By characterizing complex memory effects and spectral regrowth, these models enable safe and systematic design evaluation without the risks of extensive hardware iterations. This thesis develops bandwidth-scalable neural behavioral modeling methods for wideband PAs, emphasizing accuracy, robustness under bandwidth variation, and implementation-relevant complexity. A structured recurrent formulation based on the nonlinear autoregressive neural network with exogenous inputs (NARXNN) is established as a favorable accuracy-complexity compromise relative to polynomial baselines
and representative neural network alternatives. To improve modeling fidelity in strongly nonlinear regimes, a piecewise NARXNN (PW-NARXNN) architecture is proposed by segmenting the operating space and training specialized NARXNN submodels ; on the reference dataset, PW-NARXNN improves NMSE to -39.2 dB with a moderate increase in parameters (666 compare to 377 of global NARXNN), and yields improved spectral fidelity. Bandwidth generalization is then investigated on a measured multi-band 5G-NR dataset spanning 20-100 MHz acquired on an LDMOS PA, using standard, interpolation, and extrapolation validation schemes to quantify robustness under bandwidth changes. Finally, a bandwidth-aware Figure of Merit (FoM) is introduced to unify comparison by combining mean accuracy, prediction stability, bandwidth sensitivity, and a logarithmic complexity penalty, providing a compact ranking and consistently identifying NARXNN as the most favorable trade-off among evaluated baselines.