Cyprian Grassmann
Infineon Technologies
14 Papers
50 Citations
Cyprian Grassmann is an academic researcher from Infineon Technologies. The author has contributed to research in topics: Computer science & Software-defined radio. The author has an hindex of 3, co-authored 6 publications. Previous affiliations of Cyprian Grassmann include Intel Mobile Communications.
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Papers
Architecture and implementation of a Software-Defined Radio baseband processor
Ulrich Ramacher,Wolfgang Raab,Ulrich Hachmann,Dominik Langen,Jörg Berthold,Ronalf Kramer,Alexander Schackow,Cyprian Grassmann,Mirko Sauermann,P. Szreder,F. Capar,G. Obradovic,W. Xu,N. Bruls,Kang Lee,Eugene Weber,Ray Kuhn,John Harrington +17 more
- 15 May 2011
TL;DR: The architecture and implementation of a Software Defined Radio (SDR) multi-standard baseband processor are presented and the first representative of a new SDR baseband family, the X-GOLDTM SDR20 has been successfully designed and fabricated in a 65nm CMOS process.
25
A programmable platform for software-defined radio
H.-M. Bluethgen,Cyprian Grassmann,Wolfgang Raab,U. Ramacher +3 more
- 19 Nov 2003
TL;DR: For the development of an SDR platform architecture the primary design goal is to find the most flexible and easy-to-program solution within a specified power budget for the baseband processing.
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Mapping the physical layer of radio standards to multiprocessor architectures
Cyprian Grassmann,Mathias Richter,Mirko Sauermann +2 more
- 16 Apr 2007
TL;DR: This paper presents a way to safely move from a functional model to the assembly level in order to come to a tested multithreaded optimized implementation in manageable time of baseband processing for the physical layer of radio standards.
FMCW radar2radar Interference Detection with a Recurrent Neural Network
Julian Hille,Daniel Auge,Cyprian Grassmann,Alois Knoll +3 more
- 21 Mar 2022
TL;DR: A Neural Network-based outlier detection method is used to identify corrupted samples in the time domain signal after the ADC, which increases the Signal-to-Noise-Ratio ratio by up to 30 dB in the presence of interference and increases the overall system performance and reliability.
6
Resonate-and-Fire Neurons for Radar Interference Detection
Julian Hille,Daniel Auge,Cyprian Grassmann,Alois Knoll +3 more
- 27 Jul 2022
TL;DR: Inspired by the energy efficiency of Spiking Neural Networks, it is shown that Resonate-and-Fire neurons are able to encode the temporal radar signal into spikes and use a population of Leaky Integrate-and -Fire neurons to distinguish between the normality and patterns such as interference or saturation.
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