Heterogeneous Basis Parameter Combination Method and Dynamic Transfer Strategy for Digital Predistortion of RF Power Amplifiers

To accomplish rapid adaptation of the digital predistortion (DPD) model, a low-complexity parameter extraction architecture is proposed in this article. The extracted DPD model coefficients are represented by a linear combination of the previous parameters (or pretrained parameters) in a novel basis parameter combination (BPC) method, thereby avoiding the extraction of high-dimensionality coefficients and significantly lowering the computational cost. Then, we developed a feature mapping technique (FMT) to coordinate the feature spaces corresponding to different DPD model structures, which facilitates the transfer learning of heterogeneous DPD model coefficients as the DPD model structure should be modified with the transmission configuration changes. Due to the good scalability of the proposed method, a dynamic transfer strategy (DTS) is presented to enhance the method's flexibility and avoid incremental complications by combining it with dimensionality reduction techniques. The experimental results demonstrate that the proposed method outperforms the state-of-the-art in terms of computational complexity, adaptability, and modeling precision.