arXiv AI By Han Zhou, Haojie Chang, David Widen

Deep Learning-Driven Black-Box Doherty Power Amplifier with Pixelated Output Combiner and Extended Efficiency Range

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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.

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arXiv Machine Learning
Jul 16

RF-Informed Graph Neural Networks for Accurate and Data-Efficient Circuit Performance Prediction

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 Machine Learning
Sep 22

TARGet: Topology-Aware Fusion-based Radio Frequency Circuit Functional Modeling using Graph Neural Networks

TARGet is an open‑source, topology‑aware machine‑learning framework for modeling RF circuits. It uses S‑parameter representations of sub‑circuits and a fusion architecture that combines Graph Neural Networks with sub‑circuit connectivity‑aware networks, enabling learning across multiple topologies. Experiments show TARGet achieves sub‑1% prediction error, reduces training data needs by up to 35.5×, and improves accuracy by 9.7× over state‑of‑the‑art models, with zero‑shot transfer to unseen sub‑circuit topologies.

By Soroosh Noorzad, Sebastian Bodero, Morteza Fayazi