Abstract: The augmented twin nonlinear two-box modeling and predistortion method for power amplifiers and transmitters provides power amplifier distortion modeling and predistortion linearization. A memoryless nonlinearity is combined with a memory polynomial function that includes cross-terms. The method can utilize an augmented forward twin-nonlinear two-box model, an augmented reverse twin-nonlinear two-box model, or alternatively, an augmented parallel twin-nonlinear two-box model. The present two-box models are validated in modeling and predistortion applications. Measurement results demonstrate the superiority of the present two-box models with respect to conventional state of the art models. The present two-box models lead to better accuracy with reduced complexity.

Abstract: The digital predistortion system and method with extended correction bandwidth includes a predistortion system that uses a two-box architecture based on the cascade of a memory polynomial followed by a memoryless predistortion function. The memoryless predistorter is identified offline and used to perform a coarse linearization which cancels out most of the static nonlinearity of the device under test allowing for a reduced observation bandwidth for the synthesis of the memory polynomial predistortion sub-function.

Abstract: The scalable digital predistortion system provides a behavioral model that can be used to model and compensate for the nonlinear distortions of power amplifiers and transmitters. The predistorter and update algorithms make the model/DPD scalable in terms of signal bandwidth and average power, allowing for low complexity update following changes in the signal's bandwidth and/or power level. Experimental validation carried on a 300 Watt Doherty power amplifier shows that the scalable model and the predistorter architecture achieve performance similar to their conventional counterpart. However, the present model/predistorter requires the update of up to 50% fewer coefficients than the conventional model/predistorter.