Thresholds Optimization of Decomposed Vector Rotation Model for Digital Predistortion of RF Power Amplifier

In this paper, we propose an efficient approach for optimizing the decomposed vector rotation (DVR) model for digital predistortion (DPD). The DVR model’s basis functions are constructed piecewise by dividing the input space into segments bounded by thresholds. This paper investigates how to set the thresholds optimally using an iterative approach based on the decomposition of the global optimization problem into a set of unimodal sub-problems so that a unidirectional minimization can be used to optimize the positions of thresholds. The proposed approach has been evaluated using measurements from a real power amplifier (PA). The experimental results illustrate the efficiency of the proposed optimization approach and show that the thresholds’ optimization improves linearization performances significantly compared to conventional DVR with uniform segmentation

Assessment of digital predistortion methods for DFB-SSMF radio-over-fiber links linearization

This letter presents a comparative evaluation between three different behavioral models to perform digital predistortion (DPD) that enhances the linearity of radio-over-fiber (RoF)-based front haul links for the mobile network. In particular, the intention is to jump out of the volterra box and propose models based on segmentation approach. Especially the decomposed vector rotation (DVR) model is compared to volterra polynomials such as memory and generalized memory polynomial (GMP) architectures. DPD is employed to RoF links that are based on distributed feedback laser emitting at 1310 nm, and standard single-mode fiber for long-term evolution 20-MHz signal with 256-QAM modulation format. The effectiveness of the digital predistortion methodology is investigated for varying input powers in terms of normalized mean square error, adjacent channel power ratio, and error vector magnitude. The experimental results demonstrate that DVR achieves elevated linearization when compared to memory polynomial and GMP models