Skip to main content

Hybrid OSA-CSA Model for an Efficient Dynamic Spectrum Access in Cognitive Radio Environments

  • Conference paper
  • First Online:
Human Centred Intelligent Systems (KES-HCIS 2021)

Abstract

The access efficiency of the radio spectrum can be improved by adopting dynamic spectrum management (DSM) to face the existing challenges in wireless communication, such as the ever-increasing spectrum demand and inefficient spectrum use. This work proposes a hybrid model for dynamic spectrum access in cognitive radio environments. The access type combines the benefits of opportunistic spectrum access (OSA) and concurrent spectrum access (CSA). The spectrum allocation problem is modeled as a non-cooperative game, each unlicensed user (secondary user (SU)) acting as a player to gain a channel for transmission. Nash equilibrium is adopted as the solution of this game model, in which, the licensed users (primary users (PUs)) have a higher priority than the SUs and are completely protected from any harmful interference. Results show that the proposed model can improve spectrum allocation efficiency, both in terms of minimizing the total generated interference and maximizing energy efficiency.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 229.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 299.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 299.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. “Gartner says the internet of things will transform the data center,” Gartner Inc. (2014). http://www.gartner.com/newsroom/id/2684616

  2. Palattella, M.R., et al.: Internet of Things in the 5G era: enablers, architecture, and business models. IEEE J. Sel. Areas Commun. 34(3), 510–527 (2016)

    Article  Google Scholar 

  3. Li, S., Xu, L.D., Zhao, S.: 5G Internet of Things: a survey. J. Ind. Inf. Integr. 10 (2018). Identification and quantification of key socio-economic data to support strategic planning for the introduction of 5G in Europe-SMART, 2014/0008. Technical report, European Union (2016)

    Google Scholar 

  4. Niu, Y., Li, Y., Jin, D., Su, L., Vasilakos, A.V.: A survey of millimeter wave communications (mmWave) for 5G: opportunities and challenges. Wireless Netw. 21(8), 2657–2676 (2015)

    Article  Google Scholar 

  5. Arjoune, Y., Kaabouch, N.: ‘A comprehensive survey on spectrum sensing in cognitive radio networks: recent advances, new challenges, and future research directions.’ Sensors 19(1), 126 (2019)

    Article  Google Scholar 

  6. FCC Spectrum Policy Task Force, “Report of the spectrum efficiency working group,” November 2002. http://www.fcc.gov/sptf/reports.html

  7. Kolodzy, P., et al.: Next generation communications: Kickoff meeting. In: Proceedings of DARPA (2001)

    Google Scholar 

  8. Liang, Y.-C.: Dynamic Spectrum Management: From Cognitive Radio to Blockchain and Artificial Intelligence. Springer, Heidelberg (2020). https://doi.org/10.1007/978-981-15-0776-2

  9. Filippou, M.C., Ropokis, G.A., Gesbert, D., Ratnarajah, T.: Performance analysis and optimization of hybrid MIMO Cognitive Radio systems. In: Proceedings of the IEEE International Conference on Communication Workshop (ICCW), London, UK, 8–12 June 2015, pp. 555–561 (2015)

    Google Scholar 

  10. Mitola, J., Maguire, G.Q.: Cognitive radio: making software radios more personal. IEEE Pers. Commun. 6(4), 13–18 (1999)

    Article  Google Scholar 

  11. Mitola Iii, J.: Cognitive radio for flexible mobile multimedia communications. Mob. Netw. Appl. 6(5), 435–441 (2001)

    Google Scholar 

  12. Akyildiz, I.F., Lee, W.-Y., Vuran, M.C., et al.: A survey on spectrum management in cognitive radio networks. IEEE Commun. Mag. 46(4), 40–48 (2008)

    Google Scholar 

  13. Saber, M., El Rharras, A., Saadane, R., et al. : Artificial neural networks, support vector machine and energy detection for spectrum sensing based on real signals. Int. J. Commun. Netw. Inf. Secur. 11(1), 52–60 (2019)

    Google Scholar 

  14. El Rharras, A., El Moukhlis, S., Saadane, et al.: FPGA-based fully parallel PCA-ANN for spectrum sensing. Comput. Inf. Sci. 8(1), 108 (2015)

    Google Scholar 

  15. Saber, M., Elrharras, A., Saadane, R., et al.: Transmit-power and interference control algorithm in cognitive radio network based on non-cooperative game theory. In: The Proceedings of the Third International Conference on Smart City Applications, pp. 647–662. Springer, Cham (2019)

    Google Scholar 

  16. Rong, Z., Rappaport, T.S.: Wireless Communications: Principles and Practice, Solutions Manual. Prentice Hall (1996)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Mohammed, S., Chehri, A., Yassine, E.H., Rachid, S., Mohammed, W., Hatim, K.A. (2021). Hybrid OSA-CSA Model for an Efficient Dynamic Spectrum Access in Cognitive Radio Environments. In: Zimmermann, A., Howlett, R.J., Jain, L.C., Schmidt, R. (eds) Human Centred Intelligent Systems . KES-HCIS 2021. Smart Innovation, Systems and Technologies, vol 244. Springer, Singapore. https://doi.org/10.1007/978-981-16-3264-8_13

Download citation

Publish with us

Policies and ethics