Abstract
Thresholds Optimization of Decomposed Vector Rotation Model for Digital Predistortion of RF Power Amplifier
Highlights
Higher data rates require to use spectral efficient modulation techniques such as Orthogonal Frequency Division Multiplexing (OFDM)
The decomposed vector rotation (DVR) model is an extension of the canonical piecewise linear (CPWL) model, which is capable of representing a wide range of continuous nonlinear functions with high precision [11]
The normalized mean square error (NMSE) of the proposed approach has been significantly improved by nearly 6.5 dB, as it can be seen from Fig. 4 for = 4
Summary
Higher data rates require to use spectral efficient modulation techniques such as Orthogonal Frequency Division Multiplexing (OFDM). The modulated signals have a high peak-to-average power ratio (PAPR), which stimulates PA’s nonlinearities. Many works in the literature are dedicated to the DVR model with uniform segmentation, few of them deal with optimal segmentation. The benefit of optimal segmentation versus uniform segmentation has already been established in [9] where the authors suggest to reduce the complexity of the algorithm by considering the memoryless version of the actual DVR model. We propose a new approach in which the optimization problem is decomposed into a set of unimodal sub-problems that allow to use unidirectional minimization and decrease the search complexity dramatically.
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