FPGA-implementation of an adaptive neural network for RF power amplifier modeling

In this paper, we propose an architecture for FPGA-implementation of neural adaptive neural network RF power behavioral modeling. The real-valued time-delay neural network (RVTDNN) and the backpropagation (BP) learning algorithm were implemented on FPGA using Xilinx System Generator for DSP and the Virtex-6 FPGA ML605 Evaluation Kit. Performances obtained with 16-QAM modulated test signal and material resource requirement are presented for a network of six hidden layer neurons.