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sahin2018reinforcement


Taylan ┼×ahin, Ramin Khalili, Mate Boban and Adam Wolisz, "Reinforcement Learning Scheduler for Vehicle-to-Vehicle Communications Outside Coverage," Proceedings of 10th IEEE Vehicular Networking Conference (VNC 2018), Taipei, Taiwan, December 2018.

Abstract

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. The former approach yields a considerably higher resource utilization in case the network coverage is uninterrupted. However, in case of intermittent or-of-coverage, due to not having input from centralized scheduler, vehicles need to revert to distributed scheduling.Motivated by recent advances in reinforcement learning (RL), we investigate whether a centralized learning scheduler can be taught to efficiently pre-assign the resources to vehicles for-of-coverage V2V communication. Specifically, we use the actor-critic RL algorithm to train the centralized scheduler to provide non-interfering resources to vehicles before they enter the-of-coverage area.Our initial results show that a RL-based scheduler can achieve performance as good as or better than the state-of-art distributed scheduler, often outperforming it. Furthermore, the learning process completes within a reasonable time (ranging from a few hundred to a few thousand epochs), thus making the RL-based scheduler a promising solution for V2V communications with intermittent network coverage.

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Taylan ┼×ahin
Ramin Khalili
Mate Boban
Adam Wolisz

BibTeX reference

@inproceedings{sahin2018reinforcement,
    author = {{\c{S}}ahin, Taylan and Khalili, Ramin and Boban, Mate and Wolisz, Adam},
    doi = {10.1109/vnc.2018.8628366},
    title = {{Reinforcement Learning Scheduler for Vehicle-to-Vehicle Communications Outside Coverage}},
    publisher = {IEEE},
    issn = {2157-9865},
    isbn = {978-1-5386-9428-2},
    address = {Taipei, Taiwan},
    booktitle = {10th IEEE Vehicular Networking Conference (VNC 2018)},
    month = {12},
    year = {2018},
   }
   
   

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