Deep Reinforcement Learning-Empowered Resource Allocation for Mobile Edge Computing in Cellular V2X...
Deep Reinforcement Learning-Empowered Resource Allocation for Mobile Edge Computing in Cellular V2X Networks
About this item
Full title
Author / Creator
Li, Dongji , Xu, Shaoyi and Li, Pengyu
Publisher
Switzerland: MDPI AG
Journal title
Language
English
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Publication information
Publisher
Switzerland: MDPI AG
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More information
Scope and Contents
Contents
With the rapid development of vehicular networks, vehicle-to-everything (V2X) communications have huge number of tasks to be calculated, which brings challenges to the scarce network resources. Cloud servers can alleviate the terrible situation regarding the lack of computing abilities of vehicular user equipment (VUE), but the limited resources, t...
Alternative Titles
Full title
Deep Reinforcement Learning-Empowered Resource Allocation for Mobile Edge Computing in Cellular V2X Networks
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Author / Creator
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Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_42f5de56f22e4e4faa8241d327abab11
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_42f5de56f22e4e4faa8241d327abab11
Other Identifiers
ISSN
1424-8220
E-ISSN
1424-8220
DOI
10.3390/s21020372
