arXiv AI

Deep Learning-Driven Inverse Design of Doherty Power Amplifiers Using Pixelated Combiners and Dual-State Impedance Synthesis

arXiv:2606. 18395v1 Announce Type: cross Abstract: The output combiner of a Doherty power amplifier (PA) integrates load modulation, impedance matching, and phase compensation within a single network, making its design and synthesis highly challenging.

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 AI
Jun 18

Deep-Learning-Based Pixelated Microwave Filter Design and Characterization using Electro-Optical Electric-Field Measurements

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 Machine Learning
Aug 11

Transfer Learning-Enabled Distortion Compensation for Amplitude-Phase-Time Block Modulation-Based Nonlinear Single-Carrier Wireless Communications

arXiv:2608. 08554v1 Announce Type: cross Abstract: Power amplifier (PA) nonlinearity and memory effects significantly limit the spectral compliance, reliability, and energy efficiency of communication systems.

By Guoxing Duan, Min Fan, Cheng Yi, Bensheng Yang, Wei Xu, Haiming Wang, Xiaohu You