Wireless Personal Communications

, Volume 48, Issue 1, pp 33–47 | Cite as

Emerging, Self-Management Functionality for Introducing Cognition in the Wireless, B3G World

  • George Dimitrakopoulos
  • Panagiotis Demestichas
  • Kostas Tsagkaris
  • Aggelos Saatsakis
  • Klaus Moessner
  • Markus Muck
  • Didier Bourse
Article

Abstract

The unparalleled evolution of wireless communications is reflected in the tremendous investments on research and development, targeted at the continuous introduction of innovations that could serve the information society. This has led to the coexistence and complementary exploitation of versatile, legacy and also emerging Radio Access Technologies (RATs). At the same time, the continuously varying environment/users requirements impose the adaptation of those technologies to external stimuli, through reconfiguration (reconsideration) of their infrastructure and/or operating parameters. One feasible option to tackle the increased complexity of such environments, is to design wireless infrastructures with learning capabilities, thus forming cognitive networks. Cognitive networks are able to retain information from their interactions with the environment and intelligently adapt to any requirements. A prerequisite to facilitate operability of cognitive networks is the development of novel management mechanisms, which need to, distributively (centralized approaches would get even more complex), evaluate changes in external conditions and determine the way in which the network will properly respond to them. To this effect, this paper presents a complete framework under which Cognitive Access Points (CgAPs) could be managed and analyzes the functionality of its entities. Moreover, it also provides an approach for managing Cognitive Wireless Network Segments (CgWNSs).

Keywords

Cognitive networks Self-Management of Cognitive Access Points (SMCgAP) Self-Management of Cognitive Wireless Network Segments (SMCgWNS) 

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References

  1. 1.
    Third (3rd) Generation Partnership Project (3GPP) (2007). Web site, http://www.3gpp.org
  2. 2.
    Institute of Electrical and Electronics Engineers (IEEE) (2007). http://www.ieee802.org
  3. 3.
    WiMAX Forum (2007) http://www.wimaxforum.org
  4. 4.
    Digital Video Broadcasting (DVB) (2007). Web site, http://www.dvb.org
  5. 5.
    Bluetooth (2007). http://www.bluetooth.com
  6. 6.
    ZigBee Alliance (2007). http://www.zigbee.org
  7. 7.
    FP6/IST project E2R (End-to-End Reconfigurability) (2007). http://www.e2r.motlabs.com
  8. 8.
    Wireless World Research Forum (WWRF) (2007). http://www.wireless-world-research.org
  9. 9.
    Demestichas P., Boscovic D., Stavroulaki V., Lee A., Strassner J. (2006). m@ANGEL: Autonomic management platform for seamless wireless cognitive connectivity to the mobile Internet. IEEE Communications Magazine 44(6): 118–127CrossRefGoogle Scholar
  10. 10.
    Thomas, R., DaSilva, L., & MacKenzie, A. (2005). Cognitive networks. In Proceedings of the 1st IEEE Symposium on Dynamic Spectrum Access Networks 2005 (DySPAN 2005), Baltimore, USA, (pp. 352–360).Google Scholar
  11. 11.
    Strassner, J. (2005). Policy-based network management: Solutions for the next generation. Morgan Kaufmann (series in networking).Google Scholar
  12. 12.
    Mitchel, T. (1997). Machine learning. McGraw-Hill.Google Scholar
  13. 13.
    Neapolitan, R. E. (2002). Learning Bayesian networks. Prentice Hall (series in artificial intelligence).Google Scholar

Copyright information

© Springer Science+Business Media, LLC. 2008

Authors and Affiliations

  • George Dimitrakopoulos
    • 1
  • Panagiotis Demestichas
    • 1
  • Kostas Tsagkaris
    • 1
  • Aggelos Saatsakis
    • 1
  • Klaus Moessner
    • 2
  • Markus Muck
    • 3
  • Didier Bourse
    • 3
  1. 1.University of PiraeusPiraeusGreece
  2. 2.The University of SurreyGuilfordUK
  3. 3.Motorola LabsParisFrance

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