ID | 66746 |
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Author |
Hou, Yafei
Faculty of Environmental, Life, Natural Science and Technology, Okayama University
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Denno, Satoshi
Faculty of Environmental, Life, Natural Science and Technology, Okayama University
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Abstract | Due to massive increase in wireless access from smartphones, IoT devices, WLAN is aiming to improve its spectrum efficiency (SE) using many technologies. Some interesting techniques for WLAN systems are flexible allocation of frequency resource and cognitive radio (CR) techniques which expect to find more useful spectrum resource by modeling and then predicting of channel status using the captured statistics information of the used spectrum. This paper investigates the prediction accuracy of busy/idle duration of two major wireless services: audio service and video service using neural network based predictor. We first study the statistics distribution of their time-series busy/idle (B/I) duration, and then analyze the predictability of the busy/idle duration based on the predictability theory. Then, we propose a data categorization (DC) method which categorizes the duration of recent B/I duration according the their ranges to make the duration of next data be distributed into several streams. From the predictability analysis of each stream and the prediction performance using the probabilistic neural network (PNN), it can be confirmed that the proposed DC can improve the prediction accuracy of time-series data in partial streams.
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Keywords | Wireless LAN
Wireless communication
Media streaming
Wireless sensor networks
Resource management
Probability distribution
Channel allocation
Audio-visual systems
Data processing
Predictive models
Neural networks
Channel status duration prediction
WLAN audio/video traffic
data predictability analysis
probabilistic neural network (PNN)
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Published Date | 2024-02-12
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Publication Title |
IEEE Access
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Volume | volume12
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Publisher | Institute of Electrical and Electronics Engineers
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Start Page | 28201
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End Page | 28211
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ISSN | 2169-3536
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Content Type |
Journal Article
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language |
English
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OAI-PMH Set |
岡山大学
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Copyright Holders | © 2024 The Authors.
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File Version | publisher
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DOI | |
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Related Url | isVersionOf https://doi.org/10.1109/ACCESS.2024.3365188
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License | https://creativecommons.org/licenses/by-nc-nd/4.0/
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Citation | Y. Hou and S. Denno, "WLAN Channel Status Duration Prediction for Audio and Video Services Using Probabilistic Neural Networks," in IEEE Access, vol. 12, pp. 28201-28211, 2024, doi: 10.1109/ACCESS.2024.3365188.
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