Busy/Idle Duration Prediction for Video and Audio WLAN Traffics Using Autoregressive Predictor with Data Categorization

Yafei Hou, Shun Kawasaki, Satoshi Denno

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

Abstract

Due to huge access from massive devices and peoples such as hospitals, railway stations and airports, wireless local area network (WLAN) is required to have high spectrum efficiency (SE). One of the most intensively researched techniques for wireless LAN systems is cognitive radio (CR) technique which is expected to solve such issue by modeling and predicting of channel status from the current statistics information of spectrum usage. In this paper, we investigate the prediction performance of busy/idle (B/I) duration of two major and widely used wireless services: video service; and audio service using an auto-regressive (AR) based predictor. We first investigate the modeling of their busy/idle duration and analyze their predictability based on predictability theory. Then, we categorize the durations of recent B/I statuses with their ranges to make the duration of the next status be distributed into different sets or streams with different ranges. From their predictability and prediction performance using the low-complexity AR-based predictor, we can confirm that data categorization can largely improve the prediction performance of partial time-series data.

Original languageEnglish
Title of host publication23rd International Conference on Advanced Communication Technology
Subtitle of host publicationOn-Line Security in Pandemic Era!, ICACT 2021 - Proceeding
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages259-264
Number of pages6
ISBN (Electronic)9791188428069
DOIs
Publication statusPublished - Feb 7 2021
Event23rd International Conference on Advanced Communication Technology, ICACT 2021 - Virtual, PyeongChang, Korea, Republic of
Duration: Feb 7 2021Feb 10 2021

Publication series

NameInternational Conference on Advanced Communication Technology, ICACT
Volume2021-February
ISSN (Print)1738-9445

Conference

Conference23rd International Conference on Advanced Communication Technology, ICACT 2021
Country/TerritoryKorea, Republic of
CityVirtual, PyeongChang
Period2/7/212/10/21

Keywords

  • Autoregressive predictor
  • Channel status prediction
  • Data categorizationn
  • WLAN traffic

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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