A generalized dual-input digital predistortion (DPD) methodology for supply-modulated (SM) power amplifiers (PAs) is proposed. Contrary to classical approaches, the SM and the radio frequency (RF) signals are treated as separate digital inputs to be jointly predistorted. A multi-objective optimization (MOO) strategy allows to explore the inherent PA performance tradeoffs to jointly optimize the DPD coefficients for improved power-added efficiency (PAE) and higher RF output power, yet guaranteeing a prescribed linearity performance. To avoid dealing with an unbearably high number of experimental acquisitions, MOO is here made feasible by fast simulation of an empirical surrogate model of the PA, which is progressively refined from a reduced set of iterative acquisitions. The proposed technique enables the adoption of a dynamic supply shaping function, and it automatically accounts for the signals' statistics. Eventually, the method outperforms classical SM approaches, yet using a DPD of the same order, as demonstrated by the experimental results on a gallium nitride (GaN) SM PA operating at 3.5 GHz in the presence of orthogonal-frequency-division-multiplexing (OFDM)-like high-peak-to-average power ratio (PAPR) modulated signals with 10- and 20-MHz bandwidths (BWs).

While digital predistortion (DPD) usually targets only the linearity performance of the radio–frequency (RF) power amplifier (PA), this work addresses more than a single PA performance metric exploiting a multi-objective optimization approach. We present a predistorer learning procedure based on a constrained optimization algorithm that maximizes the RF output power, while guaranteeing a prescribed linearity level, i.e., a maximum normalized mean square error (NMSE) or adjacent-channel power ratio (ACPR). Experimental results on a Gallium Nitride (GaN) PA show that the proposed approach outperforms the classical indirect learning architecture (ILA), yet using the same predistorter structure with predetermined nonlinearity and memory orders.

Digitally controlled Dual-Input Doherty Power Amplifiers (DIDPAs) are becoming increasingly popular due to the flexible input signal splitting between the main and auxiliary stages. Nevertheless, the presence of many degrees of freedom, e.g., input amplitude split and phase displacement as well as biasing for multiple stages, often involves inefficient trial-and-error procedures to reach a suitable PA performance. This article presents automated parameter setting based on coordinate descent or Bayesian optimizations, demonstrating an improvement in the performance in terms of RF output power and power-added efficiency (PAE) in the presence of broadband-modulated signals, yet maintaining suitable linear behavior for, e.g., communications applications.

An approach based on machine learning is proposed for the global linearization of microwave active beamforming arrays. The method allows for the low-complexity real-time update of the digital predistortion (DPD) coefficients by exploiting order-reduced model features, hence avoiding the need for repeated local DPD identification steps across the various operating conditions of the beamformer (e.g., different beam angles or RF power levels). The validation is performed by over-the-air (OTA) measurements of a $1$ $\times$ $4$ array operating at 28 GHz across 100-MHz modulation bandwidth (BW).

This work reports on a wideband (WB) measurement system based on a vector network analyzer (VNA) topology for multiport large-signal measurements. The setup exploits a modified commercial VNA with WB intermediate frequency (IF) outputs that allow for more than 5-GHz analysis bandwidth (BW), where the IF is captured using an external high-speed oscilloscope. A three-port configuration using six receivers is described, together with the signal processing to achieve the high dynamic range required for WB signal measurements. The calibration procedure to establish reference planes at the three ports of the device-under-test (DUT) is shown. A comparison is made between single-tone continuous-wave (CW) calibration and WB calibration using multitone signals, illustrating advantages, challenges, and limitations. The system is then demonstrated for on-wafer WB characterization and linearization of a dual input Doherty power amplifier (DIDPA) using a 100-MHz instantaneous BW excitation signal at millimeter-wave frequencies. By leveraging on the error-corrected WB waves, an algorithm is proposed for modulated input signal control across a 600-MHz acquisition BW at the DUT on-wafer reference planes. The algorithm enables a user-defined emulated splitting of the input WB signals. The linearization performance of such an emulated PA configuration is finally evaluated.