Modeling the Nonlinear Power Amplifier with Memory Using Complex-valued Radial Basis Function Networks
In This paper,we propose a novel complex-valued RBF networks approach for dynamic behavioral modeling of nonlinear power amplifier with memory. The complex Recursive Orthogonal Least Squares (ROLS) algorithm is applied to train complex Radial Basis Function (RBF) Network when modeling,so as to save large memory and computational efforts. Using the information available from the trained network with complex ROLS algorithm,the effective centers of the network can be obtained by adopting backward selection algorithm,which achieve acceptable accuracy with significant reduction of network structure. The model has been validated using measured and simulated data. The results of simulation and experimentation show thatThis complex RBF network model requires a significantly reduced complexity in the analysis and training procedures,when driven with WCDMA signals,than previously published neural-network-based PA models.
Mingyu Li Songbai He Xiaodong Li
College of Electronic engineering,University of Electronic Science and Technology of china,Chengdu,610054,China
国际会议
2008 International Conference on Microwave and Millimeter Wave Technology(2008国际微波毫米波技术会议)
南京
英文
100-103
2008-04-21（万方平台首次上网日期，不代表论文的发表时间）