Applications and Services of Intelligent Spectrum Handover

  • Anandakumar Haldorai
  • Umamaheswari Kandaswamy
Part of the EAI/Springer Innovations in Communication and Computing book series (EAISICC)


Intelligent spectrum handover is a fundamental resource, which implies that its services are needed for various wireless network applications. The allotted spectrum has been subjected to an increased demand, thus making it nearly impossible to control the scarce spectrum, hence instigating that the spectrum allotment and utilization is minimal. Intelligent spectrum is discussed in this contribution as a proposed solution that enhances the utility of the spectrum, which resultantly aids in the mitigation of the spectrum scarcity. The contribution presents a spectrum instance that highlights the necessity for fewer techniques for enhancing the efficiency of the spectrum. Networks in cognitive radio fundamentally enhance the utility of the spectrum by permitting unlicensed individuals to embrace the opportunity of accessing the unutilized permitted spectrum. This paper further evaluates critical applications and challenges that determine how users can effectively assign unutilized slots in the spectrum without causing a significant impact on the permitted users. Moreover, the activities are launched without the need to move the present users to other bands of spectrum. Presenting the shift of the spectrum model and presenting a simulated annealing framework is illustrated in this contribution since it aids to proposing effecting mitigating factors during the spectrum shift. This further optimizes and aggregates the spectrum use and also assures the capabilities of constraints in the shift, leading to interference constraints and rating the fundamentals of the constraints. Lastly, this paper discusses the services of intelligent spectrum handovers and the relevant critical applications in the field.


Intelligent spectrum handover Cognitive radio Spectrum allotment Radio resource management Wireless communication Spectrum scarcity 


  1. 1.
    Khan, S., Mitschele-Thiel, A.: Hypernetworks based radio spectrum profiling in cognitive radio networks. EAI Endors. Trans. Cognit. Commun. 1(2), e5 (2015)CrossRefGoogle Scholar
  2. 2.
    Anandakumar, H., Umamaheswari, K.: An efficient optimized handover in cognitive radio networks using cooperative spectrum sensing. Intell. Autom. Soft Comput. 1–8 (2017)Google Scholar
  3. 3.
    Suganya, M., Anandakumar, H.: Handover based spectrum allocation in cognitive radio networks. In: 2013 International Conference on Green Computing, Communication and Conservation of Energy (ICGCE), Chennai, pp. 215–219 (2013)Google Scholar
  4. 4.
    Anandakumar, H., Umamaheswari, K.: Energy efficient network selection using 802.16g based GSM technology. J. Comput. Sci. 10(5), 745–754 (2014)CrossRefGoogle Scholar
  5. 5.
    Kuiper, D., Wenkstern, R.: Agent vision in multi-agent based simulation systems. Auton. Agent. Multi-Agent Syst. 29(2), 161–191 (2014)CrossRefGoogle Scholar
  6. 6.
    Tsuji, H., Tsukamoto, K., Suzuki, K., Nagayama, H.: Development of high-speed mobile radio communication systems using 40 GHz frequency band. Radio Sci. 51(7), 1220–1233 (2016)CrossRefGoogle Scholar
  7. 7.
    Liu, T., Shao, S., Ye, D., Tang, Y., Zhou, J.: Visual cognitive radio. Concurr. Comput. Pract. Exp. 24(11), 1252–1260 (2011)CrossRefGoogle Scholar
  8. 8.
    W Anandakumar, H., Arulmurugan, R., Onn, C.C.: Computational intelligence and sustainable systems. In: EAI/Springer Innovations in Communication and Computing (2019)Google Scholar
  9. 9.
    Wu, Y.: Localization algorithm of energy efficient radio spectrum sensing in cognitive internet of things radio networks. Cogn. Syst. Res. 52, 21–26 (2018)CrossRefGoogle Scholar
  10. 10.
    Su, H., Moh, S.: A directional cognitive-radio-aware MAC protocol for cognitive radio sensor networks. Int. J. Smart Home. 9(4), 239–250 (2015)CrossRefGoogle Scholar
  11. 11.
    Vizziello, A., Amadeo, R., Favalli, L.: Social cognitive cooperation for device to device communications. EAI Endors. Trans. Cognit. Commun. 3(11), 152557 (2017)CrossRefGoogle Scholar
  12. 12.
    Szydelko, M., Dryjanski, M.: 3GPP spectrum access evolution towards 5G. EAI Endors. Trans. Cognit. Commun. 3(10), 152184 (2017)CrossRefGoogle Scholar
  13. 13.
    Grace, D., Zhang, H., Nekovee, M.: Editorial: cognitive communications. IET Commun. 6(8), 783 (2012)CrossRefGoogle Scholar
  14. 14.
    Gurugopinath, S., Muralishankar, R., Shankar, H.: Spectrum sensing for cognitive radios through differential entropy. EAI Endors. Trans. Cognit. Commun. 2(6), 151147 (2016)CrossRefGoogle Scholar
  15. 15.
    Borra, D., Iori, M., Borean, C., Fagnani, F.: A reputation-based distributed district scheduling algorithm for smart grids. EAI Endors. Trans. Cognit. Commun. 1(2), e3 (2015)CrossRefGoogle Scholar
  16. 16.
    Guo, W., Huang, W.: Multicast communications in cognitive radio networks using directional antennas. Wirel. Commun. Mob. Comput. (2012)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Anandakumar Haldorai
    • 1
  • Umamaheswari Kandaswamy
    • 2
  1. 1.Department of Computer Science and EngineeringSri Eshwar College of EngineeringCoimbatoreIndia
  2. 2.Department of Information TechnologyPSG College of TechnologyCoimbatoreIndia

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