AI-enabled blockchain framework for dynamic spectrum management in multi-operator 6G networks |
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Author: | Maksymyuk, Taras1; Gazda, Juraj2; Liyanage, Madhusanka3,4; |
Organizations: |
1Lviv Polytechnic National University, Bandery 12, Lviv, 79013, Ukraine 2Technical University of Kosice, Letná 1/9, 04001, Košice, Slovakia 3University College Dublin, Belfield, Dublin 4, Dublin, Ireland
4University of Oulu, Pentti Kaiteran Katu 1, 90014, Oulu, Finland
5Nanchang Institute of Technology, Tianxiang Road 289, Nanchang, 330099, Jiangxi, China 6Korea University, Sejong-ro 2511, Sejong Metropolitan City, 30019, Republic of Korea 7King’s College London, Strand, London, WC2R 2LS, UK |
Format: | article |
Version: | accepted version |
Access: | open |
Online Access: | PDF Full Text (PDF, 2.2 MB) |
Persistent link: | http://urn.fi/urn:nbn:fi-fe202301122476 |
Language: | English |
Published: |
Springer Nature,
2022
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Publish Date: | 2023-01-12 |
Description: |
AbstractA smart architectural design in 5G with flexibility for various deployment scenarios and service requirements has enabled different business models for mobile network operators in both nationwide and local scales. Future 6G networks will feature even more flexible mobile network deployment driven by spectrum and infrastructure sharing among the operators. In this chapter, we propose a new multi-layer framework for 6G with decoupled operators and infrastructure planes. The proposed framework provides a flexibility of network configuration for multiple operators in condition of open spectrum and infrastructure market by using a multi-dimensional matrix representation of the data flows. In particular, the proposed model supports the dynamic switching of the operator and multi-operator service provision for the end users. As a case study, we have developed an AI-based workflow for the dynamic spectrum allocation among multiple mobile network operators. The key advantage of the proposed workflow is that it can be adjusted to the different combinations of the data flows and thus can be suitable for the spectrum allocation among multiple operators. The intelligent capabilities of the proposed workflow are provided by the deep recurrent neural network based on the long short-term memory architecture. The developed model has been trained over the custom dataset with realistic user mobility in urban area. Simulations results show that the proposed intelligent model provides a stable service quality for end users regardless of the serving operators and outperforms the static and semi-intelligent models. see all
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Series: |
Lecture notes in electrical engineering |
ISSN: | 1876-1100 |
ISSN-E: | 1876-1119 |
ISSN-L: | 1876-1100 |
ISBN: | 978-3-030-92435-5 |
ISBN Print: | 978-3-030-92433-1 |
Volume: | 831 |
Pages: | 322 - 338 |
DOI: | 10.1007/978-3-030-92435-5_19 |
OADOI: | https://oadoi.org/10.1007/978-3-030-92435-5_19 |
Host publication: |
Future intent-based networking : on the QoS robust and energy efficient heterogeneous software defined networks |
Host publication editor: |
Klymash, Mikhailo Beshley, Mykola Luntovskyy, Andriy |
Type of Publication: |
A3 Book chapter |
Field of Science: |
213 Electronic, automation and communications engineering, electronics 113 Computer and information sciences |
Subjects: | |
Funding: |
This research was supported by the Ukrainian government project №0120U100674 “Designing the novel decentralized mobile network based on blockchain architecture and artificial intelligence for 5G/6G development in Ukraine”, by the Slovak Research and Development Agency, project number APVV-18-0214, APVV-18-0368, and by the Scientific Grant Agency of the Ministry of Education, science, research and sport of the Slovakia under the contracts: 1/0268/19. |
Copyright information: |
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG |