NDLI: A utility-based resource allocation scheme for IEEE 802.11 WLANs via a machine-learning approach

Content Provider	Springer Nature Link
Author	Wang, Chiapin Kuo, Wen Hsing
Copyright Year	2014
Abstract	The problem of allocating resources in IEEE 802.11 wireless local area networks (WLAN) is challenging due to limited bandwidth, time-varying channel conditions, and especially the distributed channel-access manner. In this paper we propose an intelligent resource allocation scheme that dynamically adjusts medium-access-control (MAC) parameters to tune channel-access opportunities and maximize the total utility. “Intelligent” refers to the capability of our approach to regulate each 802.11 node’s parameters automatically according to the changes of surrounding situations, e.g. channel conditions and number of nodes. Our intelligent allocation scheme uses neural networks to on-line learn the nonlinear function between the adopted MAC parameters and allocated throughput. Based on the learned knowledge, MAC parameters can therefore be dynamically adjusted toward the desired throughput allocation and consequently the maximal WLAN utility. Simulations results demonstrate the effectiveness of our allocation scheme in maximizing the system utility in a varying 802.11 WLAN environment.
Starting Page	1743
Ending Page	1758
Page Count	16
File Format	PDF
ISSN	10220038
Journal	Wireless Networks
Volume Number	20
Issue Number	7
e-ISSN	15728196
Language	English
Publisher	Springer US
Publisher Date	2014-03-01
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Resource allocation Utility 802.11 WLAN Neural networks Communications Engineering, Networks Computer Communication Networks Electrical Engineering Business Information Systems
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Information Systems Electrical and Electronic Engineering

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