Integrated CMOS Analog Neural Network Ability to Linearize the Distorted Characteristic of HPA Embedded in Satellites

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	Similar Articles Articles by Laurent Gatet Articles by H?l?ne Tap-B?teille Articles by Daniel Roviras Articles by Francis Gizard

4th IEEE International Symposium on Electronic Design, Test and Applications (delta 2008)

January 23-January 25

ISBN: 978-0-7695-3110-6

	ASCII Text	x
	Laurent Gatet, H?l?ne Tap-B?teille, Daniel Roviras, Francis Gizard, "Integrated CMOS Analog Neural Network Ability to Linearize the Distorted Characteristic of HPA Embedded in Satellites," Electronic Design, Test and Applications, IEEE International Workshop on, pp. 502-505, 4th IEEE International Symposium on Electronic Design, Test and Applications (delta 2008), 2008.

	BibTex	x
	@article{ 10.1109/DELTA.2008.49, author = {Laurent Gatet and H?l?ne Tap-B?teille and Daniel Roviras and Francis Gizard}, title = {Integrated CMOS Analog Neural Network Ability to Linearize the Distorted Characteristic of HPA Embedded in Satellites}, journal ={Electronic Design, Test and Applications, IEEE International Workshop on}, volume = {0}, year = {2008}, isbn = {978-0-7695-3110-6}, pages = {502-505}, doi = {http://doi.ieeecomputersociety.org/10.1109/DELTA.2008.49}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Electronic Design, Test and Applications, IEEE International Workshop on TI - Integrated CMOS Analog Neural Network Ability to Linearize the Distorted Characteristic of HPA Embedded in Satellites SN - 978-0-7695-3110-6 SP502 EP505 A1 - Laurent Gatet, A1 - H?l?ne Tap-B?teille, A1 - Daniel Roviras, A1 - Francis Gizard, PY - 2008 KW - Neural Network Architecture KW - CMOS Analog Integrated Circuits KW - Multi-Layer Perceptrons KW - Nonlinear Distortion KW - Predistorsion VL - 0 JA - Electronic Design, Test and Applications, IEEE International Workshop on ER -

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/DELTA.2008.49

New satellites generations have regenerative payload. Received signals are demodulated in order to be processed, before being modulated again andamplified on-board. For correcting non-linearities due to High Power Amplifier (HPA) operating near saturation, a predistortion system based on Neural Networks (NNs) was developed. For size, consumption and bandwidth purposes, the Multi-Layer Perceptron (MLP) type NNs were implemented in a 0.6 ?m CMOSASIC. This paper presents the linearization ability of the analog integrated NN to correct the AM/AM distortion due to the HPA for changing simulation conditions (temperature drifts, ageing variations).

Index Terms:

Neural Network Architecture, CMOS Analog Integrated Circuits, Multi-Layer Perceptrons, Nonlinear Distortion, Predistorsion

Citation:

Laurent Gatet, H?l?ne Tap-B?teille, Daniel Roviras, Francis Gizard, "Integrated CMOS Analog Neural Network Ability to Linearize the Distorted Characteristic of HPA Embedded in Satellites," delta, pp.502-505, 4th IEEE International Symposium on Electronic Design, Test and Applications (delta 2008), 2008

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