Reinhard's research focuses on signal processing and machine learning (ML) methods for wireless communication systems. In his recent publications, he has demonstrated significant performance gains through ML-assisted multi-antenna receivers leveraging a paradigm known as deep unfolding. While so far his work has been validated using NVIDIA Sionna, Reinhard is currently working towards a practical implementation on a real-world 5G system leveraging the NVIDIA Aerial Research Cloud. His goal is to implement the first ML-assisted wireless receiver on a real-time 5G system, enabling him to verify and optimize the performance and complexity of future 5G and 6G signal processing algorithms to the specifics of a real-world communication system.
Reinhard is a doctoral student with the Integrated Information Processing Group at ETH Zurich. He received the B.Sc. and M.Sc. degrees, both with high distinction, in Electrical Engineering and Information Technology from the Technical University of Munich in 2019 and 2022, respectively. Throughout his studies, Reinhard was on exchange at the National University of Singapore and did his master thesis at Northwestern University, IL, USA. He was the recipient of scholarships from the Max Weber-Program, as well as from the German Academic Scholarship Foundation.