Iterative Multimetric Model Extraction for Digital Predistortion of RF Power Amplifiers Using Enhanced Quadratic SPSA

In upcoming wireless systems, different linearity requirements in different frequency regions may be required due to data load and channel condition differences. This article presents a novel approach for identifying digital predistortion (DPD) coefficients that target multimetric linearity performance. First, a mathematical framework is established to evaluate the multimetric linearity performance in different frequency regions. Then, the DPD coefficients are extracted by optimizing the multimetric evaluation using the iterative quadratic simultaneous perturbation stochastic approximation (Q-SPSA) algorithm, which has been enhanced for improved numerical stability and reduced complexity. The experimental results demonstrate that the proposed approach achieves the multimetric requirements with fewer computational resources compared to conventional methods that only optimize the time-domain normalized mean square error (NMSE) metric. The proposed approach sheds light on DPD models, showing that DPD coefficients can be optimized to have different multimetric performances as the iteration process progresses. Detailed experimental results and complexity analysis further support the potential of the proposed approach for future systems.