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

Dong Chen, Yuan Zhuang, Jianzhu Huai, Xiao Sun, Xiansheng Yang, Muhammad Awais Javed, Jason Brown, Zhengguo Sheng, John Thompson
2021 IEEE Sensors Journal  
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
more » ... 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 cochannel interference caused by WPANs and WLANs.
doi:10.1109/jsen.2021.3117399 fatcat:dryho7i6sbdgpbkm5c73mwzp4y