Memory Feature-Based Sample Selection Strategy for Few-Sample Learning Digital Predistortion

Abstract

This article proposed a novel sample selection strategy for reducing the computational complexity of digital predistortion (DPD). Due to the memory effect of the power amplifier (PA), the PA’s output is affected by the memory term. Thus, unlike existing sample selection methods (SSMs) that consider signal amplitude as the only feature, the proposed method regards signal points and their lagged terms (memory terms) as features of each sample point. We also introduce representative subset selection methods to further increase the selected samples’ diversity, and these methods are improved to reduce their storage and computational complexity. By expanding the diversity among the selected samples, even a few samples for training can obtain satisfactory performance. In addition, the complexity analysis shows that the proposed method is effective and competitive. Based on the experimental results, the proposed method outperforms the existing techniques in performance, complexity, and stability.