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Found 7 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.
Artificial Intelligence in Vehicular Wireless Networks: A Case Study Using ns-3
Artificial intelligence (AI) techniques have emerged as a powerful approach to make wireless networks more efficient and adaptable.
A Reinforcement Learning Framework for PQoS in a Teleoperated Driving Scenario
In recent years, autonomous networks have been designed with Predictive Quality of Service (PQoS) in mind, as a means for applications operating in the industrial and/or automotive sectors to predict unanticipated Quality of Service (QoS) changes and react accordingly.
Predictive Quality of Service (PQoS): The Next Frontier for Fully Autonomous Systems
Recent advances in software, hardware, computing and control have fueled significant progress in the field of autonomous systems.
A Tutorial on 5G NR V2X Communications
The Third Generation Partnership Project (3GPP) has recently published its Release 16 that includes the first Vehicle to-Everything (V2X) standard based on the 5G New Radio (NR) air interface.
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.
