Power Amplifier Behavioral Model Adaptive Pruning Using Conjugate Gradient-Based Greedy Algorithm

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. (C) 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.