Jakob Richard Hoydis

Title: Innovations of Jakob Hoydis in Graph Neural Networks

Introduction

Jakob Hoydis is a prominent inventor based in Munich, Germany. He has made significant contributions to the field of telecommunications, particularly in the area of graph neural networks. His innovative work has led to the development of advanced decoding techniques that enhance communication systems.

Latest Patents

Jakob Hoydis holds a patent for a "Graph neural network for channel decoding." This patent encompasses various embodiments and implementations of graph-neural-network (GNN)-based decoding applications. The GNN-based decoding schemes are broadly applicable to different coding schemes and are capable of operating on both binary and non-binary codewords in various implementations. Notably, the GNN-based decoding is scalable, even with arbitrary block lengths, and is not subject to typical limits regarding dimensionality. The decoding performance of these inventive GNN-based techniques matches or outpaces BCH and LDPC (both regular and 5G NR) decoding algorithms, while also demonstrating improvements in the number of iterations required and scalability.

Career Highlights

Jakob Hoydis is currently associated with Nvidia Corporation, where he continues to push the boundaries of innovation in his field. His work has been instrumental in advancing the capabilities of decoding algorithms, making significant strides in telecommunications technology.

Collaborations

Jakob has collaborated with notable colleagues such as Sebastian Cammerer and Faycal Ait Aoudia. Their combined expertise has contributed to the successful development and implementation of cutting-edge technologies in the industry.

Conclusion

Jakob Hoydis is a key figure in the realm of graph neural networks, with a focus on enhancing channel decoding techniques. His contributions are paving the way for more efficient communication systems, showcasing the importance of innovation in technology.