arXiv:2606. 27002v1 Announce Type: cross Abstract: This paper presents a deep learning-assisted methodology for the inverse synthesis of a compact, wideband inverted Doherty power amplifier (PA).
By Han Zhou, Haojie Chang, David Widen, Christian Fager
arXiv:2603. 16565v2 Announce Type: replace-cross Abstract: This article presents a deep learning-driven inverse design methodology for Doherty power amplifiers (PA) with multi-port pixelated output combiner networks.
By Han Zhou, Haojie Chang, David Widen
arXiv:2507. 06849v3 Announce Type: replace-cross Abstract: Neural network (NN)-based Digital Predistortion (DPD) improves linearization for wideband radio frequency (RF) power amplifiers (PAs) but often increases the complexity of the digital back-end.
By Yizhuo Wu, Ang Li, Chang Gao
arXiv:2508. 16403v3 Announce Type: replace Abstract: Accurately predicting the performance of active radio frequency (RF) circuits is essential for modern wireless systems but remains challenging due to highly nonlinear behavior and the high computational cost of traditional simulation tools.
By Anahita Asadi, Leonid Popryho, Inna Partin-Vaisband
arXiv:2606. 18402v1 Announce Type: cross Abstract: Traditional microwave filter design typically relies on iterative parameter tuning and predefined topologies, which limits design space and increases development time.
By Han Zhou, Richard Bannister, Caspar Pierce, Haojie Chang, David Widen, Ludvig Fornstedt, Gabriel Melin, Alexander Bohlin, Pontus Lindeberg Fredriksson, Dilbagh Singh, Christian Fager, Koen Buisman
arXiv:2606. 01265v1 Announce Type: cross Abstract: This paper demonstrates the effectiveness of machine learning-driven optimization for designing application-specific GaN tri-gate FinFETs in vertical power delivery systems.
By Ayoub Sadeghi, Leonid Popryho, Inna Partin-Vaisband