  A non-parametric technique for modeling the behavior of power amplifiers is
presented. The proposed technique relies on the principles of density
estimation using the kernel method and is suited for use in power amplifier
modeling. The proposed methodology transforms the input domain into an
orthogonal memory domain. In this domain, non-parametric static functions are
discovered using the kernel estimator. These orthogonal, non-parametric
functions can be fitted with any desired mathematical structure, thus
facilitating its implementation. Furthermore, due to the orthogonality, the
non-parametric functions can be analyzed and discarded individually, which
simplifies pruning basis functions and provides a tradeoff between complexity
and performance. The results show that the methodology can be employed to model
power amplifiers, therein yielding error performance similar to
state-of-the-art parametric models. Furthermore, a parameter-efficient model
structure with 6 coefficients was derived for a Doherty power amplifier,
therein significantly reducing the deployment's computational complexity.
Finally, the methodology can also be well exploited in digital linearization
techniques.
