Behavioral modeling and digital predistortion of power amplifiers with memory using two hidden layers artificial neural networks

This paper presents a novel Two Hidden Layers Neural Networks (2HLANN) model for behavioral modeling and linearization of RF PAs. Starting with a feedback loop principle model of a PA, an appropriate structure is deduced. This structure was then optimized to form a 2HLANN based model capable of pred...

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Bibliographic Details
Published in2010 IEEE MTT-S International Microwave Symposium p. 1
Main Authors Mkadem, F., Ben Ayed, M., Boumaiza, S., Wood, J., Aaen, P.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2010
Subjects
Accuracy
Artificial neural networks
Bandwidth
Feedback loop
Power amplifiers
Power generation
Predictive models
Predistortion
Radio frequency
Radiofrequency amplifiers
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Summary:This paper presents a novel Two Hidden Layers Neural Networks (2HLANN) model for behavioral modeling and linearization of RF PAs. Starting with a feedback loop principle model of a PA, an appropriate structure is deduced. This structure was then optimized to form a 2HLANN based model capable of predicting the nonlinear behavior and the memory effects of PAs. The validation of the proposed model in mimicking the behavior of a DUT is carried out in terms of its accuracy in predicting the output spectrum, dynamic AM/AM and AM/PM and the NMSE. In addition, the 2HLANN model was used to linearize two 250 Watt PEP Doherty PAs (DPAs) driven with 20 MHz bandwidth signals. The linearization of these DPAs using the 2HLANN enabled attaining an output power of 46.8 dBm and an average efficiency of up to 47.5% coupled with an ACPR higher than 50 dBc. When compared to some published behavioral and DPD schemes, the 2HLANN model demonstrated an excellent modeling accuracy and linearization capability
ISBN:1424460565
9781424460564
ISSN:0149-645X
2576-7216
DOI:10.1109/MWSYM.2010.5514964