TY - GEN
T1 - Busy/idle duration model for WLAN traffic and its prediction performance using autoregressive method
AU - Hou, Yafei
AU - Tanaka, Yusuke
AU - Webber, Julian
AU - Yano, Kazuto
AU - Denno, Satoshi
AU - Kumagai, Tomoaki
N1 - Funding Information:
ACKNOWLEDGMENT This work is supported by the Ministry of Internal Affairs and Communications under a grant entitled “R D of Spectral-Efficiency Improvement Technology Employing Simultaneous Transmission over Multiple License-Exempt Bands.”
Publisher Copyright:
© 2018 IEICE
PY - 2019/1/16
Y1 - 2019/1/16
N2 - This paper will study the busy/idle duration model and its prediction performance of autoregressive (AR) based predictor using the real environment data collected during rush-hour weekday evening at a railway station. The analysis shows that both busy and idle duration distribution largely appear as a generalized Pareto (GP) distribution with a different scale value. In addition, the scale value highly decides the prediction performance of the low-complexity linear AR based predictor. We also propose a new AR based predictor by separating busy/idle duration data into different streams to differentiate the scale value of the streams. The prediction performance of the proposed predictor can be improved for the streams with small scale value.
AB - This paper will study the busy/idle duration model and its prediction performance of autoregressive (AR) based predictor using the real environment data collected during rush-hour weekday evening at a railway station. The analysis shows that both busy and idle duration distribution largely appear as a generalized Pareto (GP) distribution with a different scale value. In addition, the scale value highly decides the prediction performance of the low-complexity linear AR based predictor. We also propose a new AR based predictor by separating busy/idle duration data into different streams to differentiate the scale value of the streams. The prediction performance of the proposed predictor can be improved for the streams with small scale value.
KW - Channel status prediction
KW - Generalized Pareto (GP) distribution
KW - WLAN traffic
UR - http://www.scopus.com/inward/record.url?scp=85061769725&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85061769725&partnerID=8YFLogxK
U2 - 10.23919/APMC.2018.8617663
DO - 10.23919/APMC.2018.8617663
M3 - Conference contribution
AN - SCOPUS:85061769725
T3 - Asia-Pacific Microwave Conference Proceedings, APMC
SP - 893
EP - 895
BT - 2018 Asia-Pacific Microwave Conference, APMC 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 30th Asia-Pacific Microwave Conference, APMC 2018
Y2 - 6 November 2018 through 9 November 2018
ER -