Low Complexity Stochastic Optimization-Based Model Extraction for Digital Predistortion of RF Power Amplifiers

摘要

This paper introduces a low-complexity stochastic optimization-based model coefficients extraction solution for digital predistortion of RF power amplifiers (PAs). The proposed approach uses a closed-loop extraction architecture and replaces conventional least squares (LS) training with a modified version of the simultaneous perturbation stochastic approximation (SPSA) algorithm that requires a very low number of numerical operations per iteration, leading to considerable reduction in hardware implementation complexity. Experimental results show that the complete closed-loop stochastic optimization-based coefficient extraction solution achieves excellent linearization accuracy while avoiding the complex matrix operations associated with conventional LS techniques.