TY - GEN
T1 - Study on idle slot availability prediction for WLAN using a probabilistic neural network
AU - Webber, Julian
AU - Mehbodniya, Abolfazl
AU - Hou, Yafei
AU - Yano, Kazuto
AU - Kumagai, Tomoaki
N1 - Funding Information:
This work is supported by Japan Ministry of Internal Affairs and Communications with the fund of “Research and Development of Spectral-Efficiency Improvement Technology Employing Simultaneous Transmission over Multiple License-Exempt Bands.”
Publisher Copyright:
© 2017 University of Western Australia.
PY - 2018/2/27
Y1 - 2018/2/27
N2 - 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.
AB - 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.
KW - WLAN
KW - idle prediction
KW - machine-learning
KW - probabilistic neural network
UR - http://www.scopus.com/inward/record.url?scp=85050633404&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85050633404&partnerID=8YFLogxK
U2 - 10.23919/APCC.2017.8304030
DO - 10.23919/APCC.2017.8304030
M3 - Conference contribution
AN - SCOPUS:85050633404
T3 - 2017 23rd Asia-Pacific Conference on Communications: Bridging the Metropolitan and the Remote, APCC 2017
SP - 1
EP - 6
BT - 2017 23rd Asia-Pacific Conference on Communications
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 23rd Asia-Pacific Conference on Communications, APCC 2017
Y2 - 11 December 2017 through 13 December 2017
ER -