Melina Geis, M.Sc.
Since August 2021
Part-Time Research Assistant, Communication Networks Institute, TU Dortmund
August 2019 - January 2020
Studies Abroad at Umeå University, Sweden
March 2019 - July 2021
Working Student, QASS GmbH
October 2018 - June 2021
Master, Electrical Engineering and Information Technology, TU Dortmund
July 2018 - September 2018
Internship, QASS GmbH
July 2017 - December 2017
Student Assistant, Communication Technology Institute, TU Dortmund
October 2015 - September 2018
Bachelor, Information and Communication Engineering, TU Dortmund
Publications
REMIBRANDT: Imputing Radio Environmental Maps for Safety-Critical Applications with Machine Learning
N. Altenburg, H. Schippers, M. Geis, C.Wietfeld
In IEEE Globecom Workshops (GC Wkshps), Cape Town, South Africa, Dec. 2024.
AI-driven Planning of Private Networks for Shared Operator Models
M. Geis, C. Bektas, S. Böcker, C. Wietfeld
In IEEE Symposium on Local and Metropolitan Area Networks (LANMAN), July 2024.
Automated Private 5G Network Planning for Professional Industries
M. Geis, C. Bektas, S. Böcker, C. Wietfeld
In IEEE Symposium on Local and Metropolitan Area Networks (LANMAN), July 2024.
AI-based Anomaly Detection for Industrial 5G Networks by Distributed SDR Measurements
K. Šabanović, C. Arendt, S. Fricke, M. Geis, S. Böcker, C. Wietfeld
In IEEE International Symposium on Measurements and Networking (M&N), July 2024.
Aerial-DRaGon: Machine Learning-based Channel Modeling for Airspace Communication Networks
M. Geis, T. Gebauer, H. Tuna, C. Wietfeld
In IEEE International Conference on Communications (ICC) Workshops, June 2024.
IndoorDRaGon: Data-Driven 3D Radio Propagation Modeling for Highly Dynamic 6G Environments
M. Geis, H. Schippers, M. Danger, C. Krieger, S. Böcker, J. Freytag, I. Priyanta, M. Roidl, C. Wietfeld
In European Wireless 2023, October 2023.
TinyDRaGon: Lightweight Radio Channel Estimation for 6G Pervasive Intelligence
M. Geis, B. Sliwa, C. Bektas, C. Wietfeld
In 2022 IEEE Future Networks World Forum (FNWF), October 2022.
DRaGon: Mining latent radio channel information from geographical data leveraging deep learning
B. Sliwa, M. Geis, C. Bektas, M. Lopéz, P. Mogensen, C. Wietfeld
In 2022 IEEE Wireless Communications and Networking Conference (WCNC), Austin, Texas