Abstract
On Segmented Predistortion for Linearization of RF Power Amplifiers
Highlights
Digital predistortion is an efficient technique to linearize power amplifiers (PA) in wireless transmitters
Models derived from Volterra series such as generalized memory polynomial model (GMP) or DDR have proven their effectiveness for numerous applications using mildly nonlinear PA such as class AB PA
In order to take into account PA memory effects, one possible approach is to use block oriented nonlinear models (BONL) separating nonlinearity from memory effects such as Wiener, Hammerstein, Wiener-Hammerstein models
Summary
Digital predistortion is an efficient technique to linearize power amplifiers (PA) in wireless transmitters. Volterra series present two interesting properties for the modeling of nonlinear dynamic systems: generality and linearity of the model in function of their coefficients which simplifies their identification. Their number of coefficients increases dramatically with memory depth and order of nonlinearity. It represents new challenges for DPD in terms of bandwidth, nonlinearity and dynamic behavior It becomes difficult for a global DPD model to achieve an accurate representation of the system with good numerical properties and low computational complexity. It focuses on segmented models derived from Volterra series even if the presented principles can be applied to neural networks It starts with some mathematical generalities on interpolation, approximation and splines (Sec. 2).
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