# Discontinuity Characterization and Low-Complexity Smoothing in RF-PA Polynomial Piecewise Modeling

## TL;DR

This paper introduces a new method to smooth discontinuities in power amplifier models, improving prediction accuracy and digital predistortion performance.

## Contribution

A low-complexity smoothing technique using a raised cosine weighting function to address discontinuities in piecewise amplifier models.

## Key findings

- The proposed smoothing technique reduces NMSE by up to 3 dB in PA modeling.
- The method suppresses spectral errors across various 5G/LTE signals and amplifier architectures.
- The approach requires no retraining and works with existing modeling workflows.

## Abstract

Piecewise modeling of power amplifiers (PAs) typically involves assembling different polynomials to capture nonlinear behavior across different operating regions. However, recombining these sub-models can introduce discontinuities at segment boundaries, degrading prediction accuracy and potentially impacting digital predistortion (DPD) performance. This work addresses this issue by introducing a statistical framework to detect discontinuities through localized variations in the conditional mean and variance of amplitude and phase responses. Using the Vector-Switched Generalized Memory Polynomial (VS-GMP) as a case study, we propose a low-complexity post-processing smoothing technique based on a raised cosine weighting function applied at model transition regions. Unlike structural approaches, the method requires no retraining and integrates seamlessly into existing workflows as a post-processing tool. Experimental validation across two PA architectures (Doherty and Single-Stage) and multiple 5G/LTE signals (20–200 MHz bandwidth, up to 11 dB PAPR, including carrier aggregation) demonstrates consistent improvements: up to a 3 dB NMSE reduction and notable spectral error suppression.