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Low Complexity Stochastic Optimization-Based Model Extraction for Digital Predistortion of RF Power Amplifiers
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| File | Description | Size | Format | |
|---|---|---|---|---|
| TMTT-2015-10-1375R1_completemanuscript.pdf | 5.2 MB | Adobe PDF | Download |
| 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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