activity
20222026
most citedBenchmarking Quantum Reinforcement Learning

3 citations · 7 across the 12 of their papers we have counts for

collaborators

12 papers

eess.SP2026

The S-ICDF Dataset: Sionna-Simulated Dynamic Interference Characterization and Direction Finding

Christian Wielenberg, Lucas Heublein, Jonathan Ott +8

Jamming and spoofing threaten wireless and satellite navigation by disrupting or manipulating radio frequency (RF) signals, undermining availability, integrity, and trust. Robust i…

eess.SP2026

Resilient Channel Charting Under Varying Radio Link Availability

Jonas Pirkl, Jonathan Ott, Maximilian Stahlke +3

Channel charting (CC) has become a key technology for RF-based localization, enabling unsupervised radio fingerprinting, even in non line of sight scenarios, with a minimum of refe…

cs.LG2025

Meta-Learning Multi-armed Bandits for Beam Tracking in 5G and 6G Networks

Alexander Mattick, George Yammine, Georgios Kontes +2

Beamforming-capable antenna arrays with many elements enable higher data rates in next generation 5G and 6G networks. In current practice, analog beamforming uses a codebook of pre…

eess.SP2025

Hardware Limitations and Optimization Approach in 1-Bit RIS Design at 28 GHz

Hossein Rezaei, Mehmet Emin Arslan, George Yammine +2

Reconfigurable intelligent surfaces (RIS) have emerged as a transformative technology for electromagnetic (EM) wave manipulation, offering unprecedented control over wave reflectio…

eess.SP2025

Passive Channel Charting: Locating Passive Targets using a UWB Mesh

Raffael Poeggel, Maximilian Stahlke, Jonas Pirkl +4

Fingerprint-based passive localization enables high localization accuracy using low-cost UWB IoT radio sensors. However, fingerprinting demands extensive effort for data acquisitio…

quant-ph2025★ 3 cited

Benchmarking Quantum Reinforcement Learning

Georg Kruse, Rodrigo Coelho, Andreas Rosskopf +2

Quantum Reinforcement Learning (QRL) has emerged as a promising research field, leveraging the principles of quantum mechanics to enhance the performance of reinforcement learning…

quant-ph2025★ 1 cited

Benchmarking Quantum Reinforcement Learning

Nico Meyer, Christian Ufrecht, George Yammine +3

Benchmarking and establishing proper statistical validation metrics for reinforcement learning (RL) remain ongoing challenges, where no consensus has been established yet. The emer…

eess.SP2023

Velocity-Based Channel Charting with Spatial Distribution Map Matching

Maximilian Stahlke, George Yammine, Tobias Feigl +2

Fingerprint-based localization improves the positioning performance in challenging, non-line-of-sight (NLoS) dominated indoor environments. However, fingerprinting models require a…

eess.SP2022

Indoor Localization with Robust Global Channel Charting: A Time-Distance-Based Approach

Maximilian Stahlke, George Yammine, Tobias Feigl +2

Fingerprinting-based positioning significantly improves the indoor localization performance in non-line-of-sight-dominated areas. However, its deployment and maintenance is cost-in…

eess.SY2022

Efficient Beam Search for Initial Access Using Collaborative Filtering

George Yammine, Georgios Kontes, Norbert Franke +2

Beamforming-capable antenna arrays overcome the high free-space path loss at higher carrier frequencies. However, the beams must be properly aligned to ensure that the highest powe…

eess.SP2022★ 1 cited

Towards Realistic Statistical Channel Models For Positioning: Evaluating the Impact of Early Clusters

Mohammad Alawieh, George Yammine, Ernst Eberlein +5

Physical effects such as reflection, refraction, and diffraction cause a radio signal to arrive from a transmitter to a receiver in multiple replicas that have different amplitude…

eess.SP2022★ 2 cited

Complementary Semi-Deterministic Clusters for Realistic Statistical Channel Models for Positioning

Mohammad Alawieh, Ernst Eberlein, Stephan Jäckel +5

Positioning benefits from channel models that capture geometric effects and, in particular, from the signal properties of the first arriving path and the spatial consistency of the…