University of Oulu

Maksymyuk, T. et al. (2022). AI-Enabled Blockchain Framework for Dynamic Spectrum Management in Multi-operator 6G Networks. In: Klymash, M., Beshley, M., Luntovskyy, A. (eds) Future Intent-Based Networking. Lecture Notes in Electrical Engineering, vol 831. Springer, Cham.

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:
Language: English
Published: Springer Nature, 2022
Publish Date: 2023-01-12


A 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.

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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
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
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