Title :
RF Power Amplifier Behavioral Modeling using a Globally Recurrent Neural Network
Author :
O´Brien, Bill ; Dooley, John ; Brazil, Thomas J.
Author_Institution :
Sch. of Electr., Electron. & Mech. Eng., Univ. Coll., Dublin
Abstract :
In this paper it is shown that a globally recurrent time delay neural network can accurately model a nonlinear RF power amplifier having significant memory. The recognized difficulty of training a recurrent neural network is overcome by reducing it initially to a feed forward network, training that network, and then using the weights established by this training sequence in a restructured recurrent network. The training of the recurrent network thus reduces to the training of a feed forward network and a simple restructuring. The required maximum input delay is established by examination of the temporal profile of the energy contained in the amplifier impulse response. The model was successfully trained with an RF pass band time domain multi-sine signal and subsequently validated with another multi-sine signal composed of different sine components at different amplitudes. A second model trained with a wider bandwidth multi-sine was successfully validated with a W-CDMA signal
Keywords :
circuit analysis computing; delays; feedforward neural nets; nonlinear network analysis; power amplifiers; radiofrequency amplifiers; recurrent neural nets; transient response; RF pass band signal; W-CDMA signal; amplifier impulse response; behavioral modeling; feed forward network; multisine signal; nonlinear RF power amplifier; recurrent neural network; restructured recurrent network; time delay; time domain signal; Bandwidth; Delay effects; Feeds; Multiaccess communication; Neural networks; Power amplifiers; RF signals; Radio frequency; Radiofrequency amplifiers; Recurrent neural networks; Behavioral modeling; neural networks; power amplifiers; recurrent neural networks;
Conference_Titel :
Microwave Symposium Digest, 2006. IEEE MTT-S International
Conference_Location :
San Francisco, CA
Print_ISBN :
0-7803-9541-7
Electronic_ISBN :
0149-645X
DOI :
10.1109/MWSYM.2006.249952