Ecole d'ingénieur et centre de recherche en Sciences du numérique

Deep learning-aided application scheduler for vehicular safety communication

Khan, Mohammad Irfan; Aubet, François-Xavier; Pahl, Marc-Oliver; Härri, Jérôme

Submitted on ArXiV, 25 January 2019

802.11p based V2X communication uses stochastic medium access control, which cannot prevent broadcast packet collision, in particular during high channel load. Wireless congestion control has been designed to keep the channel load at an optimal point. However, vehicles' lack of precise and granular knowledge about true channel activity, in time and space, makes it impossible to fully avoid packet collisions. In this paper, we propose a machine learning approach using deep neural network for learning the vehicles' transmit patterns, and as such predicting future channel activity in space and time. We evaluate the performance of our proposal via simulation considering multiple safety-related V2X services involving heterogeneous transmit patterns. Our results show that predicting channel activity, and transmitting accordingly, reduces collisions and significantly improves communication performance.

Arxiv Bibtex

Titre:Deep learning-aided application scheduler for vehicular safety communication
Type:Conférence
Langue:English
Ville:
Date:
Département:Systèmes de Communication
Eurecom ref:6071
Copyright: © EURECOM. Personal use of this material is permitted. The definitive version of this paper was published in Submitted on ArXiV, 25 January 2019 and is available at :
Bibtex: @inproceedings{EURECOM+6071, year = {2019}, title = {{D}eep learning-aided application scheduler for vehicular safety communication}, author = {{K}han, {M}ohammad {I}rfan and {A}ubet, {F}ran{\'c}ois-{X}avier and {P}ahl, {M}arc-{O}liver and {H}{\"a}rri, {J}{\'e}r{\^o}me}, booktitle = {{S}ubmitted on {A}r{X}i{V}, 25 {J}anuary 2019 }, address = {}, month = {01}, url = {http://www.eurecom.fr/publication/6071} }
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