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Low Complexity Stochastic Optimization-Based Model Extraction for Digital Predistortion of RF Power Amplifiers
Kelly, Noel; Zhu, Anding
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. European Commission - European Regional Development Fund Science Foundation Ireland
Keyword(s): Digital predistortion; Linearization; Model extraction; Stochastic optimization; Simultaneous perturbation stochastic approximation; Power amplifier
Publication Date:
2017
Type: Journal article
Peer-Reviewed: Unknown
Language(s): English
Institution: University College Dublin
Publisher(s): IEEE
First Indexed: 2019-05-11 06:16:53 Last Updated: 2019-05-11 06:16:53