Inhalt des Dokuments
Matthias Mehlhose, M.Sc.
- © Fraunhofer HHI
Matthias Mehlhose received his diploma degree from university of applied sciences (FHTW Berlin, now HTW) in 2008 and his master of science in 2012 from Technische Universität Berlin. He started his scientific career at Fraunhofer Heinrich Hertz Institute in 2007 with an internship. After he worked as a Research Assistant and Research Associate he is now working toward his Ph.D..
His current research interests focused on modern wireless communication system like LTE (4G) and NR (5G) on the physical layer signal processing. Especially using software-defined radio with massive MIMO antennas on different radio bands to examine beamforming, user localization algorithms and channel fingerprints.
- digitalTWIN 
- One5G 
- MIDRAS 
- ResearchGate 
- Google Scholar 
- LinkedIn 
Conference, Symposium, and Workshop Papers
|Author||M. Mehlhose, D. A. Awan, R. L.G. Cavalcante, M. Kurras and S. Stanczak|
|Journal||accepted, IEEE International Conference on Communications (ICC), Dublin, Ireland, 2020|
|Abstract||Conventional multiuser detection techniques either require a large number of antennas at the receiver for a desired performance, or they are too complex for practical implementation. Moreover, many of these techniques, such as successive interference cancellation (SIC), suffer from errors in parameter estimation (user channels, covariance matrix, noise variance, etc.) that is performed before detection of user data symbols. As an alternative to conventional methods, this paper proposes and demonstrates a low-complexity practical Machine Learning (ML) based receiver that achieves similar (and at times better) performance to the SIC receiver. The proposed receiver does not require parameter estimation; instead it uses supervised learning to detect the user modulation symbols directly. We perform comparisons with minimum mean square error (MMSE) and SIC receivers in terms of symbol error rate (SER) and complexity.|
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