Optimal Segmentation for Piecewise RF Power Amplifier Models
(2016) In IEEE Microwave and Wireless Components Letters 26(11). p.909-911- Abstract
Accurate modeling of an RF power amplifier and/or its inverse is the core element of every digital predistortion system. An interesting alternative to the family of classic polynomial models are piecewise models, which divide the magnitude range into segments and define gain/phase-distortion through complex-valued functions on a per-segment basis. Naturally, the question arises whether a well-chosen non-uniform segmentation outperforms straightforward uniform segmentation and whether the benefit outweighs the extra effort. This work has two contributions: First, the segmentation that is optimal in the least-squares sense is determined jointly with the model coefficients and its benefit in terms of linearization improvement is... (More)
Accurate modeling of an RF power amplifier and/or its inverse is the core element of every digital predistortion system. An interesting alternative to the family of classic polynomial models are piecewise models, which divide the magnitude range into segments and define gain/phase-distortion through complex-valued functions on a per-segment basis. Naturally, the question arises whether a well-chosen non-uniform segmentation outperforms straightforward uniform segmentation and whether the benefit outweighs the extra effort. This work has two contributions: First, the segmentation that is optimal in the least-squares sense is determined jointly with the model coefficients and its benefit in terms of linearization improvement is demonstrated through measurements on a Doherty power amplifier. Second, a reduced-complexity approach with negligible performance loss is proposed.
(Less)
- author
- Magesacher, Thomas LU ; Singerl, Peter and Mataln, Martin
- organization
- publishing date
- 2016-11-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Digital predistortion (DPD), nonlinear least-squares, piecewise segmentation, power amplifier (PA), spline
- in
- IEEE Microwave and Wireless Components Letters
- volume
- 26
- issue
- 11
- article number
- 7726033
- pages
- 3 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- external identifiers
-
- wos:000388215900018
- scopus:84994317349
- ISSN
- 1531-1309
- DOI
- 10.1109/LMWC.2016.2614974
- language
- English
- LU publication?
- yes
- id
- e4fcecbe-8b8a-4f3c-9ad4-e53f72fe7cb6
- date added to LUP
- 2016-12-05 11:19:15
- date last changed
- 2024-01-19 14:47:46
@article{e4fcecbe-8b8a-4f3c-9ad4-e53f72fe7cb6,
abstract = {{<p>Accurate modeling of an RF power amplifier and/or its inverse is the core element of every digital predistortion system. An interesting alternative to the family of classic polynomial models are piecewise models, which divide the magnitude range into segments and define gain/phase-distortion through complex-valued functions on a per-segment basis. Naturally, the question arises whether a well-chosen non-uniform segmentation outperforms straightforward uniform segmentation and whether the benefit outweighs the extra effort. This work has two contributions: First, the segmentation that is optimal in the least-squares sense is determined jointly with the model coefficients and its benefit in terms of linearization improvement is demonstrated through measurements on a Doherty power amplifier. Second, a reduced-complexity approach with negligible performance loss is proposed.</p>}},
author = {{Magesacher, Thomas and Singerl, Peter and Mataln, Martin}},
issn = {{1531-1309}},
keywords = {{Digital predistortion (DPD); nonlinear least-squares; piecewise segmentation; power amplifier (PA); spline}},
language = {{eng}},
month = {{11}},
number = {{11}},
pages = {{909--911}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
series = {{IEEE Microwave and Wireless Components Letters}},
title = {{Optimal Segmentation for Piecewise RF Power Amplifier Models}},
url = {{http://dx.doi.org/10.1109/LMWC.2016.2614974}},
doi = {{10.1109/LMWC.2016.2614974}},
volume = {{26}},
year = {{2016}},
}