Implementation of a NARX neural network in a FPGA for modeling the inverse characteristics of power amplifiers

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 the Virtex-6 FPGA ML 605 Evaluation Kit using Verilog language. Experimental results have shown a high correlation with the inverse model computed with SystemVue in co-simulation with MATLAB for a GaN class F PA working with a LTE signal center at 2 GHz.