113 citations · 290 across the 22 of their papers we have counts for
6 papers · 1 filter
DISTINQT: A Distributed Privacy Aware Learning Framework for QoS Prediction for Future Mobile and Wireless Networks
Nikolaos Koursioumpas, Lina Magoula, Ioannis Stavrakakis +3
Beyond 5G and 6G networks are expected to support new and challenging use cases and applications that depend on a certain level of Quality of Service (QoS) to operate smoothly. Pre…
Scheduling Out-of-Coverage Vehicular Communications Using Reinforcement Learning
Taylan Şahin, Ramin Khalili, Mate Boban +1
Performance of vehicle-to-vehicle (V2V) communications depends highly on the employed scheduling approach. While centralized network schedulers offer high V2V communication reliabi…
VRLS: A Unified Reinforcement Learning Scheduler for Vehicle-to-Vehicle Communications
Taylan Şahin, Ramin Khalili, Mate Boban +1
Vehicle-to-vehicle (V2V) communications have distinct challenges that need to be taken into account when scheduling the radio resources. Although centralized schedulers (e.g., loca…
Reinforcement Learning Scheduler for Vehicle-to-Vehicle Communications Outside Coverage
Taylan Şahin, Ramin Khalili, Mate Boban +1
Radio resources in vehicle-to-vehicle (V2V) communication can be scheduled either by a centralized scheduler residing in the network (e.g., a base station in case of cellular syste…
MSPlayer: Multi-Source and multi-Path LeverAged YoutubER
Yung-Chih Chen, Don Towsley, Ramin Khalili
Online video streaming through mobile devices has become extremely popular nowadays. YouTube, for example, reported that the percentage of its traffic streaming to mobile devices h…
Multi-Source Multi-Path HTTP (mHTTP): A Proposal
Juhoon Kim, Ramin Khalili, Anja Feldmann +2
Today, most devices have multiple network interfaces. Coupled with wide-spread replication of popular content at multiple locations, this provides substantial path diversity in the…