Algorithms for Numerical Analysis in High Dimensions
SIAM Journal on Scientific Computing
International Journal of RF and Microwave Computer-Aided Engineering
Multivariate Regression and Machine Learning with Sums of Separable Functions
SIAM Journal on Scientific Computing
Orthogonal polynomials for complex Gaussian processes
IEEE Transactions on Signal Processing - Part I
A Generalized Memory Polynomial Model for Digital Predistortion of RF Power Amplifiers
IEEE Transactions on Signal Processing
Novel Adaptive Nonlinear Predistorters Based on the Direct Learning Algorithm
IEEE Transactions on Signal Processing
A new Volterra predistorter based on the indirect learningarchitecture
IEEE Transactions on Signal Processing
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This paper is concerned with digital predistortion for linearization of RF high power amplifiers (HPAs). It has two objectives. First, we establish a theoretical framework for a generic predistorter system, and show that if a postdistorter exists, then it is also a predistorter, and therefore, the predistorter and postdistorter are equivalent. This justifies the indirect learning methods for a large class of HPAs. Second, we establish a systematic and general structure for a predistorter that is capable of compensating nonlinearity for a large variety of HPAs. This systematic structure is derived using approximation by separable functions, and avoids selection of predistorters based on the assumption of HPA models traditionally done in the literature.