AUTODEEPSLICE: A Data Driven Network Slicing Technique of 5G network using Automatic Deep Learning

Deepraj Chowdhury; Rupanjan Das; Risav Rana; Ashutosh Dhar Dwivedi; Pushpita Chatterjee; Raghava Rao Mukkamala

doi:10.1109/GCWkshps56602.2022.10008652

AUTODEEPSLICE: A Data Driven Network Slicing Technique of 5G network using Automatic Deep Learning

Deepraj Chowdhury^*, Rupanjan Das, Risav Rana, Ashutosh Dhar Dwivedi, Pushpita Chatterjee, Raghava Rao Mukkamala

^*Corresponding author for this work

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

2 Citations (Scopus)

Abstract

With the advancement of mobile networks, and the revolution of mobile communication from 1G to 5G, human dependency has increased, so it has become the most priority to provide an efficient and highly reliable network. The 5G networks need high capacity and high-speed data transfer, with the least latency and increased security. 5G service providers are trying to find a programmable solution that can handle a huge number of independent service users on the same physical infrastructure. Network slicing is used to provide end-to-end network resource allocation. To accelerate the 5G network performance, Artificial Intelligence (AI) powered data-driven based decisions will be used for future communication. In this proposed work, Automatic deep learning has been implemented, where the hyperparameter will automatically select the algorithm and tune the hyperparameter. The proposed model AUTODEEPSLICE efficiently makes intelligent decisions and will choose the best network slice at the time of network breakdown.

Original language	English
Title of host publication	2022 IEEE GLOBECOM Workshops, GC Wkshps 2022 - Proceedings
Number of pages	5
Publisher	IEEE
Publication date	2022
Pages	450-454
ISBN (Electronic)	9781665459754
DOIs	https://doi.org/10.1109/GCWkshps56602.2022.10008652
Publication status	Published - 2022
Externally published	Yes
Event	2022 IEEE GLOBECOM Workshops, GC Wkshps 2022 - Virtual, Online, Brazil Duration: 4 Dec 2022 → 8 Dec 2022

Conference

Conference	2022 IEEE GLOBECOM Workshops, GC Wkshps 2022
Country/Territory	Brazil
City	Virtual, Online
Period	04/12/2022 → 08/12/2022

Series	2022 IEEE GLOBECOM Workshops, GC Wkshps 2022 - Proceedings

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

5G Cellular Networks
Autokeras
Automatic Deep Learning
Optimised Network Slicing

Access to Document

10.1109/GCWkshps56602.2022.10008652

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Cite this

@inproceedings{88cec18cf527441da697905b56327de1,

title = "AUTODEEPSLICE: A Data Driven Network Slicing Technique of 5G network using Automatic Deep Learning",

abstract = "With the advancement of mobile networks, and the revolution of mobile communication from 1G to 5G, human dependency has increased, so it has become the most priority to provide an efficient and highly reliable network. The 5G networks need high capacity and high-speed data transfer, with the least latency and increased security. 5G service providers are trying to find a programmable solution that can handle a huge number of independent service users on the same physical infrastructure. Network slicing is used to provide end-to-end network resource allocation. To accelerate the 5G network performance, Artificial Intelligence (AI) powered data-driven based decisions will be used for future communication. In this proposed work, Automatic deep learning has been implemented, where the hyperparameter will automatically select the algorithm and tune the hyperparameter. The proposed model AUTODEEPSLICE efficiently makes intelligent decisions and will choose the best network slice at the time of network breakdown.",

keywords = "5G Cellular Networks, Autokeras, Automatic Deep Learning, Optimised Network Slicing",

author = "Deepraj Chowdhury and Rupanjan Das and Risav Rana and Dwivedi, {Ashutosh Dhar} and Pushpita Chatterjee and Mukkamala, {Raghava Rao}",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE GLOBECOM Workshops, GC Wkshps 2022 ; Conference date: 04-12-2022 Through 08-12-2022",

year = "2022",

doi = "10.1109/GCWkshps56602.2022.10008652",

language = "English",

series = "2022 IEEE GLOBECOM Workshops, GC Wkshps 2022 - Proceedings",

pages = "450--454",

booktitle = "2022 IEEE GLOBECOM Workshops, GC Wkshps 2022 - Proceedings",

publisher = "IEEE",

address = "United States",

}

Chowdhury, D, Das, R, Rana, R, Dwivedi, AD, Chatterjee, P & Mukkamala, RR 2022, AUTODEEPSLICE: A Data Driven Network Slicing Technique of 5G network using Automatic Deep Learning. in 2022 IEEE GLOBECOM Workshops, GC Wkshps 2022 - Proceedings. IEEE, 2022 IEEE GLOBECOM Workshops, GC Wkshps 2022 - Proceedings, pp. 450-454, 2022 IEEE GLOBECOM Workshops, GC Wkshps 2022, Virtual, Online, Brazil, 04/12/2022. https://doi.org/10.1109/GCWkshps56602.2022.10008652

AUTODEEPSLICE: A Data Driven Network Slicing Technique of 5G network using Automatic Deep Learning. / Chowdhury, Deepraj; Das, Rupanjan; Rana, Risav et al.
2022 IEEE GLOBECOM Workshops, GC Wkshps 2022 - Proceedings. IEEE, 2022. p. 450-454 (2022 IEEE GLOBECOM Workshops, GC Wkshps 2022 - Proceedings).

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

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AU - Dwivedi, Ashutosh Dhar

AU - Chatterjee, Pushpita

AU - Mukkamala, Raghava Rao

PY - 2022

Y1 - 2022

N2 - With the advancement of mobile networks, and the revolution of mobile communication from 1G to 5G, human dependency has increased, so it has become the most priority to provide an efficient and highly reliable network. The 5G networks need high capacity and high-speed data transfer, with the least latency and increased security. 5G service providers are trying to find a programmable solution that can handle a huge number of independent service users on the same physical infrastructure. Network slicing is used to provide end-to-end network resource allocation. To accelerate the 5G network performance, Artificial Intelligence (AI) powered data-driven based decisions will be used for future communication. In this proposed work, Automatic deep learning has been implemented, where the hyperparameter will automatically select the algorithm and tune the hyperparameter. The proposed model AUTODEEPSLICE efficiently makes intelligent decisions and will choose the best network slice at the time of network breakdown.

AB - With the advancement of mobile networks, and the revolution of mobile communication from 1G to 5G, human dependency has increased, so it has become the most priority to provide an efficient and highly reliable network. The 5G networks need high capacity and high-speed data transfer, with the least latency and increased security. 5G service providers are trying to find a programmable solution that can handle a huge number of independent service users on the same physical infrastructure. Network slicing is used to provide end-to-end network resource allocation. To accelerate the 5G network performance, Artificial Intelligence (AI) powered data-driven based decisions will be used for future communication. In this proposed work, Automatic deep learning has been implemented, where the hyperparameter will automatically select the algorithm and tune the hyperparameter. The proposed model AUTODEEPSLICE efficiently makes intelligent decisions and will choose the best network slice at the time of network breakdown.

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ER -

AUTODEEPSLICE: A Data Driven Network Slicing Technique of 5G network using Automatic Deep Learning

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Keywords

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