The next-generation (5G/6G) wireless communication aims to leapfrog the currently
occupied sub-6 GHz spectrum to the wideband millimeter-wave (MMW) spectrum. However …

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 future intelligent transmitter will dynamically adjust the transmission configuration on
demand, which will bring new challenges to digital predistortion (DPD). In this article, we …

In this article, we present a novel decomposed vector rotation (DVR)-based recurrent neural
network behavioral model for digital predistortion (DPD) of radio frequency (RF) power …

In this article, a probabilistic machine learning framework based on Gaussian process
regression (GPR) and principal component analysis (PCA) is proposed for the uncertainty …

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 …

Artificial intelligence (AI) provides opportunities to enable high-efficiency wireless
communication to dynamically adapt to the local environments and user demands. In this …

In this article, a novel dynamic activation digital predistortion (DPD) technique is proposed to
linearize radio frequency (RF) power amplifiers (PAs) for 6G dynamic spectrum aggregation …

To accomplish rapid adaptation of the digital predistortion (DPD) model, a low-complexity
parameter extraction architecture is proposed in this article. The extracted DPD model …

In this article, we present a novel digital predistortion (DPD) system which can be updated
quickly and efficiently in response to the dynamic reconfiguration of multiuser multiple-input …