3 citations · 4 across the 3 of their papers we have counts for
6 papers
Deadline-Aware Multi-Agent Reinforcement Learning for TSN-Based Vehicular Edge Networks
Bernardo A. C. Pereira, Marcos Carvalho, Fatih Temiz +5
Vehicular edge computing (VEC) enables latency-sensitive applications by bringing computing and networking resources closer to vehicles. However, existing approaches often overlook…
HELENA for 5G NR LEO NTN Channel Estimation: A Comparative Evaluation
Miguel Camelo Botero, Nina Slamnik-Kriještorac, Johann Marquez-Barja
Deep Learning (DL)-based channel estimation has shown high accuracy and low latency in terrestrial 5G NR, but Low Earth Orbit (LEO) Non-Terrestrial Networks (NTNs) introduce Dopple…
LITE: Lightweight Channel Gain Estimation with Reduced X-Haul CSI Signaling in O-RAN
David Goez, Marco Piazzola, Giulia Costa +6
Cell-Free Massive Multiple-Input Multiple-Output (CF-MaMIMO) in Open Radio Access Network (O-RAN) promises high spectral efficiency but is limited by frequent Channel State Informa…
HELENA: High-Efficiency Learning-based channel Estimation using dual Neural Attention
Miguel Camelo Botero, Esra Aycan Beyazit, Nina Slamnik-Kriještorac +1
Accurate channel estimation is critical for high-performance Orthogonal Frequency-Division Multiplexing systems such as 5G New Radio, particularly under low signal-to-noise ratio a…
Decentralized Multi-Party Multi-Network AI for Global Deployment of 6G Wireless Systems
Merim Dzaferagic, Marco Ruffini, Nina Slamnik-Krijestorac +21
Multiple visions of 6G networks elicit Artificial Intelligence (AI) as a central, native element. When 6G systems are deployed at a large scale, end-to-end AI-based solutions will…
An optimized application-context relocation approach for Connected and Automated Mobility (CAM)
Nina Slamnik-Kriještorac, Steven Latré, Johann M. Marquez-Barja
In this paper, we study and present a management and orchestration framework for vehicular communications, which enables service continuity for the vehicle via an optimized applica…