Deep Neural Network Based Digital Predistorter of Power Amplifiers

Funda Daylak, Ece Olcay Gunes, Oguz Bayat, Serdar Ozoguz

Araştırma sonucu: ???type-name???Konferans katkısıbilirkişi

1 Atıf (Scopus)

Özet

We show how to address nonlinearities in power amplifiers (PAs), which limit the power efficiency of mobile devices, increase the error vector magnitude, using an deep neural-network (DNN) method. DPD is frequently performed using polynomial-based algorithms that employ an indirect-learning architecture (ILA), which can be computationally complex, particularly on mobile devices, and highly sensitive to noise. By first training a DNN to model the PA and then training a predistorter using PA data through the PA DNN model. The DNN DPD successfully learns the unique PA distortions that a polynomial-based model may struggle to fit, and therefore may provide a nice balance between computation cost and DPD efficiency. We use two different DNN models to show the performance of our DNN approach and examine the complexity tradeoffs.

Orijinal dilİngilizce
Ana bilgisayar yayını başlığı2021 13th International Conference on Electrical and Electronics Engineering, ELECO 2021
YayınlayanInstitute of Electrical and Electronics Engineers Inc.
Sayfalar408-410
Sayfa sayısı3
ISBN (Elektronik)9786050114379
DOI'lar
Yayın durumuYayınlandı - 2021
Etkinlik13th International Conference on Electrical and Electronics Engineering, ELECO 2021 - Virtual, Bursa, Turkey
Süre: 25 Kas 202127 Kas 2021

Yayın serisi

Adı2021 13th International Conference on Electrical and Electronics Engineering, ELECO 2021

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???event.eventtypes.event.conference???13th International Conference on Electrical and Electronics Engineering, ELECO 2021
Ülke/BölgeTurkey
ŞehirVirtual, Bursa
Periyot25/11/2127/11/21

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Publisher Copyright:
© 2021 Chamber of Turkish Electrical Engineers.

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