Study on idle slot availability prediction for WLAN using a probabilistic neural network

Julian Webber, Abolfazl Mehbodniya, Yafei Hou, Kazuto Yano, Tomoaki Kumagai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

We have recently proposed a multi-band wireless local area network (WLAN) system as a solution to the increasingly crowded frequency space. Efficiency can be improved by an agile transceiver that transmits on an idle channel on either or both bands concurrently, and a busy/idle (B/I) predictor will form part of the sensing unit for such a system. A probabilistic neural network (PNN) is studied here for predicting upcoming WLAN B/I status based on pattern matching and classification of previous state patterns. IEEE 802.11 wireless data frames were captured at two hot-spots on multiple channels and the B/I status estimated. The prediction performance is compared for two different locations, channels, prediction matrix dimensions, B/I vs channel occupancy ratio (COR) input types, and frequency of retraining. Results show that the PNN has good potential to estimate the number of idle slots in the upcoming 20 slots and the performance improves with regular retraining.

Original languageEnglish
Title of host publication2017 23rd Asia-Pacific Conference on Communications
Subtitle of host publicationBridging the Metropolitan and the Remote, APCC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
Volume2018-January
ISBN (Electronic)9781740523905
DOIs
Publication statusPublished - Feb 27 2018
Event23rd Asia-Pacific Conference on Communications, APCC 2017 - Perth, Australia
Duration: Dec 11 2017Dec 13 2017

Other

Other23rd Asia-Pacific Conference on Communications, APCC 2017
CountryAustralia
CityPerth
Period12/11/1712/13/17

Keywords

  • idle prediction
  • machine-learning
  • probabilistic neural network
  • WLAN

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing

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  • Cite this

    Webber, J., Mehbodniya, A., Hou, Y., Yano, K., & Kumagai, T. (2018). Study on idle slot availability prediction for WLAN using a probabilistic neural network. In 2017 23rd Asia-Pacific Conference on Communications: Bridging the Metropolitan and the Remote, APCC 2017 (Vol. 2018-January, pp. 1-6). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/APCC.2017.8304030