Publication
Title
A neural-network-based MF-TDMA MAC scheduler for collaborative wireless networks
Author
Abstract
In the unlicensed spectrum, many wireless technologies (e.g Wi-Fi, Bluelooth) use the same spectrum for wireless transmission. This often results in cross-technology interference effects, which are hard to address. Without new methods to manage this shared spectrum, wireless communication is increasingly challenging as too many nodes attempt al accessing the same spectrum. Collaboration between different wireless networks that use the same spectrum will be required to handle this massive amount of devices. In this paper, we present two algorithms based on Neural Networks (NNs) to demonstrate that a function approximation can accurately predict free slots in a Multiple Frequencies Time Division Multiple Access (MF-TDMA) network. By observing the spectrum, we are able to do online learning and let the corresponding NN predict the behavior of the spectrum a second in advance using our approach. We are able to reduce the number of collisions by half if the nodes from other networks are sending data following a Poisson distribution. When the nodes or the other network follow a more periodic traffic pattern, a collision reduction of factor 15 could be achieved.
Language
English
Source (journal)
IEEE Wireless Communications and Networking Conference : WCNC. - Piscataway, NJ, 1999, currens
Source (book)
IEEE Wireless Communications and Networking Conference (WCNC), APR 15-18, 2018, Barcelona, SPAIN
Publication
New york : Ieee , 2018
ISBN
978-1-5386-1734-2
978-1-5386-1734-2
Volume/pages
(2018) , 6 p.
ISI
000435542400098
Full text (publisher's version - intranet only)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 02.08.2018
Last edited 11.09.2021
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