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Progress In Electromagnetics Research C
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A MODFIED REAL-VALUED FEED-FORWARD NEURAL NETWORK LOW-PASS EQUIVALENT BEHAVIORAL MODEL FOR RF POWER AMPLFIERS

By L. B. Chipansky Freire, C. De Franca, and E. G. de Lima

Full Article PDF (291 KB)

Abstract:
This work addresses the low-pass equivalent behavioral modeling of radio frequency (RF) power amplifiers (PAs) for modern wireless communication systems. Similar to a previous approach, here the PA behavioral modeling is based on two independent real-valued feed-forward artificial neural networks (ANNs). A careful analysis is first presented to show that the nonlinear training algorithm for the previous ANN-based approach can be easily trapped into local minima, especially for the ANN that estimates the polar angle component of a complex-valued signal. Then, a modified ANNbased model is proposed to eliminate the local minimum problem, in this way significantly improving the modeling accuracy. Indeed, in the proposed model the two real-valued ANNs are responsible for estimating the in-phase and quadrature components of a complex-valued base-band signal. When applied to the behavioral modeling of a GaN HEMT class AB PA, the proposed ANN-based model reduces normalized mean-square error (NMSE) by up to 2.2 dB, in comparison with the previous ANN-based model having an equal number of network parameters.

Citation:
L. B. Chipansky Freire, C. De Franca, and E. G. de Lima, "A Modfied Real-Valued Feed-Forward Neural Network Low-Pass Equivalent Behavioral Model for RF Power Amplfiers," Progress In Electromagnetics Research C, Vol. 57, 43-52, 2015.
doi:10.2528/PIERC15022802
http://www.jpier.org/pierc/pier.php?paper=15022802

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