48 citations · 83 across the 8 of their papers we have counts for
10 papers
Deep Reinforcement Learning for Uplink Scheduling in NOMA-URLLC Networks
Benoît-Marie Robaglia, Marceau Coupechoux, Dimitrios Tsilimantos
This article addresses the problem of Ultra Reliable Low Latency Communications (URLLC) in wireless networks, a framework with particularly stringent constraints imposed by many In…
Distributed no-regret edge resource allocation with limited communication
Saad Kriouile, Dimitrios Tsilimantos, Theodoros Giannakas
To accommodate low latency and computation-intensive services, such as the Internet-of-Things (IoT), 5G networks are expected to have cloud and edge computing capabilities. To this…
Multi-Agent Deep Stochastic Policy Gradient for Event Based Dynamic Spectrum Access
Rahif Kassab, Apostolos Destounis, Dimitrios Tsilimantos +1
We consider the dynamic spectrum access (DSA) problem where Internet of Things (IoT) devices compete for time slots constituting a frame. Devices collectively monitor e…
Optimizing Adaptive Video Streaming in Mobile Networks via Online Learning
Theodoros Karagkioules, Georgios S. Paschos, Nikolaos Liakopoulos +3
In this paper, we propose a novel algorithm for video rate adaptation in HTTP Adaptive Streaming (HAS), based on online learning. The proposed algorithm, named Learn2Adapt (L2A), i…
Learn2MAC: Online Learning Multiple Access for URLLC Applications
Apostolos Destounis, Dimitrios Tsilimantos, Mérouane Debbah +1
This paper addresses a fundamental limitation of previous random access protocols, their lack of latency performance guarantees. We consider IoT transmitters competing for upli…
Classifying flows and buffer state for YouTube's HTTP adaptive streaming service in mobile networks
Dimitrios Tsilimantos, Theodoros Karagkioules, Stefan Valentin
Accurate cross-layer information is very useful to optimize mobile networks for specific applications. However, providing application-layer information to lower protocol layers has…