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20242026
most citedMulti-agent Reinforcement Learning-based In-place Scaling Engine for Edge-cloud Systems

4 citations · 5 across the 10 of their papers we have counts for

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eess.SP2025

Design Principles of Zero-Shot Self-Supervised Unknown Emitter Detectors

Mikhail Krasnov, Ljupcho Milosheski, Mihael Mohorčič +1

The proliferation of wireless devices necessitates more robust and reliable emitter detection and identification for critical tasks such as spectrum management and network security…

eess.SP2025

SABER: Symbolic Regression-based Angle of Arrival and Beam Pattern Estimator

Shih-Kai Chou, Mengran Zhao, Cheng-Nan Hu +3

Accurate Angle-of-arrival (AoA) estimation is essential for next-generation wireless communication systems to enable reliable beamforming, high-precision localization, and integrat…

cs.AI2025

Analysis of AI Techniques for Orchestrating Edge-Cloud Application Migration

Sadig Gojayev, Ahmad Anaqreh, Carolina Fortuna

Application migration in edge-cloud system enables high QoS and cost effective service delivery. However, automatically orchestrating such migration is typically solved with heuris…

cs.DC2025★ 4 cited

Multi-agent Reinforcement Learning-based In-place Scaling Engine for Edge-cloud Systems

Jovan Prodanov, Blaž Bertalanič, Carolina Fortuna +4

Modern edge-cloud systems face challenges in efficiently scaling resources to handle dynamic and unpredictable workloads. Traditional scaling approaches typically rely on static th…

cs.LG2025

Automated Modeling Method for Pathloss Model Discovery

Ahmad Anaqreh, Shih-Kai Chou, Blaž Bertalanič +3

Modeling propagation is the cornerstone for designing and optimizing next-generation wireless systems, with a particular emphasis on 5G and beyond era. Traditional modeling methods…