The Saleh behavioral model exhibits high prediction accuracy for nonlinearity of traveling-wave tube power amplifiers (TWT-PAs). However, the accuracy of the Saleh model degrades when modeling solid-state power amplifiers (SSPAs) technology. In addition, the polynomial expansion of the Saleh model consists of only odd-order terms as analyzed in this work. This paper proposes a novel model accuracy enhancement for the Saleh amplitude-to-amplitude (AM/AM) model when applied to radio frequency (RF) SSPAs. The proposed model enhancement accounts for the second-order intermodulation distortion, which is an important nonlinearity challenge in wideband wireless communications. The proposed static AM/AM model is a three-parameter rational function, which exhibits low complexity compared to the state-of-the-art behavioral models. A transpose architecture of finite-impulse digital filter is used to quantify the memory effect in SSPAs. A least-squares method is used for extracting all the model parameters. A linearization technique using a three-parameter digital predistortion model is also calculated to compensate for the AM/AM nonlinear distortion in SSPAs. Finally, the identification and evaluation of the enhanced Saleh model is presented based on measurements of RF SSPAs.
Vector Decomposition Based Time-Delay Neural Network Behavioral Model for Digital Predistortion of RF Power Amplifiers