- Title
- Contention resolution in wi-fi 6-enabled internet of things based on deep learning
- Creator
- Chen, Chen; Li, Junchao; Balasubramanian, Venki; Wu, Yongqiang; Zhang, Yongqiang; Wan, Shaohua
- Date
- 2021
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/176324
- Identifier
- vital:15100
- Identifier
-
https://doi.org/10.1109/JIOT.2020.3037774
- Identifier
- ISBN:2327-4662 (ISSN)
- Abstract
- Internet of Things (IoT) is expected to vastly increase the number of connected devices. As a result, a multitude of IoT devices transmit various information through wireless communication technology, such as the Wi-Fi technology, cellular mobile communication technology, low-power wide-area network (LPWAN) technology. However, even the latest Wi-Fi technology is still ready to accommodate these large amounts of data. Accurately setting the contention window (CW) value significantly affects the efficiency of the Wi-Fi network. Unfortunately, the standard collision resolution used by IEEE 802.11ax networks is nonscalable; thus, it cannot maintain stable throughput for an increasing number of stations, even when Wi-Fi 6 has been designed to improve performance in dense scenarios. To this end, we propose a CW control strategy for Wi-Fi 6 systems. This strategy leverages deep learning to search for optimal configuration of CW under different network conditions. Our deep neural network is trained by data generated from a Wi-Fi 6 simulation system with some varying key parameters, e.g., the number of nodes, short interframe space (SIFS), distributed interframe space (DIFS), and data transmission rate. Numerical results demonstrated that our deep learning scheme could always find the optimal CW adjustment multiple by adaptively perceiving the channel competition status. The finalized performance of our model has been significantly improved in terms of system throughput, average transmission delay, and packet retransmission rate. This makes Wi-Fi 6 better adapted to the access of a large number of IoT devices. © 2014 IEEE.
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- IEEE Internet of Things Journal Vol. 8, no. 7 (2021), p. 5309-5320
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright @ 2020 IEEE
- Rights
- Open Access
- Subject
- 0805 Distributed Computing; 1005 Communications Technologies; Contention window (CW) optimization; deep learning; Internet of Things (IoT); Wi-Fi 6
- Full Text
- Reviewed
- Funder
- Manuscript received September 12, 2020; revised October 20, 2020; accepted November 6, 2020. Date of publication November 16, 2020; date of current version March 24, 2021. This work was supported in part by the National Key Research and Development Program of China under Grant 2020YFB1807500; in part by the National Natural Science Foundation of China under Grant 62072360, Grant 62001357, Grant 61672131, and Grant 61901367; in part by the Key Research and Development Plan of Shaanxi Province under Grant 2017ZDCXL-GY-05-01 and Grant 2020JQ-844; in part by the Key Laboratory of Embedded System and Service Computing (Tongji University) under Grant ESSCKF2019-05; in part by the Ministry of Education, Xi’an Science and Technology Plan under Grant 20RGZN0005; and in part by the Xi’an Key Laboratory of Mobile Edge Computing and Security under Grant 201805052-ZD3CG36. (Corresponding author: Shaohua Wan.) Chen Chen, Junchao Li, and Yuru Zhang are with the State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an 710071, China (e-mail: cc2000@mail.xidian.edu.cn; 519435930@qq.com; zyr15848264587@163.com). Venki Balasubramaniam is with the School of Science, Engineering and Information Technology, Federation University, Mount Helen, VIC 3350, Australia (e-mail: v.balasubramanian@federation.edu.au). Yongqiang Wu is with the Department of Management, Zhejiang Wellsun Intelligent Technology Company Ltd., Hangzhou 317200, China (e-mail: wyq@wellsun.com). Shaohua Wan is with the School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan 430073, China (e-mail: shaohua.wan@ieee.org). Digital Object Identifier 10.1109/JIOT.2020.3037774 2327-4662 ©c 2020 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.
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