A Novel Machine Learning-Based Scheme for Spectrum Sharing in Virtualized 5G Networks

Network virtualization allows the coexistence of multiple network slices over a shared physical infrastructure, each delivering a service with own requirements in terms of quality of service, coverage, and time span. Spectrum is an expensive commodity, so it must be managed among these slices in the most efficient way. However, current spectrum sharing techniques are too rigid to address all combinations of service requirements and properly exploit the new air interface flexibility and network deployment options introduced by the Fifth Generation (5G) mobile networks. In this paper, the authors extend the state of the art first by proposing a novel spectrum sharing scheme that supports an unlimited number of radio networks. Second, they propose a 5G compliant novel service-based network management architecture to integrate Machine Learning (ML) algorithms for Radio Resource Management (RRM), including spectrum sharing. Finally, they propose a new three-stage ML framework that exploits forecasting, clustering and reinforcement learning algorithms to implement the proposed spectrum sharing scheme. They proposed solution can allow an arbitrary number of 5G network slices to share spectrum more effectively and more dynamically either with each other or with other radio networks.

• Record URL:
  http://dx.doi.org/10.1109/TITS.2022.3173153
• Record URL:
  https://ieeexplore.ieee.org/document/9797230
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2022, IEEE.
• Authors:
  • Morgado, António J
  • Saghezchi, Firooz B
  • Mumtaz, Shahid
  • Frascolla, Valerio
  • Rodriguez, Jonathan
  • Otung, Ifiok
• Publication Date: 2022-10

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 19691-19703
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 23
  • Issue Number: 10
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Machine learning; Mobile communication systems; Mobile radio; Resource allocation; Visualization
• Subject Areas: Data and Information Technology; Highways;

Filing Info

• Accession Number: 01870961
• Record Type: Publication
• Files: TRIS
• Created Date: Jan 24 2023 9:29AM