Digital Predistortion of RF Power Amplifiers With Decomposed Vector Rotation-Based Recurrent Neural Networks

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 amplifiers (PAs) in wideband scenarios. By representing memory terms DVR with states and redesigning the piecewise modeling, propose scheme. To ensure stable operation enhanced modeling accuracy, integrate into gated learning mechanism modified Just Another NETwork (JANET). Experimental results confirm that proposed DVR-JANET provides much improved linearization performance significantly reduced complexity compared recent existing models.