Skip to main content
Log in

Techno-economic Comparison of Cognitive Radio and Software Defined Network (SDN) Cost Models in 5G Networks

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

5G is anticipated in 2020. In this generation of mobile networks, a great deal of requirements have been set. Although, there are many strong technologies in the telecommunications’ sector they do not respond to the 5G goals. On the other hand, telecommunication operators and providers do not want to invest in new equipment/architectures. Cognitive radio (CR) and software defined networking are two technologies with special and vigorous advantages. In this paper, several technical and economic models are developed. The CR is combined with the Stackelberg game. A sensitivity analysis is implemented and the parameters that impact mostly on the model are pinpointed. It is shown that the CR technology could offer all its fundamental cognitive advantages and even financial profits to the telecommunication companies.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27

Similar content being viewed by others

References

  1. Description, M. K. (2017). Nolle: Cost per bit exceeds revenues. https://www.lightreading.com/business-employment/business-transformation/nolle-in-2017-cost-per-bit-exceeds-revenues/d/d-id/729446

  2. Akyildiz, I. F., Nie, S., Lin, S.-C., & Chandrasekaran, M. (2016). 5G roadmap: 10 key enabling technologies. Computer Networks, 106, 17–48.

    Article  Google Scholar 

  3. Sharma, P. (2013). Evolution of mobile wireless communication networks-1g to 5g as well as future prospective of next generation communication network. International Journal of Computer Science and Mobile Computing, 2(8), 47–53.

    Google Scholar 

  4. Smail, G., & Weijia, J. (2017). Techno-economic analysis and prediction for the deployment of 5g mobile network. In 20th Conference on innovations in clouds, internet and networks (ICIN) (pp. 9–16). IEEE.

  5. Von Stackelberg, H. (2010). Market structure and equilibrium. New York: Springer.

    MATH  Google Scholar 

  6. Fomin, V. V., & Medeisis, A. (2015). Co-evolutionary analysis of cognitive radio systems. In Modern trends surrounding information technology standards and standardization within organizations (pp. 107–124). IGI Global. https://www.vdu.lt/cris/handle/20.500.12259/40861

  7. Numan, P. E., Yusof, K. M., Suleiman, D. U., Bassi, J. S., Yusof, S. K. S., & Din, J. B. (2016). Hidden node scenario: A case for cooperative spectrum sensing in cognitive radio networks. Indian Journal of Science and Technology, 9(46). http://52.172.159.94/index.php/indjst/article/view/107145/76099

  8. Medeisis, A., & Delaere, S. (2011). High-level scenarios for the future of cognitive radio business. In IEEE 22nd international symposium on personal indoor and mobile radio communications (PIMRC) (pp. 2330–2334). IEEE.

  9. Barrie, M., Tytgat, L., Gonçalves, V., Yaron, O., Moerman, I., Demeester, P., Pollin, S., Ballon, P., & Delaere, S. (2011). Techno-economic evaluation of cognitive radio in a factory scenario. In International conference on research in networking (pp. 52–61). Springer.

  10. Xie, R., Yu, F. R., Ji, H., & Li, Y. (2012). Energy-efficient resource allocation for heterogeneous cognitive radio networks with femtocells. IEEE Transactions on Wireless Communications, 11(11), 3910–3920.

    Article  Google Scholar 

  11. Liang, Y. C., Chen, K. C., Li, G. Y., & Mahonen, P. (2011). Cognitive radio networking and communications: An overview. IEEE Transactions on Vehicular Technology, 60, 3386–3407.

    Article  Google Scholar 

  12. Preet, A., Kaur, A., & Sahib, S. F. (2014). Review paper on cognitive radio networking and communications. International Journal of Computer Science and Information Technologies, 5(4), 5508–5511.

    Google Scholar 

  13. Haykin, S., et al. (2005). Cognitive radio: Brain-empowered wireless communications. IEEE Journal on Selected Areas in Communications, 23(2), 201–220.

    Article  Google Scholar 

  14. Bouras, C., Kollia, A., & Papazois, A. (2017). SDN & NFV in 5g: Advancements and challenges. In 20th Conference on innovations in clouds, internet and networks (ICIN) (pp. 107–111). IEEE.

  15. Bouras, C., Ntarzanos, P., & Papazois, A. (2016). Cost modeling for SDN/NFV based mobile 5g networks. In 8th International congress on ultra modern telecommunications and control systems and workshops (ICUMT) (pp. 56–61).

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Christos Bouras.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bouras, C., Kollia, A. & Maligianni, E. Techno-economic Comparison of Cognitive Radio and Software Defined Network (SDN) Cost Models in 5G Networks. Wireless Pers Commun 114, 1403–1430 (2020). https://doi.org/10.1007/s11277-020-07426-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-020-07426-8

Keywords

Navigation