Performance study on nonlinear models of radio frequency power amplifiers based on neural networks

As wireless communication systems continue to evolve, the study of Radio Frequency (RF) Power Amplifiers (PAs) has attracted growing interest. Addressing the modeling issues of PAs can provide more convenient research methods and more efficient guidance in fields such as power amplifier predistortion, power amplifier design and evaluation, and the identification of subtle characteristics of RF radiation sources. This paper investigates RF PA nonlinear modeling methods based on neural networks, using BP, Elman, and RBF neural network algorithms to model and simulate five common nonlinear models, namely Saleh, Rapp, memoryless polynomial, Volterra, and memory polynomial models. This study not only demonstrates the performance differences of various algorithms across different models but also provides important references for the selection and optimization of algorithms in practical applications.