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

Deep learning-aided resource orchestration for vehicular safety communication

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

Wireless Days 2019, IEEE/IFIP Days 2019, 11th edition, 24-26 April 2019, Manchester, UK

IEEE 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 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.

Document Doi Arxiv Bibtex

Titre:Deep learning-aided resource orchestration for vehicular safety communication
Département:Systèmes de Communication
Eurecom ref:5796
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Bibtex: @inproceedings{EURECOM+5796, doi = {}, year = {2019}, title = {{D}eep learning-aided resource orchestration for vehicular safety communication}, author = {{I}rfan {K}han, {M}ohammad 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 = {{W}ireless {D}ays 2019, {IEEE}/{IFIP} {D}ays 2019, 11th edition, 24-26 {A}pril 2019, {M}anchester, {UK}}, address = {{M}anchester, {ROYAUME}-{UNI}}, month = {04}, url = {} }
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