Busy/Idle Duration Models of Video and Audio WLAN Traffics and Their Prediction Performance

Yafei Hou, Shun Kawasaki, Satoshi Denno

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

Abstract

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.

Original languageEnglish
Title of host publicationLifeTech 2020 - 2020 IEEE 2nd Global Conference on Life Sciences and Technologies
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages202-203
Number of pages2
ISBN (Electronic)9781728170633
DOIs
Publication statusPublished - Mar 2020
Event2nd IEEE Global Conference on Life Sciences and Technologies, LifeTech 2020 - Kyoto, Japan
Duration: Mar 10 2020Mar 12 2020

Publication series

NameLifeTech 2020 - 2020 IEEE 2nd Global Conference on Life Sciences and Technologies

Conference

Conference2nd IEEE Global Conference on Life Sciences and Technologies, LifeTech 2020
CountryJapan
CityKyoto
Period3/10/203/12/20

Keywords

  • Channel status prediction
  • Model distribution
  • Model fitting
  • WLAN traffic

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition

Fingerprint Dive into the research topics of 'Busy/Idle Duration Models of Video and Audio WLAN Traffics and Their Prediction Performance'. Together they form a unique fingerprint.

  • Cite this

    Hou, Y., Kawasaki, S., & Denno, S. (2020). Busy/Idle Duration Models of Video and Audio WLAN Traffics and Their Prediction Performance. In LifeTech 2020 - 2020 IEEE 2nd Global Conference on Life Sciences and Technologies (pp. 202-203). [9081311] (LifeTech 2020 - 2020 IEEE 2nd Global Conference on Life Sciences and Technologies). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/LifeTech48969.2020.1570619143