Low Complexity Stochastic Optimization-Based Model Extraction for Digital Predistortion of RF Power Amplifiers

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Title: Low Complexity Stochastic Optimization-Based Model Extraction for Digital Predistortion of RF Power Amplifiers
Authors: Kelly, Noel
Zhu, Anding
Permanent link: http://hdl.handle.net/10197/8389
Date: May-2016
Abstract: This paper introduces a low-complexity stochastic optimization-based model coefficients extraction solution for digital predistortion of RF power amplifiers (PAs). The proposed approach uses a closed-loop extraction architecture and replaces conventional least squares (LS) training with a modified version of the simultaneous perturbation stochastic approximation (SPSA) algorithm that requires a very low number of numerical operations per iteration, leading to considerable reduction in hardware implementation complexity. Experimental results show that the complete closed-loop stochastic optimization-based coefficient extraction solution achieves excellent linearization accuracy while avoiding the complex matrix operations associated with conventional LS techniques.
Funding Details: European Commission - European Regional Development Fund
Science Foundation Ireland
Type of material: Journal Article
Publisher: IEEE
Copyright (published version): 2016 IEEE
Keywords: Digital predistortion;Linearization;Model extraction;Stochastic optimization;Simultaneous perturbation stochastic approximation;Power amplifier
DOI: 10.1109/TMTT.2016.2547383
Language: en
Status of Item: Peer reviewed
Appears in Collections:Electrical and Electronic Engineering Research Collection

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