Sparsely Shared Module-Based Parameter Extraction for Behavioral Modeling and Digital Predistortion of RF Power Amplifiers

The extraction of linear parameters from high-dimensional features is usually time-consuming, which hinders the timely response of the digital predistortion (DPD) module to changes in the nonlinear characteristics of the radio frequency (RF) power amplifier (PA). To address this issue, we propose a novel sparsely shared module (SSM)-based parameter identification for DPD of RF PAs. Initially, we demonstrate the utilization of the shared module to integrate the commonality of PA nonlinear behaviors across different transmission configurations and to reduce the consumption of storage resources by introducing sparse model masks. This is a move that also facilitates feature mapping among heterogeneous DPD models because it avoids several redundant constructions of basis function matrices. Then, based on the sparsity of the columns in the shared module, a novel sparsity-ordering-based dimensionality reduction technique is proposed to further decrease the number of parameters to be extracted. Finally, considering the distinct performance requirements for different configurations, the sparsity of different DPD models in the shared module is flexible by adjusting allowable performance loss, which is beneficial for the on-demand allocation of the system resource. Experimental results indicate that the proposed method can attain satisfactory modeling accuracy by extracting only a few model coefficients.