University of Oulu

M. K. Abdel-Aziz, C. Perlecto, S. Samarakoon and M. Bennis, "V2V Cooperative Sensing using Reinforcement Learning with Action Branching," ICC 2021 - IEEE International Conference on Communications, 2021, pp. 1-6, doi: 10.1109/ICC42927.2021.9500832

V2V cooperative sensing using reinforcement learning with action branching

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Author: Abdel-Aziz, Mohamed K.1; Perfecto, Cristina2; Samarakoon, Sumudu1;
Organizations: 1Centre for Wireless Communications, University of Oulu, Finland
2University of the Basque Country UPV/EHU, Spain
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 1.2 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2021
Publish Date: 2021-08-27


Cooperative perception plays a vital role in extending a vehicle’s sensing range beyond its line-of-sight. However, exchanging raw sensory data under limited communication resources is infeasible. Towards enabling an efficient cooperative perception, vehicles need to address fundamental questions such as: what sensory data needs to be shared? at which resolution? with which vehicles? In this view, this paper proposes a reinforcement learning (RL)-based vehicular association, resource block (RB) allocation, and content selection of cooperative perception messages by utilizing a quadtree-based point cloud compression mechanism. Simulation results show the ability of the RL agents to efficiently learn the vehicles’ association, RB allocation and message content selection that maximizes the fulfillment of the vehicles in terms of the received sensory information.

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Series: IEEE International Conference on Communications
ISSN: 1550-3607
ISSN-E: 1938-1883
ISSN-L: 1550-3607
ISBN: 978-1-7281-7122-7
ISBN Print: 978-1-7281-7123-4
Pages: 1 - 5
DOI: 10.1109/ICC42927.2021.9500832
Host publication: ICC 2021 - IEEE International Conference on Communications
Conference: IEEE International Conference on Communications
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
213 Electronic, automation and communications engineering, electronics
Funding: This work was supported in part by the INFOTECH Project NOOR, in part by the NEGEIN project, by the EU-CHISTERA projects LeadingEdge and CONNECT, the EU-H2020 project IntellIoT under grant agreement No. 957218, and the Academy of Finland projects MISSION and SMARTER.
EU Grant Number: (957218) IntellIoT - Intelligent, distributed, human-centered and trustworthy IoT environments
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