Dual Feature Indexed Quadratic Polynomial-Based Piecewise Behavioral Model for Digital Predistortion of RF Power Amplifiers

This paper proposes a dual feature indexed quadratic polynomial-based piecewise (DIQP) behavioral modeling technique for digital predistortion (DPD) of RF transmitters. The proposed DIQP model is used to find the most suitable DPD model by performing a dual feature classification on the optimized submodels with a reuse-based function screening algorithm. The optimized submodel is adapted from the previous instantaneous sample indexed magnitude-selective affine (I-MSA) function-based model by transforming the original single linear term into a quadratic term with stronger fitting ability. This key improvement not only enhances the flexibility of the model but also boosts its fitting capability. The segmentation rule of the piecewise model has evolved from a simple threshold segmentation to a dual feature segmentation based on threshold and clustering segments. This reconstruction provides the model with enhanced feature-building capabilities. Additionally, the corresponding hybrid basis function screening (HBFS) algorithm and running complexity identification algorithm based on basis function reuse are proposed. The ingenious design of this reuse-based function screening algorithm not only enhances running efficiency but also ensures the overall performance of the model. The experimental part uses two different power amplifiers (PAs) for behavioral modeling and linearization tests. And the results of the experiments prove that the screened DIQP model is able to achieve the linearization performance-complexity trade-off excellently.