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Benchmarking of learning architectures for digital predistortion

Magesacher, Thomas LU and Singerl, Peter (2017) 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016 In Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016 p.648-651
Abstract

Indirect and direct learning architectures are the two main parameter identification approaches for digital predistortion systems. While the indirect scheme is less complex, its inherent shortcomings are avoided by the direct learning approach. Trying to answer the question whether this advantage of the direct approach can be exploited in terms of measurable linearization-performance improvement in a predistortion platform for advanced power amplifier structures, we present a performance comparison based on laboratory results for wideband high-power Doherty amplifiers. Rather than using single-shot least-squares estimates, each architecture is combined with an adaptive parameter-update scheme to reach the desired performance range and... (More)

Indirect and direct learning architectures are the two main parameter identification approaches for digital predistortion systems. While the indirect scheme is less complex, its inherent shortcomings are avoided by the direct learning approach. Trying to answer the question whether this advantage of the direct approach can be exploited in terms of measurable linearization-performance improvement in a predistortion platform for advanced power amplifier structures, we present a performance comparison based on laboratory results for wideband high-power Doherty amplifiers. Rather than using single-shot least-squares estimates, each architecture is combined with an adaptive parameter-update scheme to reach the desired performance range and allow for a fair comparison. In conclusion, although the direct learning approach may excel in peak performance, the indirect learning approach achieves virtually the same average performance over linearization runs and has a clear advantage in terms of robustness.

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author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
pages
4 pages
publisher
IEEE Computer Society
conference name
50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
external identifiers
  • scopus:85016275335
DOI
10.1109/ACSSC.2016.7869123
language
English
LU publication?
yes
id
d29024a9-bba7-42db-98a6-40e2a66cc0d4
date added to LUP
2017-04-13 08:07:03
date last changed
2017-04-13 13:46:32
@inproceedings{d29024a9-bba7-42db-98a6-40e2a66cc0d4,
  abstract     = {<p>Indirect and direct learning architectures are the two main parameter identification approaches for digital predistortion systems. While the indirect scheme is less complex, its inherent shortcomings are avoided by the direct learning approach. Trying to answer the question whether this advantage of the direct approach can be exploited in terms of measurable linearization-performance improvement in a predistortion platform for advanced power amplifier structures, we present a performance comparison based on laboratory results for wideband high-power Doherty amplifiers. Rather than using single-shot least-squares estimates, each architecture is combined with an adaptive parameter-update scheme to reach the desired performance range and allow for a fair comparison. In conclusion, although the direct learning approach may excel in peak performance, the indirect learning approach achieves virtually the same average performance over linearization runs and has a clear advantage in terms of robustness.</p>},
  author       = {Magesacher, Thomas and Singerl, Peter},
  booktitle    = {Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016},
  language     = {eng},
  month        = {03},
  pages        = {648--651},
  publisher    = {IEEE Computer Society},
  title        = {Benchmarking of learning architectures for digital predistortion},
  url          = {http://dx.doi.org/10.1109/ACSSC.2016.7869123},
  year         = {2017},
}