Literature Database Entry

sahin2019hybrid

Taylan Şahin, Mate Boban, Ramin Khalili and Adam Wolisz, "A Hybrid Sensing and Reinforcement Learning Scheduler for Vehicle-to-Vehicle Communications," Proceedings of 53rd Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, November 2019.

Abstract

Vehicle-to-vehicle (V2V) communications performance depends significantly on the approach taken to schedule the radio resources. When the infrastructure is available, so far the best performing V2V scheduling algorithms are based on centralized approach. In case there is no infrastructure, sensing the resources in a distributed manner to determine whether a specific resource is free performs well. We propose a hybrid solution, where a centralized reinforcement learning (RL) algorithm provides a candidate subset of resources, whereas a distributed sensing mechanism, running on each vehicle, makes the final resource selection. We evaluate the performance of the proposed approach in an out-of-coverage setting and show that it outperforms the state-of-the-art algorithms in highly dynamic scenarios by using the best of both worlds: RL agent provides optimized long-term resource allocations, while the distributed sensing handles temporary and unforeseen network conditions that can not be predicted effectively.

Quick access

Original Version DOI (at publishers web site)
BibTeX BibTeX

Contact

Taylan Şahin
Mate Boban
Ramin Khalili
Adam Wolisz

BibTeX reference

@inproceedings{sahin2019hybrid,
    author = {{\c{S}}ahin, Taylan and Boban, Mate and Khalili, Ramin and Wolisz, Adam},
    doi = {10.1109/ieeeconf44664.2019.9048691},
    title = {{A Hybrid Sensing and Reinforcement Learning Scheduler for Vehicle-to-Vehicle Communications}},
    publisher = {IEEE},
    address = {Pacific Grove, CA},
    booktitle = {53rd Asilomar Conference on Signals, Systems, and Computers},
    month = {11},
    year = {2019},
   }
   
   

Copyright notice

Links to final or draft versions of papers are presented here to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted or distributed for commercial purposes without the explicit permission of the copyright holder.

The following applies to all papers listed above that have IEEE copyrights: Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

The following applies to all papers listed above that are in submission to IEEE conference/workshop proceedings or journals: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.

The following applies to all papers listed above that have ACM copyrights: ACM COPYRIGHT NOTICE. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Publications Dept., ACM, Inc., fax +1 (212) 869-0481, or permissions@acm.org.

The following applies to all SpringerLink papers listed above that have Springer Science+Business Media copyrights: The original publication is available at www.springerlink.com.

This page was automatically generated using BibDB and bib2web.

Last modified: 2024-04-25