Wireless Personal Communications

, Volume 63, Issue 1, pp 1–30 | Cite as

Utility-Aware Cognitive Network Selections in Wireless Infrastructures

  • V. Stavroulaki
  • D. Petromanolakis
  • P. DemestichasEmail author


Operators of wireless infrastructures should maintain their users “always-best-connected”. This concept means that applications should be offered to users at the best possible Quality of Service (QoS) level, taking into account profile, context and policy information. The profiles provide the user requirements and preferences, the terminal capabilities, and the application requirements. The policies provide the objectives, constraints imposed by various stakeholders, for instance the network operator (NO). The context of operation designates relevant applications, available networks and their QoS capabilities. The “always-best-connectivity” concept can be achieved by directing user terminals to the most appropriate networks of the heterogeneous infrastructure of the NO. In this respect, advanced terminal management functionality is required. This paper presents management mechanisms for utility-based cognitive network selections. The utility is used for expressing the user desire for a QoS level. Cognition mechanisms are applied for learning the QoS capabilities of candidate networks, and therefore increasing the reliability and seamlessness of the network selections. Extensive results are provided, which show the behaviour of the scheme in terms of network selections made, and computational effort required for the acquisition of the knowledge.


Heterogeneous infrastructures Terminal management functionality Cognitive systems Always best connectivity 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Gustafsson E., Jonsson A.: Always best connected. IEEE Wireless Communications Magazine 10(1), 49–55 (2003)CrossRefGoogle Scholar
  2. 2.
    International Telecommunication Union. (2001). Telecommunications standardization bureau (ITU-T), communications quality of service: A framework and definitions, Recommendation G1000.Google Scholar
  3. 3.
    International Telecommunications Union. (2003). Telecommunications standardization bureau (ITU-T), end-user multimedia QoS categories, Recommendation G1010.Google Scholar
  4. 4.
    Wireless World Research Forum (WWRF). (2010).
  5. 5.
    Project End-to-End Efficiency (E3). (2009)., 7th Framework Programme (FP7) of the European Commission, Information and Communication Technologies (ICT).
  6. 6.
    Third (3rd) Generation Partnership Project (3GPP). (2010). Web site
  7. 7.
    Institute of Electrical and Electronics Engineers (IEEE). (2010). 802 Standards,
  8. 8.
    WiMAX Forum. (2010).
  9. 9.
    Stavroulaki V., Buljore S., Roux P., Melin E.: Equipment management issues in B3G end-to-end reconfigurable systems. IEEE Wireless Communications Magazine 13(3), 24–32 (2006)CrossRefGoogle Scholar
  10. 10.
    Demestichas, P., Katidiotis, A., Petromanolakis, D., & Stavroulaki, V. Management system for terminals in the wireless B3G world. Accepted for publication in the Wireless Personal Communications Journal.Google Scholar
  11. 11.
    Song Q., Jamalipour A.: Network selection in integrated wireless LAN and UMTS environment using mathematical modelling and computing techniques. IEEE Wireless Communications Magazine 12(3), 42–48 (2005)CrossRefGoogle Scholar
  12. 12.
    Bari F., Leung V.: Automated network selection in a heterogeneous wireless network environment. IEEE Network 21(1), 34–40 (2007)CrossRefGoogle Scholar
  13. 13.
    Nguyen-Vuong Q. T., Agoulmine N., Ghamri-Doudane Y.: Terminal controlled mobility management in heterogeneous wireless networks. IEEE Communications Magazine 45(4), 122–129 (2007)CrossRefGoogle Scholar
  14. 14.
    Russell Stuart J., Norvig P.: Artificial intelligence: A modern approach. Prentice-Hall, New Jersey (2002)Google Scholar
  15. 15.
    Neapolitan, R. E. (2002). Learning Bayesian networks. Prentice Hall (series in artificial intelligence).Google Scholar
  16. 16.
    Jensen F.: Bayesian networks and decision graphs. Springer, New York (2001)zbMATHGoogle Scholar
  17. 17.
    Open Mobile Alliance (OMA.) (2010).
  18. 18.
    Strassner J., Btrabsner J.: Policy-based network management: Solution for the next generation. Elsevier, Amsterdam (2003)Google Scholar
  19. 19.
    Von Neumann J., Morgenstern O.: Theory of games and economic behaviour. Wiley, New York (1944)Google Scholar
