TY - GEN
T1 - Busy/Idle Duration Models of Video and Audio WLAN Traffics and Their Prediction Performance
AU - Hou, Yafei
AU - Kawasaki, Shun
AU - Denno, Satoshi
PY - 2020/3
Y1 - 2020/3
N2 - Recently, efficient spectrum usage has become a critical issue, especially for some extreme wireless environments with huge access from massive devices and peoples such as hospitals, railway stations and airports. Cognitive radio (CR) is expected to solve such issue by predicting of channel status from the current statistics information of spectrum usage. This paper will investigate the distribution model of continuous busy/idle duration of two major and widely used wireless services: video service; and audio service, and will show their prediction performances using a simple auto-regressive (AR) based predictor. The results shows that both busy and idle duration data can be fitted using some simple distribution functions. In addition, the AR predictor can provide efficient prediction results especially for idle duration prediction.
AB - Recently, efficient spectrum usage has become a critical issue, especially for some extreme wireless environments with huge access from massive devices and peoples such as hospitals, railway stations and airports. Cognitive radio (CR) is expected to solve such issue by predicting of channel status from the current statistics information of spectrum usage. This paper will investigate the distribution model of continuous busy/idle duration of two major and widely used wireless services: video service; and audio service, and will show their prediction performances using a simple auto-regressive (AR) based predictor. The results shows that both busy and idle duration data can be fitted using some simple distribution functions. In addition, the AR predictor can provide efficient prediction results especially for idle duration prediction.
KW - Channel status prediction
KW - Model distribution
KW - Model fitting
KW - WLAN traffic
UR - http://www.scopus.com/inward/record.url?scp=85085192015&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85085192015&partnerID=8YFLogxK
U2 - 10.1109/LifeTech48969.2020.1570619143
DO - 10.1109/LifeTech48969.2020.1570619143
M3 - Conference contribution
AN - SCOPUS:85085192015
T3 - LifeTech 2020 - 2020 IEEE 2nd Global Conference on Life Sciences and Technologies
SP - 202
EP - 203
BT - LifeTech 2020 - 2020 IEEE 2nd Global Conference on Life Sciences and Technologies
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd IEEE Global Conference on Life Sciences and Technologies, LifeTech 2020
Y2 - 10 March 2020 through 12 March 2020
ER -