Institute of Electrical & Electronic Engineers
In this paper, Field Programmable Gate Array (FPGA) implementation of two Power Amplifier (PA) dynamic behavioral modeling approaches with Real-Valued Time-Delay Neural Network (RVTDNN) and Real-Valued Recurrent Neural Network (RVRNN) architectures are presented. The proposed PA models are based on the Multi-Layer Perceptron (MLP) neural networks with delayed inputs to take into account nonlinearity and memory effects of the PA. The synoptic weights of these neural networks are dynamically updated in order to prevent any eventual change in the PA's characteristics. Both architectures have been optimized to include only six hidden neurons and implemented on FPGA using Xilinx system generator.