A Data Analysis Methodology for Obtaining Network Slices Towards 5G Cellular Networks

Kalyoncu, Feyzullah; Zeydan, Engin; Yigit, Ibrahim Onuralp

doi:10.1109/vtcspring.2018.8417782

“…K-means clustering algorithm and Sparse Autoencoders (SAEs) are the most commonly used unsupervised learning approaches in the context of network slicing. K-means is used for identifying categories of end-users as well as conducting performance monitoring [74] and Sparse Autoencoders (SAEs) are used to encode multidimensional network slicing data to lower-dimensional one [73]. In the following, we detail these two algorithms.…”

Section: Unsupervised Learningmentioning

confidence: 99%

Machine Learning in Network Slicing - A Survey

Phyu¹,

Naboulsi²,

Stanica³

2022

Preprint

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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 on 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 sub-categories in the literature. Key takeaways are also presented and open research challenges are discussed, together with potential solutions.

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“…K-means clustering algorithm and Sparse Autoencoders (SAEs) are the most commonly used unsupervised learning approaches in the context of network slicing. K-means is used for identifying categories of end-users as well as conducting performance monitoring [74] and Sparse Autoencoders (SAEs) are used to encode multidimensional network slicing data to lower-dimensional one [73]. In the following, we detail these two algorithms.…”

Section: Unsupervised Learningmentioning

confidence: 99%

Machine Learning in Network Slicing - A Survey

Phyu¹,

Naboulsi²,

Stanica³

2022

Preprint

0

View full text Add to dashboard Cite

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 on 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 sub-categories in the literature. Key takeaways are also presented and open research challenges are discussed, together with potential solutions.

show abstract

“…In network slicing, k-means can be used to group applications into different clusters according to their similar features. In [31], authors discussed that the number of network slices that can be created by using k-means algorithm for each mobile network operator (MNO) depending on the KPI performances. In Section 7, we have proposed an approach that how clustering can be done in network slicing in order to manage the resources intelligently.…”

Section: Unsupervised Learning For Network Slicingmentioning

confidence: 99%

Network Slicing for Beyond 5G Systems: An Overview of the Smart Port Use Case

Sohaib

¹

,

Onireti

²

,

Sambo

³

et al. 2021

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As the idea of a new wireless communication standard (5G) started to circulate around the world, there was much speculation regarding its performance, making it necessary to carry out further research by keeping in view the challenges presented by it. 5G is considered a multi-system support network due to its ability to provide benefits to vertical industries. Due to the wide range of devices and applications, it is essential to provide support for massively interconnected devices. Network slicing has emerged as the key technology to meet the requirements of the communications network. In this paper, we present a review of the latest achievements of 5G network slicing by comparing the architecture of The Next Generation Mobile Network Alliance’s (NGMN’s) and 5G-PPP, using the enabling technologies software-defined networking (SDN) and network function virtualization (NFV). We then review and discuss machine learning (ML) techniques and their integration with network slicing for beyond 5G networks and elaborate on how ML techniques can be useful for mobility prediction and resource management. Lastly, we propose the use case of network slicing based on ML techniques in a smart seaport environment, which will help to manage the resources more efficiently.

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A Data Analysis Methodology for Obtaining Network Slices Towards 5G Cellular Networks

Cited by 8 publications

References 4 publications

Machine Learning in Network Slicing - A Survey

Machine Learning in Network Slicing - A Survey

Machine Learning in Network Slicing - A Survey

Network Slicing for Beyond 5G Systems: An Overview of the Smart Port Use Case

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