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Title
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A machine learning approach for IEEE 802.11 channel allocation
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Author
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Abstract
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| Today's communication is mainly done over wireless networks, with IEEE 802.11 (Wi-Fi) at the forefront. There are billions of devices and millions of access points (APs), but only very few non-overlapping channels. As a result, the performance of Wi-Fi devices is severely degraded, because perfect channel allocation - with every AP alone in its channel - is close to impossible. Even in situations where all networks are under centralised control, existing approaches quickly tend to be either unscalable or suboptimal. By focusing on a subset of problems, identifying Wireless Local Area Networks (WLANs) that severely interfere with each other, performance can be improved even in such a complex situation. We tackle this problem through machine learning and coin it Bad Neighbour Detection (BND). Based on this output alongside monitoring data about the networks' activity, we then propose a channel allocation that optimises performance and as a side effect, stabilises networks that we do not control. We evaluate our approach in a field trial and show that we significantly improve the experience for users, eliminating virtually all interference-related issues. |
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Language
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English
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Source (journal)
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International Conference on Network and Service Management : [proceedings]. - Piscataway, NJ
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International Conference on Network and Service Management
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Source (book)
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14th International Conference on Network and Service Management (CNSM), 5-9 Nov. 2018, Rome, Italy / Salsano, Stefano [edit.]; et al.
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Publication
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New york
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Ieee
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2018
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ISBN
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978-3-903176-14-0
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978-3-903176-14-0
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Volume/pages
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(2018)
, p. 28-36
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ISI
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000458916300004
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Full text (open access)
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