In this article, we present a novel recurrent neural network (RNN)-based behavioral model to
linearize radio frequency (RF) power amplifiers (PAs) under wideband excitations. Based on …

The soil's compression modulus (Es) is one of the most critical mechanical parameters for
studying land subsidence in urban strata. Meanwhile, the vertical heterogeneity and lateral …

In this article, we propose a new behavioral modeling method to reduce the running
complexity and power consumption of digital predistortion (DPD) models for radio frequency …

In this article, a uniform neural network (NN) digital predistortion (DPD) model of radio
frequency (RF) power amplifiers (PAs) is proposed for dynamic applications, which is …

A novel behavioral modeling approach called adaptive model tree (AMT) is proposed for
digital predistortion (DPD) of RF power amplifiers (PAs) in fixed and time-varying …

XGBoost is the optimization of gradient boosting with the best overall performance among
machine learning algorithms. By introducing a regularization term into the loss function of …

Digital predistortion (DPD) is an effective linearization technique for RF power amplifiers
(PAs), but conventional full sampling (FS) DPD systems use ADCs with three to five times …

Machine learning methods, such as support vector regression (SVR) and gradient boosting,
have been introduced into the modeling of power amplifiers and achieved good results …

Load mismatch often occurs in radio frequency (RF) power amplifiers (PAs) in handset,
which can complicate the nonlinear behavior of the transmitter, particularly when the voltage …

We propose a new power amplifier (PA) behavioral modeling approach, to characterize and
compensate for the signal quality degrading effects induced by a PA with a machine …