University of Oulu

M. K. Abdel-Aziz, S. Samarakoon, M. Bennis and W. Saad, "Ultra-Reliable and Low-Latency Vehicular Communication: An Active Learning Approach," in IEEE Communications Letters, vol. 24, no. 2, pp. 367-370, Feb. 2020. doi: 10.1109/LCOMM.2019.2956929

Ultra-reliable and low-latency vehicular communication : an active learning approach

Saved in:
Author: Abdel-Aziz, Mohamed K.1; Samarakoon, Sumudu1; Bennis, Mehdi1,2;
Organizations: 1Centre for Wireless Communications, University of Oulu, 90014 Oulu, Finland
2Department of Computer Engineering, Kyung Hee University, Yongin 446-701, South Korea
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, 0.8 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2019121146720
Language: English
Published: Institute of Electrical and Electronics Engineers, 2020
Publish Date: 2019-12-11
Description:
In this letter, an age of information (AoI)-aware transmission power and resource block (RB) allocation technique for vehicular communication networks is proposed. Due to the highly dynamic nature of vehicular networks, gaining a prior knowledge about the network dynamics, i.e., wireless channels and interference, in order to allocate resources, is challenging. Therefore, to effectively allocate power and RBs, the proposed approach allows the network to actively learn its dynamics by balancing a tradeoff between minimizing the probability that the vehicles’ AoI exceeds a predefined threshold and maximizing the knowledge about the network dynamics. In this regard, using a Gaussian process regression (GPR) approach, an online decentralized strategy is proposed to actively learn the network dynamics, estimate the vehicles’ future AoI, and proactively allocate resources. Simulation results show a significant improvement in terms of AoI violation probability, compared to several baselines, with a reduction of at least 50%.
see all

Series: IEEE communications letters
ISSN: 1089-7798
ISSN-E: 2373-7891
ISSN-L: 1089-7798
Volume: 24
Issue: 2
Pages: 1 - 4
DOI: 10.1109/LCOMM.2019.2956929
OADOI: https://oadoi.org/10.1109/LCOMM.2019.2956929
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Subjects:
V2X
Funding: This work was supported in part by the Academy of Finland project CARMA, and 6Genesis Flagship (grant no. 318927), in part by the INFOTECH project NOOR, in part by the Office of Naval Research (ONR) under MURI Grant N00014-19-1-2621, and in part by the Kvantum Institute strategic project SAFARI.
Copyright information: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.