Piecewise NARX Behavioral Model for RF Power Amplifiers in 5G Applications

The complexity of RF PA behavior in 5G communication systems is driven by nonlinearities and long memory effects under wideband, high dynamic range signals; this poses significant challenges for existing models. Traditional Volterra-based models, such as the Generalized Memory Polynomial (GMP), struggle with overfitting, while neural network-based approaches require large datasets and exhibit high computational complexity both for training and inference. This paper presents a novel Piecewise Nonlinear AutoRegressive with eXogenous inputs (PW-NARX) model that combines the strengths of piecewise modeling and the NARX architecture to capture both nonlinear and memory effects efficiently over high dynamic ranges. Each sub-model in the piecewise framework operates within a different region of the input space, significantly reducing model complexity while maintaining high accuracy. Simulation results demonstrate that the PW-NARX model outperforms state-of-the-art models, achieving the lowest normalized mean square error (NMSE) of −39.18 dB and similar or better NMSE performance as other state-of-the-art models with fewer parameters.