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On-Demand Real-Time Optimizable Dynamic Model Sizing for Digital Predistortion of Broadband RF Power Amplifiers
Author(s)
Date Issued
2020-07
Date Available
2021-03-10T16:47:12Z
Abstract
© 1963-2012 IEEE. In this article, we present a dynamic model sizing approach for digital predistortion (DPD) of broadband radio-frequency power amplifiers. By employing a novel model structure adaptation algorithm, the DPD model structure can be adaptively adjusted during its real-time deployment to keep the optimum size and complexity under different operation conditions. Power consumption of DPD can be reduced by on-demand automatic model structure adaptation instead of reusing the same model structure for all power levels and band allocations. To realize dynamic model sizing, the adaptation algorithm explores new potential terms based on prior knowledge of the model structure and prunes the DPD model with a stepwise backward regression method. Experimental results show that the algorithm can quickly find the optimum model structure when the operation condition changes. During the adaptation, it can also maintain robust linearization performance with a relatively low computational complexity and thus demonstrates itself as a suitable solution to the linearization of future broadband wireless systems.
Sponsorship
Science Foundation Ireland
Type of Material
Journal Article
Publisher
IEEE
Journal
IEEE Transactions on Microwave Theory and Techniques
Volume
68
Issue
7
Start Page
2891
End Page
2901
Copyright (Published Version)
2020 IEEE
Language
English
Status of Item
Peer reviewed
ISSN
0018-9480
This item is made available under a Creative Commons License
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