Olcay Güneş, EceOzoğuz, Serdar2022-03-222022-03-222021Daylak, F., Gunes, E. O., Bayat, O., & Ozoguz, S. (2021, November). Deep Neural Network Based Digital Predistorter of Power Amplifiers. In 2021 13th International Conference on Electrical and Electronics Engineering (ELECO) (408-410). IEEE.978-605011437-9https://hdl.handle.net/20.500.12939/2299We 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.eninfo:eu-repo/semantics/closedAccessDNNDPDPAConference Object4084102-s2.0-85125264304N/A