Digital Predistortion of RF Power Amplifiers With Decomposed Vector Rotation-Based Recurrent Neural Networks
Tugce Kobal, Anding Zhu
Abstract
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 of DVR with recurrent states and redesigning the piecewise modeling, we propose a novel recurrent DVR scheme. To ensure stable operation and enhanced modeling accuracy, we integrate the recurrent DVR into the gated learning mechanism of the modified Just Another NETwork (JANET). Experimental results confirm that the proposed DVR-JANET model provides much improved linearization performance with significantly reduced model complexity compared with the recent existing models.
Topics & Concepts
PredistortionAmplifierComputer scienceLinearizationRadio frequencyRecurrent neural networkWidebandElectronic engineeringPower (physics)Artificial neural networkArtificial intelligenceControl theory (sociology)EngineeringBandwidth (computing)TelecommunicationsNonlinear systemPhysicsControl (management)Quantum mechanicsAdvanced Power Amplifier DesignRadio Frequency Integrated Circuit DesignGaN-based semiconductor devices and materials