Automated Deep Neural Learning-Based Optimization for High Performance High Power Amplifier Designs

Lida Kouhalvandi*, Osman Ceylan, Serdar Ozoguz

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37 Atıf (Scopus)


This study presents an automated optimization-oriented strategy for designing high power amplifiers (HPAs) using deep neural networks (DNNs). The proposed strategy consists of two optimization phases that are applied sequentially. In the first phase, the circuit topology is optimized by determining the number of passive components in the input and output matching networks using deep learning classification network. In the second optimization phase, component values are estimated using a deep learning regression network with electromagnetic-based Thompson Sampling Efficient Multiobjective Optimization (TSEMO). The proposed approach is compact, in the sense that the optimum solution is automatically generated by the process, opposite to the conventional approaches where manual post-processing is required to prune the process outcomes. It addresses the problem of heavy reliance of the system performance on the designer's experience and automatically generates valid layouts. In the demanding HPA design problem, uses of DNNs have been shown to provide much more accuracy than conventional shallow neural networks. The effectiveness of the proposed method is verified by implementing two designed HPAs, including GaN HEMTs. The efficiency-oriented optimized amplifier reveals higher than 60% drain efficiency, and the gain-oriented optimized amplifier has 17.6-18 dB linear gain in the frequency band of 1.8-2.2 GHz.

Orijinal dilİngilizce
Makale numarası9144294
Sayfa (başlangıç-bitiş)4420-4433
Sayfa sayısı14
DergiIEEE Transactions on Circuits and Systems I: Regular Papers
Basın numarası12
Yayın durumuYayınlandı - Ara 2020

Bibliyografik not

Publisher Copyright:
© 2004-2012 IEEE.


Manuscript received January 7, 2020; revised June 19, 2020; accepted July 7, 2020. Date of publication July 20, 2020; date of current version December 1, 2020. This work was supported by the Istanbul Technical University the Scientific Research Projects Unit under Grant MDK-2019-41968. This article was recommended by Associate Editor L. Hernandez. (Corresponding author: Lida Kouhalvandi).

FinansörlerFinansör numarası
Firat University Scientific Research Projects Management UnitMDK-2019-41968

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