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

António J. Morgado, Firooz B. Saghezchi, Shahid Mumtaz, Valerio Frascolla, Jonathan Rodriguez, Ifiok Otung

Research output: Contribution to journalArticlepeer-review


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, we extend the state of the art first by proposing a novel spectrum sharing scheme that supports an unlimited number of radio networks. Second, we 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, we propose a new three-stage ML framework that exploits forecasting, clustering and reinforcement learning algorithms to implement the proposed spectrum sharing scheme. Our 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.

Original languageEnglish
Pages (from-to)19691-19703
Number of pages13
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number10
Early online date15 Jun 2022
Publication statusPublished - 1 Oct 2022


  • 5G
  • 5G mobile communication
  • Clustering algorithms
  • Computer architecture
  • Interference
  • Licenses
  • machine learning
  • Microprocessors
  • network function virtualization
  • network management
  • radio spectrum management
  • resource allocation
  • Resource management
  • software-defined networking


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