable in the analog domain, and a nonlinear model with memory effects in the digital domain to relax the sampling rate requirements on both the forward and feedback paths. Experimental validation was carried out with a 20 MHz and a 40 MHz 5G signal, and the results indicate minimal linearization degradation with a sampling rate reduction of 50% and 30%, respectively. This sampling rate reduction is concurrently applied in the digital-to-analog converter of the forward path and the analog-to-digital converter of the feedback path.

Augmented twin-nonlinear two-box behavioral models for multicarrier LTE power amplifiers.

A novel class of behavioral models is proposed for LTE-driven Doherty power amplifiers with strong memory effects. The proposed models, labeled augmented twin-nonlinear two-box models, are built by cascading a highly nonlinear memoryless function with a mildly nonlinear memory polynomial with cross terms. Experimental validation on gallium nitride based Doherty power amplifiers illustrates the accuracy enhancement and complexity reduction achieved by the proposed models. When strong memory effects are observed, the augmented twin-nonlinear two-box models can improve the normalized mean square error by up to 3 dB for the same number of coefficients when compared to state-of-the-art twin-nonlinear two-box models. Furthermore, the augmented twin-nonlinear two-box models lead to the same performance as previously reported twin-nonlinear two-box models while requiring up to 80% less coefficients.