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.

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.

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