PhD Student Yi Zhu Published in IEEE JSTEAP

Excited to share that our article by our industrial PhD student Yi Zhu, “Deep-Learning Based Hardware-Efficient Load Identification for Power Amplifiers,” has been published in the IEEE Journal of Selected Topics in Electromagnetics, Antennas and Propagation (JSTEAP).

In this work, we explore how deep learning can be used to estimate the load impedance of power amplifiers directly from existing PA I/Q data, without requiring additional impedance-sensing hardware. This provides a hardware-efficient path toward load-aware modeling, adaptive DPD, and more robust RF transmitters for massive-MIMO base-station applications.

This paper is also a great outcome of our enjoyable collaboration with Ampleon. Many thanks to John Gajadharsing, Fred van Rijs, and Leo de Vreede for their strong support throughout the project. We also gratefully acknowledge the support from the Future Network Services (FNS) project under the Dutch National Growth Fund.

Looking forward to more exciting joint work on AI-enabled and adaptive RF transmitters!