  20. 20.
    Fishburn P.: Utility theory for decision making. Robert E. Krieger Publishing Co, Huntington, NY (1970)zbMATHGoogle Scholar
  21. 21.
    Mitola J., Maguire G. Q. Jr.: Cognitive radio: Making software radios more personal. IEEE Personal Communications 6(4), 13–18 (1999)CrossRefGoogle Scholar
  22. 22.
    Haykin S.: Cognitive radio: Brain-empowered wireless communications. IEEE Journal on Selected Areas In Communications 23(2), 201–220 (2005)CrossRefGoogle Scholar
  23. 23.
    Thomas R., Friend D., DaSilva L., McKenzie A.: Cognitive networks: Adaptation and learning to achieve end-to-end performance objectives. IEEE Communications Magazine 44(12), 51–57 (2006)CrossRefGoogle Scholar
  24. 24.
    Kephart J., Chess D.: The vision of autonomic computing. IEEE Computer 36(1), 41–50 (2003)CrossRefGoogle Scholar
  25. 25.
    Demestichas P., Boscovic D., Stavroulaki V., Lee A., Strassner J.: m@ANGEL: Autonomic management platform for seamless wireless cognitive connectivity. IEEE Communications Magazine 44(6), 118–127 (2006)CrossRefGoogle Scholar
  26. 26.
    Demestichas P., Dimitrakopoulos G., Strassner J., Bourse D.: Introducing reconfigurability and cognitive networks concepts in the wireless world: Research achievements and challenges. IEEE Vehicular Technology Magazine 1(2), 33–39 (2006)Google Scholar
  27. 27.
    (2007, August). Cognitive wireless networks. Special Issue in IEEE Wireless Communications Magazine, 14(4).Google Scholar
  28. 28.
    Stavroulaki, V., Demestichas, P., Katidiotis, A., & Petromanolakis, D. (2007, June). Evolution in equipment management concepts: from reconfigurable to cognitive wireless terminals. In Proceedings of 16th IST mobile and wireless communications summit, Budapest, Hungary.Google Scholar
  29. 29.
    Van Sinderen M. J., Van Halteren A. T., Wegdam M., Meeuwissen H. B., Eertink E. H.: Supporting context-aware mobile applications. IEEE Communications Magazine 44(9), 96–104 (2006)CrossRefGoogle Scholar
  30. 30.
    Bellavista P., Corradi A., Montanari R., Tononelli A.: Context-aware semantic discovery for next generation mobile systems. IEEE Communications Magazine 44(9), 62–71 (2006)CrossRefGoogle Scholar
  31. 31.
    Tsagkaris K, Tsagkaris A., Demestichas P.: Neural network-based learning schemes for cognitive radio systems. Computer Communications 31(14), 3394–3404 (2008)CrossRefGoogle Scholar
  32. 32.
    Demestichas P., Katidiotis A., Tsagkaris K., Adamopoulou E., Demestichas K.: Enhancing channel estimation in cognitive radio systems by means of Bayesian networks. Wireless Personal Communications Journal 49(1), 87–105 (2009)CrossRefGoogle Scholar
  33. 33.
    Liu X., Shankar N. S.: Sensing-based opportunistic channel access. Mobile Networks and Applications Journal 11(4), 577–591 (2006)CrossRefGoogle Scholar
  34. 34.
    Kim H., Shin K. G.: Efficient discovery of spectrum opportunities with MAC-layer sensing in cognitive radio networks. IEEE Transactions on Mobile Computing 7(5), 533–545 (2008)MathSciNetCrossRefGoogle Scholar
  35. 35.
    Perez-Romero, J., Sallent, O., Agusti, R., & Giupponi, L. (2007, April). A novel on-demand cognitive pilot channel enabling dynamic spectrum allocation. In Proceedings of 2nd international symposium on new frontiers in dynamic spectrum access networks 2007 (DySPAN 2007), Dublin, Ireland.Google Scholar
  36. 36.
    Nolan K., Doyle L.: Teamwork and collaboration in cognitive wireless networks. IEEE Wireless Communications Magazine 14(4), 22–27 (2007)CrossRefGoogle Scholar
  37. 37.
    Tirole J.: The theory of industrial organization. MIT Press, Cambridge, MA (1998)Google Scholar
  38. 38.
    Katoozian M., Navaie K., Yanikomeroglu H.: Utility-based adaptive radio resource allocation in OFDM wireless networks with traffic prioritization. IEEE Transactions on Wireless Communications 8(1), 66–71 (2009)CrossRefGoogle Scholar
  39. 39.
    Koutsorodi A., Adamopoulou E., Demestichas K., Theologou M.: Service configuration and user profiling in 4G terminals. Wireless Personal Communications 43(4), 1303–1321 (2007)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC. 2010

Authors and Affiliations

  • V. Stavroulaki
    • 1
  • D. Petromanolakis
    • 1
  • P. Demestichas
    • 1
    Email author
  1. 1.Department of Digital SystemsUniversity of PiraeusPiraeusGreece

Personalised recommendations