University of Oulu

M. K. Abdel-Aziz, C. Perfecto, S. Samarakoon, M. Bennis and W. Saad, "Vehicular Cooperative Perception Through Action Branching and Federated Reinforcement Learning," in IEEE Transactions on Communications, vol. 70, no. 2, pp. 891-903, Feb. 2022, doi: 10.1109/TCOMM.2021.3126650

Vehicular cooperative perception through action branching and federated reinforcement learning

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Author: Abdel-Aziz, Mohamed K.1; Perfecto, Cristina2; Samarakoon, Sumudu1;
Organizations: 1Centre for Wireless Communications, University of Oulu, 90014 Oulu, Finland
2University of the Basque Country UPV/EHU, Spain
3Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA 24061, USA
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 1.4 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2022012811202
Language: English
Published: Institute of Electrical and Electronics Engineers, 2022
Publish Date: 2022-01-28
Description:

Abstract

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 the following fundamental question: What sensory data needs to be shared? at which resolution? and with which vehicles? To answer this question, in this paper, a novel framework is proposed to allow reinforcement learning (RL)-based vehicular association, resource block (RB) allocation, and content selection of cooperative perception messages (CPMs) by utilizing a quadtree-based point cloud compression mechanism. Furthermore, a federated RL approach is introduced in order to speed up the training process across vehicles. Simulation results show the ability of the RL agents to efficiently learn the vehicles’ association, RB allocation, and message content selection while maximizing vehicles’ satisfaction in terms of the received sensory information. The results also show that federated RL improves the training process, where better policies can be achieved within the same amount of time compared to the non-federated approach.

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Series: IEEE transactions on communications
ISSN: 0090-6778
ISSN-E: 1558-0857
ISSN-L: 0090-6778
Volume: 70
Issue: 2
Pages: 891 - 903
DOI: 10.1109/TCOMM.2021.3126650
OADOI: https://oadoi.org/10.1109/TCOMM.2021.3126650
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
213 Electronic, automation and communications engineering, electronics
Subjects:
Funding: This work was supported in part by the INFOTECH Project NOOR, in part by the NEGEIN project, in part by the EU-CHISTERA projects LeadingEdge and CONNECT, in part by the EU-H2020 project IntellIoT under grant agreement No. 957218, in part by the U.S. National Science Foundation under Grant CNS-1836802, and in part by 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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