Finding Structural Information of RF Power Amplifiers using an Orthogonal Non-Parametric Kernel Smoothing Estimator

TL;DR

This paper introduces a non-parametric kernel smoothing method for modeling RF power amplifiers that transforms input data into an orthogonal domain, enabling efficient, accurate, and simplified amplifier modeling and digital linearization.

Contribution

It presents a novel orthogonal non-parametric kernel estimator approach for power amplifier modeling, reducing complexity while maintaining high accuracy and facilitating model pruning.

Findings

Achieved error performance comparable to state-of-the-art parametric models.

Derived a 6-coefficient model for a Doherty power amplifier, reducing computational complexity.

Demonstrated effectiveness in digital linearization applications.

Abstract

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.