Rodrigo Hernangómez

PhD student in Electrical Engineering. Fascinated by the interplay between beauty and math.

My research interests include machine and deep learning, Bayesian statistics, information theory and signal processing, specially in application to the fields of radar and wireless communication.

Ever since I started my PhD, I have been lucky enough to join forces with some leading companies in the technology and telecommunication sectors, such as Ericsson or Bosch, in order to carry out my research. From 2018 to 2021, I collaborated closely with Infineon Technologies AG in their radar sensors for IoT, which lie at the core of Google’s Project Soli.

Projects

As a research associate at the Fraunhofer Heinrich Hertz Institute, I am involved in several public and private research projects, including the following ones:

  • 6G-ICAS4Mobility — Integrated Communication and Sensing for 6G Mobility.
  • 6G-RIC — Germany-wide Research and Innovation Cluster for 6G mobile technologies.
  • AI4Mobile — AI-aided Wireless Systems for Mobility in Industry and Traffic.
  • OrtoFern3D — Precise 3D-Localization System for Wireless Controllers in District Heating Networks.

Publications

  • R. Hernangomez, J. Fink, R. L. G. Cavalcante, Z. Utkovski, and S. Stanczak, “Optimized Detection with Analog Beamforming for Monostatic Integrated Sensing and Communication,” to be presented at the 2024 IEEE International Conference on Communications (ICC), Denver, CO, Jun. 2024
  • P. Rosemann, S. Partani, M. Miranda, J. Mahn, M. Karrenbauer, W. Meli, R. Hernangomez, M. Lubke, J. Kochems, S. Kopsell, A. Aziz-Koch, R. Askar, J. Beuster, O. Blume, N. Franchi, R. Thoma, S. Stanczak, and H. D. Schotten, “Enabling Mobility-Oriented JCAS in 6G Networks: An Architecture Proposal,” to be presented at the 2024 IEEE 4th International Symposium on Joint Communications & Sensing (JC&S), Leuven, Belgium, Mar. 2024. Preprint available: arXiv.2311.11623.
  • R. Hernangómez, A. Palaios, C. Watermann, D. Schäufele, P. Geuer, R. Ismayilov, M. Parvini, A. Krause, M. Kasparick, T. Neugebauer, O. D. Ramos-Cantor, H. Tchouankem, J. L. Calvo, B. Chen, G. Fettweis, and S. Stańczak, “Towards an AI-enabled Connected Industry: AGV Communication and Sensor Measurement Datasets,” To appear in IEEE Communications Magazine, 2024, Preprint available: arXiv.2301.03364.
  • R. Hernangómez, P. Geuer, A. Palaios, D. Schäufele, C. Watermann, K. Taleb-Bouhemadi, M. Parvini, A. Krause, S. Partani, C. Vielhaus, M. Kasparick, D. F. Külzer, F. Burmeister, F. H. P. Fitzek, H. D. Schotten, G. Fettweis, and S. Stańczak, “Berlin V2X: A Machine Learning Dataset from Multiple Vehicles and Radio Access Technologies,” in 2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring), Florence, Italy, Jun. 2023, pp. 1–5. doi: 10.1109/VTC2023-Spring57618.2023.10200750.
  • S. K. Dehkordi, J. C. Hauffen, P. Jung, R. Hernangomez, G. Caire, and S. Stanczak, “Multi-Scatter-Point Target Estimation for Sensing-Assisted OTFS Automotive Communication,” in WSA & SCC 2023; 26th International ITG Workshop on Smart Antennas and 13th Conference on Systems, Communications, and Coding, Braunschweig, Germany, Feb. 2023, pp. 1–6. ISBN 978-3-8007-6050-3.
  • A. Palaios, C. L. Vielhaus, D. F. Külzer, C. Watermann, R. Hernangómez, S. Partani, P. Geuer, A. Krause, R. Sattiraju, M. Kasparick, G. Fettweis, F. H. P. Fitzek, H. D. Schotten, and S. Stańczak, “Machine Learning for QoS Prediction in Vehicular Communication: Challenges and Solution Approaches,” IEEE Access, vol. 11, pp. 92459-92477, 2023, doi: 10.1109/ACCESS.2023.3303528.
  • R. Hernangómez, A. Palaios, G. Guruvayoorappan, M. Kasparick, N. U. Ain, and S. Stańczak, “Online QoS estimation for vehicular radio environments,” in 2022 30th European Signal Processing Conference (EUSIPCO), Belgrade, Serbia, Aug. 2022, pp. 1701–1705. doi: 10.23919/EUSIPCO55093.2022.9909612.
  • R. Hernangómez, I. Bjelaković, L. Servadei, and S. Stańczak, “Unsupervised Domain Adaptation across FMCW Radar Configurations Using Margin Disparity Discrepancy,” in 2022 30th European Signal Processing Conference (EUSIPCO), Belgrade, Serbia, Aug. 2022, pp. 1566–1570. doi: 10.23919/EUSIPCO55093.2022.9909618.
  • R. Hernangómez, T. Visentin, L. Servadei, H. Khodabakhshandeh, and S. Stańczak, “Improving Radar Human Activity Classification Using Synthetic Data with Image Transformation,” Sensors, vol. 22, no. 4, Art. no. 4, Jan. 2022, doi: 10.3390/s22041519.
  • A. Palaios, P. Geuer, J. Fink, D. F. Külzer, F. Göttsch, M. Kasparick, D. Schäufele, R. Hernangómez, S. Partani, R. Sattiraju, A. Kumar, F. Burmeister, A. Weinand, C. L. Vielhaus, F. H. P. Fitzek, G. P. Fettweis, H. D. Schotten, and S. Stanczak, “Network under Control: Multi-Vehicle E2E Measurements for AI-based QoS Prediction,” in 2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Helsinki, Finland, Sep. 2021, pp. 1432–1438. doi: 10.1109/PIMRC50174.2021.9569490.
  • H. Khodabakhshandeh, T. Visentin, R. Hernangómez, and M. Pütz, “Domain Adaptation Across Configurations of FMCW Radar for Deep Learning Based Human Activity Classification,” in 2021 21st International Radar Symposium (IRS), Berlin, Germany, Jun. 2021, pp. 1–10. doi: 10.23919/IRS51887.2021.9466179.
  • R. Hernangómez, A. Santra, and S. Stańczak, “A Study on Feature Processing Schemes for Deep-Learning-Based Human Activity Classification Using Frequency-Modulated Continuous-Wave Radar,” IET Radar, Sonar & Navigation, Jun. 2020, doi: 10.1049/iet-rsn.2020.0131.
  • R. Hernangómez, A. Santra, and S. Stańczak, “Human Activity Classification with Frequency Modulated Continuous Wave Radar Using Deep Convolutional Neural Networks,” in 2019 International Radar Conference (RADAR), Sep. 2019, pp. 1–6, doi: 10.1109/RADAR41533.2019.171243.