On-Demand Real-Time Optimizable Dynamic Model Sizing for Digital Predistortion of Broadband RF Power Amplifiers
|Title:||On-Demand Real-Time Optimizable Dynamic Model Sizing for Digital Predistortion of Broadband RF Power Amplifiers||Authors:||Li, Yue; Zhu, Anding||Permanent link:||http://hdl.handle.net/10197/12020||Date:||Jul-2020||Online since:||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.||Funding Details:||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||Keywords:||Adaptation models; Heuristic algorithms; Power demand; Computational modeling; Real-time systems; Complexity theory; Matching pursuit algorithms; Behavioral modeling; Digital predistortion (DPD); Linearization; Power amplifiers (PAs); Pruning; Volterra series||DOI:||10.1109/TMTT.2020.2982165||Language:||en||Status of Item:||Peer reviewed||ISSN:||0018-9480||This item is made available under a Creative Commons License:||https://creativecommons.org/licenses/by-nc-nd/3.0/ie/|
|Appears in Collections:||Electrical and Electronic Engineering Research Collection|
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