Coexistence and Interference Mitigation for WPANs and WLANs From Traditional Approaches to Deep Learning: A Review

Chen, Dong and Zhuang, Yuan and Huai, Jianzhu and Sun, Xiao and Yang, Xiansheng and Javed, Muhammed and Brown, Jason ORCID: https://orcid.org/0000-0002-0698-5758 and Sheng, Zhenguo and Thompson, John (2021) Coexistence and Interference Mitigation for WPANs and WLANs From Traditional Approaches to Deep Learning: A Review. IEEE Sensors Journal, 21 (22). pp. 25561-25589. ISSN 1530-437X

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Abstract

More and more devices, such as Bluetooth and IEEE 802.15.4 devices forming Wireless Personal Area Networks (WPANs) and IEEE 802.11 devices constituting Wireless Local Area Networks (WLANs), share the 2.4 GHz Industrial, Scientific and Medical (ISM) band in the realm of the Internet of Things (IoT) and Smart Cities. However, the coexistence of these devices could pose a real challenge—co-channel interference that would severely compromise network performances. Although the coexistence issues has been partially discussed elsewhere in some articles, there is no single review that fully summarises and compares recent research outcomes and challenges of IEEE 802.15.4 networks, Bluetooth and WLANs together. In this work, we revisit and provide a comprehensive review on the coexistence and interference mitigation for those three types of networks. We summarize the strengths and weaknesses of the current methodologies, analysis and simulation models in terms of numerous important metrics such as the packet reception ratio, latency, scalability and energy efficiency. We discover that although Bluetooth and IEEE 802.15.4 networks are both WPANs, they show quite different performances in the presence of WLANs. IEEE 802.15.4 networks are adversely impacted by WLANs, whereas WLANs are interfered by Bluetooth. When IEEE 802.15.4 networks and Bluetooth co-locate, they are unlikely to harm each other. Finally, we also discuss the future research trends and challenges especially Deep-Learning and Reinforcement-Learning-based approaches to detecting and mitigating the co-channel interference caused by WPANs and WLANs.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Faculty/School / Institute/Centre: Historic - Faculty of Health, Engineering and Sciences - School of Mechanical and Electrical Engineering (1 Jul 2013 - 31 Dec 2021)
Faculty/School / Institute/Centre: Historic - Faculty of Health, Engineering and Sciences - School of Mechanical and Electrical Engineering (1 Jul 2013 - 31 Dec 2021)
Date Deposited: 25 Oct 2021 02:45
Last Modified: 16 Jun 2022 23:21
Uncontrolled Keywords: The Internet of Things, WPANs, WLANs, bluetooth, IEEE 802.15.4, interference mitigation, deep learning, reinforcement learning, heterogeneous networks
Fields of Research (2008): 08 Information and Computing Sciences > 0805 Distributed Computing > 080503 Networking and Communications
08 Information and Computing Sciences > 0805 Distributed Computing > 080504 Ubiquitous Computing
10 Technology > 1005 Communications Technologies > 100510 Wireless Communications
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4606 Distributed computing and systems software > 460609 Networking and communications
40 ENGINEERING > 4006 Communications engineering > 400608 Wireless communication systems and technologies (incl. microwave and millimetrewave)
Socio-Economic Objectives (2008): B Economic Development > 89 Information and Communication Services > 8901 Communication Networks and Services > 890103 Mobile Data Networks and Services
Socio-Economic Objectives (2020): 22 INFORMATION AND COMMUNICATION SERVICES > 2201 Communication technologies, systems and services > 220107 Wireless technologies, networks and services
Identification Number or DOI: https://doi.org/10.1109/JSEN.2021.3117399
URI: http://eprints.usq.edu.au/id/eprint/43890

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