An Efficient and General Automated Power Amplifier Design Method Based on Surrogate Model Assisted Hybrid Optimization Technique

Bo Liu*, Liyuan Xue, Haijun Fan, Yuan Ding, Muhammad A. Imran, Tao Wu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
17 Downloads (Pure)

Abstract

In layout-level optimization-oriented power amplifier (PA) design, the need for a good quality initial design and the high computational cost of electromagnetic (EM) simulations are remaining challenges. To address these challenges, a new method called efficient and general Bayesian neural network (BNN)-assisted hybrid optimization algorithm for PA design (E-GASPAD), is proposed. The key innovations of E-GASPAD include the introduction of BNN to model the PA design landscape and a new hybrid optimization algorithm co-working with BNN prediction for efficient PA design optimization. The performance of E-GASPAD is demonstrated by a 27–31 GHz class-AB PA and a 24–31 GHz wideband Doherty PA. Considering around 30 design variables with wide search ranges, the complete set of PA performance specifications, and full-wave EM simulations, layout-level high-performance designs are obtained automatically within a few hundred simulations (i.e., less than 72 h).
Original languageEnglish
Pages (from-to)1-12
Number of pages12
JournalIEEE Transactions on Microwave Theory and Techniques
Early online date31 Dec 2024
DOIs
Publication statusE-pub ahead of print - 31 Dec 2024

Keywords

  • Bayesian neural network (BNN)
  • Doherty power amplifier (PA)
  • evolutionary algorithm
  • optimization
  • PA
  • surrogate modeling
  • wideband

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