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Power amplifier behavioral model adaptive pruning using conjugate gradient‐based greedy algorithm
Author(s) -
Yao Yao,
Li Mingyu,
Zhang Zhongming,
Li Ruoyu,
He Songbai,
Nakatake Shigetoshi
Publication year - 2017
Publication title -
ieej transactions on electrical and electronic engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.254
H-Index - 30
eISSN - 1931-4981
pISSN - 1931-4973
DOI - 10.1002/tee.22432
Subject(s) - greedy algorithm , pruning , subspace topology , conjugate gradient method , convergence (economics) , computer science , algorithm , mathematical optimization , scheme (mathematics) , power (physics) , amplifier , greedy randomized adaptive search procedure , control theory (sociology) , mathematics , artificial intelligence , mathematical analysis , physics , quantum mechanics , agronomy , economics , biology , economic growth , computer network , control (management) , bandwidth (computing)
Starting from the greedy theory, this paper presents a novel adaptive greedy scheme for the power amplifier (PA) behavioral model adaptive pruning. The proposed scheme incorporates the stochastic conjugate gradient (SCG) principle into the subspace pursuit (SP) greedy algorithm, which can considerably offer improved tracking capabilities and faster convergence compared to other anterior adaptive greedy algorithms. Compared to conventional nonsparse methods, simulation results show that the proposed scheme can efficiently reduce the model order and computational complexity but almost have the comparable model performance with the full model. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.