A comprehensive survey on 6G and beyond: Enabling technologies, opportunities of machine learning and challenges

Jawad, Aqeel Thamer; Maaloul, Rihab; Chaari, Lamia

doi:10.1016/j.comnet.2023.110085

Cited by 16 publications

(1 citation statement)

References 147 publications

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“…The compilation includes diverse references to research papers, each centering on specific experimental contexts such as wireless sensor networks, IoT devices, 5G networks, smart grids, edge computing, and mobile ad hoc networks. These investigations employ a spectrum of techniques for packet loss detection, encompassing machine learning, statistical analysis, deep learning, time series analysis, probabilistic models, and network simulation [20][21][22].…”

Section: Related Workmentioning

confidence: 99%

Uncovering the Limitations and Insights of Packet Status Prediction Models in IEEE 802.15.4-Based Wireless Networks and Insights from Data Science

Ávalos-Arce,

Pérez-Díaz,

Del-Valle-Soto

et al. 2024

Informatics

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Wireless networks play a pivotal role in various domains, including industrial automation, autonomous vehicles, robotics, and mobile sensor networks. This research investigates the critical issue of packet loss in modern wireless networks and aims to identify the conditions within a network’s environment that lead to such losses. We propose a packet status prediction model for data packets that travel through a wireless network based on the IEEE 802.15.4 standard and are exposed to five different types of interference in a controlled experimentation environment. The proposed model focuses on the packetization process and its impact on network robustness. This study explores the challenges posed by packet loss, particularly in the context of interference, and puts forth the hypothesis that specific environmental conditions are linked to packet loss occurrences. The contribution of this work lies in advancing our understanding of the conditions leading to packet loss in wireless networks. Data are retrieved with a single CC2531 USB Dongle Packet Sniffer, whose pieces of information on packets become the features of each packet from which the classifier model will gather the training data with the aim of predicting whether a packet will unsuccessfully arrive at its destination. We found that interference causes more packet loss than that caused by various devices using a WiFi communication protocol simultaneously. In addition, we found that the most important predictors are network strength and packet size; low network strength tends to lead to more packet loss, especially for larger packets. This study contributes to the ongoing efforts to predict and mitigate packet loss, emphasizing the need for adaptive models in dynamic wireless environments.

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Section: Related Workmentioning

confidence: 99%

Uncovering the Limitations and Insights of Packet Status Prediction Models in IEEE 802.15.4-Based Wireless Networks and Insights from Data Science

Ávalos-Arce,

Pérez-Díaz,

Del-Valle-Soto

et al. 2024

Informatics

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Exploring the Role of 6G Technology in Smart Healthcare Systems

Suneel,

Manjula,

Sowmya

et al. 2024

Smart Hospitals

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Spectrum Allocation in 5G and Beyond Intelligent Ubiquitous Networks

Ravi,

Verma

2024

Int J Network Mgmt

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Effective spectrum allocation in 5G and beyond intelligent ubiquitous networks is vital for predicting future frequency band needs and ensuring optimal network performance. As wireless communication evolves from 4G to 5G and beyond, it has brought about remarkable advancements in speed and connectivity. However, with the growing demand for higher data rates and increased network capacity, new challenges in managing and utilizing network frequencies have emerged. Accurately forecasting spectrum requirements is critical to addressing these challenges. This research explores how machine learning (ML) plays a pivotal role in optimizing network performance through intelligent decision‐making, predictive analysis, and adaptive management of network resources. By leveraging ML algorithms, networks can autonomously self‐optimize in real time, adjusting to changing conditions and improving performance in 5G and beyond. The effectiveness of our approach was demonstrated through an extensive case study, which showed that it not only meets spectrum requirements in various environments but also significantly reduces energy consumption by pinpointing the appropriate spectrum range for each location. These results underscore the approach's potential for enhancing spectrum management in future networks, offering a scalable and efficient solution to the challenges facing 5G and beyond.

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A comprehensive survey on 6G and beyond: Enabling technologies, opportunities of machine learning and challenges

Cited by 16 publications

References 147 publications

Uncovering the Limitations and Insights of Packet Status Prediction Models in IEEE 802.15.4-Based Wireless Networks and Insights from Data Science

Uncovering the Limitations and Insights of Packet Status Prediction Models in IEEE 802.15.4-Based Wireless Networks and Insights from Data Science

Exploring the Role of 6G Technology in Smart Healthcare Systems

Spectrum Allocation in 5G and Beyond Intelligent Ubiquitous Networks

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