Search Results for author:
Found 4 papers, 0 papers with code
Scheduling Out-of-Coverage Vehicular Communications Using Reinforcement Learning
To exploit the benefits of the centralized approach for enhancing the reliability of V2V communications on roads lacking cellular coverage, we propose VRLS (Vehicular Reinforcement Learning Scheduler), a centralized scheduler that proactively assigns resources for out-of-coverage V2V communications \textit{before} vehicles leave the cellular network coverage.
Generation of Realistic Cloud Access Times for Mobile Application Testing using Transfer Learning
The main challenge in the generation of realistic synthetic traces is the diversity of environments and the lack of wide scope of real traces to calibrate the generators.
VRLS: A Unified Reinforcement Learning Scheduler for Vehicle-to-Vehicle Communications
VRLS is a unified reinforcement learning (RL) solution, wherein the learning agent, the state representation, and the reward provided to the agent are applicable to different vehicular environments of interest (in terms of vehicular density, resource configuration, and wireless channel conditions).
Reinforcement Learning Scheduler for Vehicle-to-Vehicle Communications Outside Coverage
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 systems) or a distributed scheduler, where the resources are autonomously selected by the vehicles.
