Optimization of a pruned 2-D Digital Predistortion Model Structure for Power Amplifiers Linearization

Siqi Wang (1, 2) , Morgan Roger (2) , Caroline Lelandais-Perrault (2)

1 L2E - Laboratoire d'Electronique et Electromagnétisme
2 GeePs - Laboratoire Génie électrique et électronique de Paris

Two-dimensional digital predistortion (2-D DPD) is one of the most commonly used approaches to linearize the concurrent dual-band radio frequency (RF) power amplifiers (PA). This paper explores the use of a hill-climbing optimization heuristic to determine the optimal structure for 2-D DPD. To improve convergence and reduce computation time, we propose a new parameterization for the 2-D DPD model. The proposed search criterion is based on the generalized information criterion, which represents the trade-off between the DPD linearization accuracy in both bands and the model complexity. A comparison against a compressed-sensing-based method is also made. The effectiveness of the proposed method is validated with two 20~MHz long-term-evolution (LTE) signals on two carriers with 100~MHz frequency separation.

Mots clés: digital predistortion; information criterion; nonlinear distortion; Optimization; RF power amplifiers; Index Terms-Digital predistortion; non- linear distortion; opitmization

Domaines: Traitement du signal et de l'image [eess.SP]; Micro et nanotechnologies/Microélectronique; Electronique

Siqi Wang, Morgan Roger, Caroline Lelandais-Perrault. Optimization of a pruned 2-D Digital Predistortion Model Structure for Power Amplifiers Linearization. NEWCAS 2019 17th IEEE International New Circuits and Systems Conference, Jun 2019, Munich, Germany. ⟨10.1109/NEWCAS44328.2019.8961300⟩. ⟨hal-02054960v2⟩