ADAPTIVE6G: Adaptive Resource Management for Network Slicing Architectures in Current 5G and Future 6G Systems

Thantharate, Anurag; Beard, Cory

doi:10.1007/s10922-022-09693-1

Cited by 26 publications

(15 citation statements)

References 32 publications

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“…Regarding key terms, recent research has focused on generating algorithms for recognizing and learning data in the network [6] and trained models start from the available data in order to improve connections to the mobile network, thus improving quality and latency and allowing increasingly faster responses from the network [13]. However, one of the great advantages that this technology is a set of concepts for automated network management, not only to improve the quality of service but also to reduce network management burdens on network administrators [94].…”

Section: Discussionmentioning

confidence: 99%

“…With the advancement of different optimization techniques, methods and tools (such as artificial intelligence, machine learning and big data), it is possible to improve the availability, quality and coverage of mobile networks, thereby facilitating better service provision to users of mobile networks [2]. Future smart wireless networks require an adaptive learning approach towards a shared learning model to enable collaboration between data generated by network elements and virtualized functions [6].…”

Section: Introductionmentioning

confidence: 99%

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Research Trends in the Use of Machine Learning Applied in Mobile Networks: A Bibliometric Approach and Research Agenda

García-Pineda,

Valencia-Arias,

Patiño-Vanegas

et al. 2023

Informatics

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This article aims to examine the research trends in the development of mobile networks from machine learning. The methodological approach starts from an analysis of 260 academic documents selected from the Scopus and Web of Science databases and is based on the parameters of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Quantity, quality and structure indicators are calculated in order to contextualize the documents’ thematic evolution. The results reveal that, in relation to the publications by country, the United States and China, who are competing for fifth generation (5G) network coverage and are responsible for manufacturing devices for mobile networks, stand out. Most of the research on the subject focuses on the optimization of resources and traffic to guarantee the best management and availability of a network due to the high demand for resources and greater amount of traffic generated by the many Internet of Things (IoT) devices that are being developed for the market. It is concluded that thematic trends focus on generating algorithms for recognizing and learning the data in the network and on trained models that draw from the available data to improve the experience of connecting to mobile networks.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research Trends in the Use of Machine Learning Applied in Mobile Networks: A Bibliometric Approach and Research Agenda

García-Pineda,

Valencia-Arias,

Patiño-Vanegas

et al. 2023

Informatics

View full text Add to dashboard Cite

show abstract

“…Typically, ML models for traffic forecasting are trained on large datasets, an operation which is time-consuming. To deal with this, the authors in [101] rely on TL-based DNN for traffic forecasting per slice. More precisely, they initialize the weights of their model with the weights of a pretrained model on a similar task to perform the per slice traffic forecasting.…”

Section: ) Rnn and Ann-based Forecastingmentioning

confidence: 99%

Machine Learning in Network Slicing—A Survey

Phyu¹,

Naboulsi²,

Stanica

2023

IEEE Access

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5G and beyond networks are expected to support a wide range of services, with highly diverse requirements. Yet, the traditional "one-size-fits-all" network architecture lacks the flexibility to accommodate these services. In this respect, network slicing has been introduced as a promising paradigm for 5G and beyond networks, supporting not only traditional mobile services, but also vertical industries services, with very heterogeneous requirements. Along with its benefits, the practical implementation of network slicing brings a lot of challenges. Thanks to the recent advances in machine learning (ML), some of these challenges have been addressed. In particular, the application of ML approaches is enabling the autonomous management of resources in the network slicing paradigm. Accordingly, this paper presents a comprehensive survey on contributions on ML in network slicing, identifying major categories and subcategories in the literature. Lessons learned are also presented and open research challenges are discussed, together with potential solutions.

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“…Third, AI-based method may need to cooperate existed or traditional methods considering the privacy issues. [4][5][6][7][8][9][10][11][12][13][14][15][16] SCL, 15 distributed DRL, 16 and ADAPTIVE6G 17 apply transfer learning to integrate models from different devices.…”

Section: The Literature Reviewmentioning

confidence: 99%

“…Network slicing enables the telecommunication service providers to accommodate heterogeneous use cases and create multiple virtual networks over a common physical telecommunication infrastructure. [1][2][3][4][5] To maximize the network resource usage efficiency, several artificial intelligent based (AI-based) methodologies [6][7][8][9][10][11][12][13][14][15][16][17] are proposed to intelligently allocate sufficient bandwidth resources to each slice. The AI-based methodologies indeed enhance communication resource usage; however, some researchers argue that it may not perform well with the increasing complexity and heterogeneity of network.…”

Section: Introductionmentioning

confidence: 99%

An adaptive heuristic algorithm to solve the network slicing resource management problem

Chen

2023

Int J Communication

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Artificial intelligence-based (AI-based) network slicing bandwidth allocation enables the 5G/6G service providers to create multiple virtual networks atop a shared physical infrastructure while fulfilling varying end-user demands. Some researchers argue that AI-enable network may run the danger of having private information compromised. We still need a backup rule-based methodology to allocate bandwidth resource to each slice, if the AI-based method suddenly encounters security issues. To design such a rule-based methodology, this study attempts to answer two questions: (1) Is the network slicing bandwidth allocation problem the nondeterministic polynomial-time completeness (NP-completeness)? ( 2) Is there a heuristic methodology without any training process, which has equivalent performance compared to the AI-based methodology? This study first proves the classical network slicing bandwidth allocation problems is NP-completeness. This shows that the designed heuristic method is inescapably suboptimal to the network slicing bandwidth allocation problem. Secondly, this study proposes the Adaptive Hungarian Algorithm (AHA), which outperforms previous AI-empowered method and does not need any training process. The experiments demonstrate that AHA reached 93%-97% of the maximal system throughput by brute-and-force algorithm, compared to other methodologies only having at most 93% of the maximal system throughput. This also indicates that AHA is capable to solve the network slicing bandwidth allocation problem, if the telecommunication operators do not have sufficient sample complexity to train an AI model.

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ADAPTIVE6G: Adaptive Resource Management for Network Slicing Architectures in Current 5G and Future 6G Systems

Cited by 26 publications

References 32 publications

Research Trends in the Use of Machine Learning Applied in Mobile Networks: A Bibliometric Approach and Research Agenda

Research Trends in the Use of Machine Learning Applied in Mobile Networks: A Bibliometric Approach and Research Agenda

Machine Learning in Network Slicing—A Survey

An adaptive heuristic algorithm to solve the network slicing resource management problem

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