Deep Reinforcement Learning for Optimizing Restricted Access Window in IEEE 802.11ah MAC Layer
Deep Reinforcement Learning for Optimizing Restricted Access Window in IEEE 802.11ah MAC Layer
About this item
Full title
Author / Creator
Jiang, Xiaojun , Gong, Shimin , Deng, Chengyi , Li, Lanhua and Gu, Bo
Publisher
Switzerland: MDPI AG
Journal title
Language
English
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Publication information
Publisher
Switzerland: MDPI AG
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Scope and Contents
Contents
The IEEE 802.11ah standard is introduced to address the growing scale of internet of things (IoT) applications. To reduce contention and enhance energy efficiency in the system, the restricted access window (RAW) mechanism is introduced in the medium access control (MAC) layer to manage the significant number of stations accessing the network. Howe...
Alternative Titles
Full title
Deep Reinforcement Learning for Optimizing Restricted Access Window in IEEE 802.11ah MAC Layer
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Author / Creator
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Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_183386c8356442ab8c0e195b25ff9c95
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_183386c8356442ab8c0e195b25ff9c95
Other Identifiers
ISSN
1424-8220
E-ISSN
1424-8220
DOI
10.3390/s24103031
