Novel learning-based spatial reuse optimization in dense WLAN deployments

被引:0
作者
Imad Jamil
Laurent Cariou
Jean-François Hélard
机构
[1] Orange,
[2] Intel,undefined
[3] Institute of Electronics and Telecommunications of Rennes (IETR)—Institut National des Sciences Appliquées (INSA) de Rennes,undefined
来源
EURASIP Journal on Wireless Communications and Networking | / 2016卷
关键词
IEEE 802.11; WLAN; High density; High efficiency WLAN (HEW); MAC; Spatial reuse; Artificial neural networks;
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学科分类号
摘要
To satisfy the increasing demand for wireless systems capacity, the industry is dramatically increasing the density of the deployed networks. Like other wireless technologies, Wi-Fi is following this trend, particularly because of its increasing popularity. In parallel, Wi-Fi is being deployed for new use cases that are atypically far from the context of its first introduction as an Ethernet network replacement. In fact, the conventional operation of Wi-Fi networks is not likely to be ready for these super dense environments and new challenging scenarios. For that reason, the high efficiency wireless local area network (HEW) study group (SG) was formed in May 2013 within the IEEE 802.11 working group (WG). The intents are to improve the “real world” Wi-Fi performance especially in dense deployments.
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