Digital predistortion of power amplifiers using structured compressed-sensing Volterra series
Digital predistortion has become an attractive technique for power amplifier linearisation whose limiting factor for using Volterra series as the underlying model is its computational complexity, since the number of components rapidly grows with the non-linear order and memory. Based on a previous r...
| Authors: | , , , |
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| Format: | article |
| Status: | Published version |
| Publication Date: | 2017 |
| Country: | España |
| Institution: | Universidad de Sevilla (US) |
| Repository: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/130515 |
| Online Access: | https://hdl.handle.net/11441/130515 https://doi.org/10.1049/el.2016.3879 |
| Access Level: | Open access |
| Keyword: | Time-frequency analysis Iterative methods Bayes methods Volterra series Compressed sensing Computational complexity Power amplifiers |
| Summary: | Digital predistortion has become an attractive technique for power amplifier linearisation whose limiting factor for using Volterra series as the underlying model is its computational complexity, since the number of components rapidly grows with the non-linear order and memory. Based on a previous reference algorithm, which consists on applying the orthogonal matching pursuit for the sorting of the model components and a Bayesian information criterion for the selection of the optimum number of components, a new technique to reduce the size of the support set taking into account the structural information within a model is presented. Experimental results of the predistortion of a commercial power amplifier are given as a proof of its capabilities, showing equivalent performance to the pruning with the reference algorithm while further reducing the number of components. |
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