Machine Learning in Beyond 5G/6G Networks—State-of-the-Art and Future Trends

Rekkas, Vasileios P.; Sotiroudis, Sotirios P.; Sarigiannidis, Panagiotis; Wan, Shaohua; Karagiannidis, George K.; Goudos, Sotirios K.

doi:10.3390/electronics10222786

Cited by 56 publications

(16 citation statements)

References 121 publications

(143 reference statements)

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“…According to the types of machine learning methods used, the data-driven intelligent routing algorithms are largely separated into intelligent routing algorithms based on supervised learning and reinforcement learning in recent years. model can accurately complete a class of machine learning tasks from input to output mapping [27,28]. In recent years, intelligent routing systems based on supervised learning have mostly relied on deep learning models.…”

Section: Overview Of Intelligent Routing Algorithmsmentioning

confidence: 99%

Comparative Analysis of Routing Schemes Based on Machine Learning

Shaoyu

Tan

Madsen

et al. 2022

Mobile Information Systems

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Machine learning-based distributed routing algorithms, in contrast to traditional mathematical model-driven distributed routing algorithms, are typically data-driven, allowing them to adapt to dynamically changing network environments and various performance evaluation index optimization requirements. It is quite likely that it will become a key part of the next-generation Internet in the future. However, current intelligent routing research is still in its early stages. This article provides a comprehensive review of the state-of-the-art routing algorithms based on machine learning. First, important research on existing data-driven intelligent routing algorithms is presented with the key concepts and applications of these systems demonstrated. To enable intelligent routing algorithms to be deployed in real scenarios with cheap cost and high reliability, two appropriate training deployment frameworks and intelligent routing algorithm training and deployment strategies are given. Finally, the future development of machine learning-based intelligent routing systems is examined. The opportunities and problems that have been encountered, as well as prospective research directions, are discussed.

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Section: Overview Of Intelligent Routing Algorithmsmentioning

confidence: 99%

Comparative Analysis of Routing Schemes Based on Machine Learning

Shaoyu

Tan

Madsen

et al. 2022

Mobile Information Systems

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“…This created new di culties in sending large amounts of data at increased levels and with network latency from resource producers to nal users. The downlink lines experience high tra c overload, especially in 5G applications where multiple smaller network elements are dispersed [40]. The most frequently used information can be cached near the network's interface, such as in ground stations, to alleviate this problem [40].…”

Section: Memory Managementmentioning

confidence: 99%

“…The downlink lines experience high tra c overload, especially in 5G applications where multiple smaller network elements are dispersed [40]. The most frequently used information can be cached near the network's interface, such as in ground stations, to alleviate this problem [40]. Choosing the right cache memory placement approach is di cult, though.…”

Section: Memory Managementmentioning

confidence: 99%

Implications of 5G Network on IoT-based Healthcare Systems Using Deep Learning: A Comprehensive Review

Kumar

Kavitha³

et al. 2022

Preprint

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A global patient-oriented approach quickly replaces the traditionally specialized healthcare model in smart health care. Several technological breakthroughs have facilitated this tremendous transition in healthcare. Presently, innovative healthcare programs use 4G communication protocols in the field. Intelligent connected healthcare systems must incorporate these methods and technologies to thrive. As the healthcare sector expands, different applications are anticipated to generate large quantities of information in different configurations and quantities. Due to end-to-end interruption, frequency, congestion, and other characteristics of these vast and varied data streams, they require specific supervision. Future health care applications will need to be very flexible and responsive within a sensitive time frame, which presents a challenge for today's communication systems. Deep learning models might be interpreted in this context as among the principal resources available to supervise data and generate conclusions. The 5G and DL approach convergence are promising because these methods can obtain pertinent features from unstructured data. Consequently, the distinct communication requirements of electronic health records in the Internet of Things are primarily addressed via the development and creation of 5G networks. IoT network connectivity approaches to address issues with interoperability, energy consumption, device complexity, and reliability while accommodating many devices. This paper contributes to an analysis of IoT-based 5G facilitated intelligent healthcare solutions and services. By organizing and evaluating existing literature, the paper provides a paradigm for efficient health care in 5G. Most of the necessary conditions are outlined for effectively implementing intelligent healthcare systems in specific 5G circumstances. Finally, the number of unresolved problems and research impasses in 5G healthcare monitoring in IoT solutions are discussed.

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“…Table 1 showcases the main differences between 5G and 6G networks and the main improvements with regard to their core attributes [9]. 2 compares the present work to already existing surveys' drone-BS related papers.…”

Section: Introductionmentioning

confidence: 99%

Drones in B5G/6G Networks as Flying Base Stations

Amponis

Λάγκας

Zevgara³

et al. 2022

Drones

Self Cite

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Advances in the fields of networking, broadband communications and demand for high-fidelity low-latency last-mile communications have rendered as-efficient-as-possible relaying methods more necessary than ever. This paper investigates the possibility of the utilization of cellular-enabled drones as aerial base stations in next-generation cellular networks. Flying ad hoc networks (FANETs) acting as clusters of deployable relays for the on-demand extension of broadband connectivity constitute a promising scenario in the domain of next-generation high-availability communications. Matters of mobility, handover efficiency, energy availability, optimal positioning and node localization as well as respective multi-objective optimizations are discussed in detail, with their core ideas defining the structure of the work at hand. This paper examines improvements to the existing cellular network core to support novel use-cases and lower the operation costs of diverse ad hoc deployments.

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Machine Learning in Beyond 5G/6G Networks—State-of-the-Art and Future Trends

Cited by 56 publications

References 121 publications

Comparative Analysis of Routing Schemes Based on Machine Learning

Comparative Analysis of Routing Schemes Based on Machine Learning

Implications of 5G Network on IoT-based Healthcare Systems Using Deep Learning: A Comprehensive Review

Drones in B5G/6G Networks as Flying Base Stations

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