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This work presents a digital predistortion (DPD) scheme to linearize power amplifiers (PAs) using a recurrent neural network called Nonlinear AutoRegressive with eXogenous input model (NARX) neural network (NARXNN). The architecture of the NARXNN is based on a class of discrete‐time nonlinear system named NARX. Its topology has embedded memory at the input and output of the neural architecture, which...
This paper evaluates the capability of a Real-Valued Nonlinear Autoregressive with exogenous Input Neural Network (RVNARXNN) to model the nonlinear behavior of multi-standard RF Power Amplifiers (PAs). The RVNARXNN is a recurrent neural network that can be trained in feedforward mode and take the advantage of real-valued representation to reduce the complexity when complex signal are used. The RVNARXNN...
In this paper the hardware implementation of a NARX neural network algorithm using a Field Programmable Gate Array (FPGA) is presented. A NARX network is a Recurrent Neural Network (RNN) suitable for modeling nonlinear systems with promising results for the modeling of the inverse characteristics (AM/AM and AM/PM) of Power Amplifiers (PAs). The implementation is realized in the Xilinx ISE tool with...
This work presents a novel Digital Predistortion (DPD) scheme based on a NARX network, suitable for linearizing power amplifiers (PAs). The NARX network is a Recurrent Neural Network (RNN) with embedded memory that allows efficient modeling of nonlinear systems. Its neural architecture is very effective to model long term dependencies, such as the typical memory effects of PAs. To demonstrate the...
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