Memory models for behavioral modeling and digital predistortion of envelope tracking power amplifiers

New advanced Envelope Tracking (ET) techniques can provide RF (Radio Frequency) transmitters with high-efficiency Power Amplifiers (PAs). On the other hand, system complexity substantially increases, requiring more advanced PA models for the representation and compensation of ET PA distortion effects. In this context, this paper proposes some solutions for behavioral modeling and digital predistortion of ET PAs. The adopted modeling strategy consists in including the modulated supply voltage as an additional independent model variable to define more accurate behavioral models capable of an increased accuracy when applied to model and compensate ET PAs. The new model variable is included in a polynomial model with memory whose nonlinear structure is derived from a binomial power series, whence the name of Memory Binomial Model (MBM). Another modeling approach is subsequently proposed, where the Cann model for static PA AM/AM nonlinearities is extended to model both AM/AM and AM/PM dynamic distortion occurring in ET PAs. The Extended Cann model includes an MBM structure for modeling dynamic AM/PM distortion effects. Both modeling approaches are tested on measured data-sets acquired using an ET measurement set-up including a commercial PA from RFMD and an envelope modulator designed using a commercial IC from Texas Instruments. The measured results showed that the proposed models could obtain a better modeling and predistortion performance when applied to ET PAs, with respect to the Memory Polynomial Model, here considered as a reference to represent the state-of-the-art of PA modeling and digital predistortion.