Black-box Modeling and Compensation of Bursty Communication Signals in RF Power Amplifiers with Power-Dependent Parameters

TL;DR

This paper introduces a black-box modeling technique for RF power amplifiers that dynamically adapts to long-term memory effects induced by bursty signals, improving accuracy in modeling transient amplifier behavior.

Contribution

It extends behavioral models with power-dependent parameters to better capture long-term memory effects in RF power amplifiers, validated through experimental testing.

Findings

Achieved 2-2.5 dB improvement in average NMSE

Achieved 5-6 dB improvement in peak NMSE

Successfully modeled bursty communication signals

Abstract

This paper presents a new black-box technique for modeling long term memory effects in radio frequency power amplifiers. The proposed technique extends commonly used behavioral models by utilizing parameters that dynamically change depending on a long term memory effect while keeping the original model structure intact. This enables us to accurately track and model transient changes in power amplifier characteristics that vary slowly and are induced by the input signal. Identification of long term memory effects is discussed and an iterative identification algorithm for the model parameters is proposed. The model is experimentally tested on a 100 Watt Doherty power amplifier with a 4 MHz Gaussian noise signal that has a step--like change in the amplitude, representative of a realistic communication signal with bursty behavior and a 20 MHz 3GPP LTE test data. Results of behavioral modeling show a 2-2.5 dB and 5-6 dB improvement in average and peak NMSE modeling performance respectively, which shows the suitability of the technique to model bursty signals.