A Block-Based LMS Using the Walsh Transform for Digital Predistortion of Power Amplifiers
Maxandre Fellmann, François Rivet, Nathalie Deltimple, Éric Kerhervé, H. Lapuyade, Yann Deval
Abstract
A novel non-linear adaptive filter for the linearization of Radio Frequency (RF) Power Amplifiers (PAs) is presented. In this study, we aim at reducing the Digital Predistortion (DPD) complexity and enhancing its convergence speed for reduced computation time. The Walsh Transform is used as a computational basis for evaluating a predistorter (PD) model. The mathematical properties of the Walsh theory are exploited to adapt a memory polynomial (MP) in the sequency domain. A block-based Walsh LMS is introduced to seek the optimal PD coefficients. Simulations and results of linearization of class-AB PAs are exhibited. The comparison with conventional DPD algorithms shows that the proposed method converges 10 times faster with a reduction of 12% of the complexity for similar accuracy. Finally, a complete DPD architecture based on the Walsh Transform is proposed.