Mapping spectrum consumption models to cognitive radio ontology for automatic inference


Radio frequency spectrum management plays a critical role in various domains, including government, military, industrial and personal communications. Current methodology of spectrum management relies primarily on licensing, i.e., giving control over a specific part of the spectrum to a limited number of providers. This approach, however, may lead to an underutilization of the spectrum. To address this problem, various dynamic spectrum access and management approaches have been investigated and some are being actively tested in the field. In the work described in this paper we use the Model-Based Spectrum Management (MBSM) approach to policy-based dynamic spectrum management in which spectrum access policies are represented using Spectrum Consumption Models (SCMs). While in MBSM SCMs are expressed in an XML markup language called SCMML, we add a “logical” layer to MBSM by mapping SCMML to an ontology expressed in Web Ontology Language (OWL)—the formal language used in the Semantic Web. We show that it is possible to use such representations for automatically interpreting policies expressed in this layer by an ontology-based inference engine to derive decisions on the permissions of specific spectrum access requests. The main benefit of using the ontology-based representation is that it does not require addition of any new procedural code and thus new policies can be loaded and used by the system on the fly. The paper focuses on how to implement such a system. Towards this end, it presents two spectrum management related use cases and shows how these use cases are implemented using ontologies. The paper also discusses the advantages of the use of an ontology-based approach to dynamic spectrum management and its potential. Quantitative evaluation of the approach is part of our current work.

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  1. 1.

    FCC. (2002). Report of the spectrum efficiency working group. Federal Communications Commission, technical report.

  2. 2.

    IEEE 1900.5 Working Group (WG) on Policy Language and Architectures for Managing Cognitive Radio for Dynamic Spectrum Access Applications. (2017). IEEE Standards Association.

  3. 3.

    IEEE Standard for Policy Language Requirements and System Architectures for Dynamic Spectrum Access Systems. IEEE Std 1900.5\(^{TM}\). (2011). IEEE Communications Society.

  4. 4.

    IEEE. (2015). IEEE DySPAN 1900.5.2 standard.

  5. 5.

    Stine, J., & Schmitz, S. (2013). Model based spectrum management. The MITRE Corporation, technical report.

  6. 6.

    Grande, L., Sherman, M., Zhu, H., Kokar, M. M., & Stine, J. (2013). IEEE DySPAN 1900.5 efforts to support spectrum access standardization. In 2013 IEEE military communications conference (pp. 1750–1755). IEEE.

  7. 7.

    W3C. (2014). XQuery 3.0: An XML query language, 2014.

  8. 8.

    W3C. (2009). OWL 2 web ontology language document overview, 2009.

  9. 9.

    Chang, C., & Keisler, H. (1992). Model theory. Amsterdam: North Holland.

    Google Scholar 

  10. 10.

    W3C. (2012). OWL 2 web ontology language profiles (2nd ed.).

  11. 11.

    Gruber, T. (2009). Ontology. In L. Liu & M. T. Özsu (Eds.), The encyclopedia of database systems (pp. 1963–1965). Berlin: Springer.

    Google Scholar 

  12. 12.

    Suresh, D., Kokar, M. M., Moskal, J., & Chen, Y. (2015). Updating CRO to CRO2. In Wireless innovation forum conference on wireless communications technologies and software defined radio. Wireless Innovation Forum.

  13. 13.

    WinnF. (2010). Description of cognitive radio ontology. Wireless Innovation Forum MLM Working Group, technical report WINNF-10-S-0007.

  14. 14.

    Nuvio: a simple foundational ontology. (2015).

  15. 15.

    Moskal, J., Kokar, M., Mieczyslaw., & Morgan. J. (2015). Semantic validation of T&E XML data. In International telemetering conference proceedings (pp. 1–11). International Foundation for Telemetering.

  16. 16.

    Baader, F., McGuinness, D., Nardi, D., & Patel-Schneider, D. (Eds.). (2003). The description logic handbook. Cambridge: Cambridge University Press.

    Google Scholar 

  17. 17.

    Protege. (2013). Stanford Center for Biomedical Informatics Research.

  18. 18.

    Ontoviz plug-in for Protege. Stanford Center for Biomedical Informatics Research.

  19. 19.

    Wilkins, D., Denker, G., Stehr, M. O., Elenius, D., Senanay, R., & Talcott, C. (2007). Policy-based cognitive radios. IEEE Wireless Communications, 14(4), 41–46.

    Article  Google Scholar 

  20. 20.

    Matheus, C., Kokar, M M., & Dionne, R. (2008). A demonstration of formal policy reasoning using an extended version of BaseVISor. In IEEE workshop on policies for distributed systems and networks, POLICY 2008.

  21. 21.

    ter Horst, H. (2005). Combining RDF and Part of OWL with rules: semantics, decidability, complexity. In Proceedings of the semantic integration workshop of the fourth international semantic web conference (ISWC-05) (pp. 668–684).

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Correspondence to Mieczyslaw M. Kokar.

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Chen, Y., Kokar, M.M., Moskal, J.J. et al. Mapping spectrum consumption models to cognitive radio ontology for automatic inference. Analog Integr Circ Sig Process 106, 9–21 (2021).

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  • RF spectrum
  • Spectrum management
  • Dynamic spectrum access
  • Spectrum access policies
  • Ontology-based descriptions
  • Spectrum access policy rules
  • Automatic policy interpretation
  • Automatic inference
  • Spectrum policy use cases