Advertisement

From Self-Organizing to Cognitive Networks: How Can the Cellular Network Operator Make Use of the Cognitive Paradigm?

  • Berna Sayrac
Chapter
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 116)

Abstract

This chapter provides a compact view on the use of cognitive radio (CR) principles in cellular Radio Access Network (RAN) operation and management. This is achieved through a set of tangible operator-centric scenarios where cognitive features have the potential to bring benefits. These benefits are primarily due to a powerful enabling concept that increases the radio environmental awareness: Radio Environmental Maps (REMs). REMs are obtained by collecting and processing geo-localized measurements/observations reported by multiple network nodes/entities with the purpose of optimizing RAN management and operations like resource allocation/usage efficiency, coverage/capacity/Quality of Service (QoS) optimization. The REM-based operator-centric scenarios which are presented in this chapter provide concrete examples where the cellular operator can apply the cognitive paradigm on its radio networks and have potential benefits/opportunities which is translated into performance and OPerational EXpenditures (OPEX) gains. Apart from those scenarios, the chapter also presents the REM functional architecture together with a detailed REM system architecture mapped onto the existing 3GPP Long Term Evolution (LTE) RAN architecture for self-optimization and self-configuration of femtocells. The REM system architecture details provided by this chapter allows us to make cost calculations for each specific scenario once the requirements in terms of spatial and temporal REM updates are known. An example of signaling cost calculation for a specific measurement reporting message is given at the end of the chapter.

Keywords

Global Navigation Satellite System Global Navigation Satellite System Long Term Evolution User Equipment Mobile Terminal 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Mitola J III, Maguire GQ Jr (1999) Cognitive radio: making software radios more personal. IEEE Pers Commun Mag 6(4):13–18 AugustCrossRefGoogle Scholar
  2. 2.
    Magnusson P, Oom J (2001) An architecture for self-tuning cellular systems, Proceedings of the 2001 IEEE/IFIP international symposium on integrated network management, pp 231–245Google Scholar
  3. 3.
    Hoglund A, Valkealahti K (2004) Automated optimization of key WCDMA parameters. Wireless communication and mobile computing, published online 23 August in Wiley Interscience. doi: 10.1002/wcm.212)
  4. 4.
    3GPP TR 36.902, Evolved universal terrestrial radio access network (E-UTRAN); self configuration and self-optimization network use cases and solutions, Release 8, Sept 2008Google Scholar
  5. 5.
    Yuan G et.al (2010) Carrier aggregation for LTELTE-advanced mobile communication systems, IEEE Commun Mag 48 (2):88–93 Feb 2010Google Scholar
  6. 6.
  7. 7.
    The next step for location based services, White paper, Northstream, 2005. [Online]. Available: http://northstream.se/wp-content/uploads/2005/02/The-next-step-for-Location-Based-Services.pdf
  8. 8.
    Fette BA, Fette B (2006) Cognitive radio technology (communications engineering). Newnes, AmsterdamGoogle Scholar
  9. 9.
    Ben Hadj Alaya-Feki A, Sayrac B, Ben Jemaa S, Moulines E, (2008) Interference cartography for hierarchical dynamic spectrum access, Proceedings of DySPAN 2008Google Scholar
  10. 10.
    Grimoud S, Ben Jemaa S, Sayrac B, Moulines E (2010) A REM enabled soft frequency reuse scheme, Proceedings of Globecom 2010, BWA Workshop, Dec 2010Google Scholar
  11. 11.
    3GPP TS 37.320, Universal Terrestrial Radio Access (UTRA) and Evolved Universal Terrestrial Radio Access (E-UTRA); Radio measurement collection for minimization of drive tests (MDT); overall description; stage 2 (Release 10)Google Scholar
  12. 12.
    Ripley BD Spatial statistics, Wiley-InterscienceGoogle Scholar
  13. 13.
    Zhao Y et al (2007) Applying radio environment maps to cognitive wireless regional area networks, Proceedings of DySPAN 2007, pp 115–118Google Scholar
  14. 14.
    Zhao Y et al (2007) Development of radio environment map enabled case- and knowledge-based learning algorithms for IEEE 802.22 WRAN cognitive engines, Proceedings of CrownCom 2007, pp 44–49Google Scholar
  15. 15.
    Riihijarvi J et al (2008) Characterization and modelling of spectrum for dynamic spectrum access with spatial statistics and random fields, Proceedings of PIMRC 2008Google Scholar
  16. 16.
    Riihijarvi J et al (2009) Enhancing cognitive radios with spatial statistics: from radio environment maps to topology engine, Proceedings of CrownCom 2009Google Scholar
  17. 17.
    FARAMIR Document Number D2.2 Scenario Definitions, Aug 2010 [Online]. Available: http://www.ict-faramir.eu/fileadmin/user_upload/deliverables/FARAMIR-D2.2-Final-PU.pdf
  18. 18.
    Yang Y et al (2009) Relay technologies for WiMax and LTE LTE-advanced mobile systems, IEEE Comm Mag 47(10):100–105Google Scholar
  19. 19.
    3GPP TS 36.300, Evolved Universal Terrestrial Radio Access (E-UTRA) and Evolved Universal Terrestrial Radio Access Network (E-UTRAN); overall description; stage 2Google Scholar
  20. 20.
    Cai T et al (2011) Design of layered radio environment maps for RAN optimization in heterogeneous LTELTE systems, Proceedings of PIMRC 2011Google Scholar
  21. 21.
    3GPP TS 36.214, Evolved Universal Terrestrial Radio Access (E-UTRA); physical layer; measurementsGoogle Scholar
  22. 22.
    3GPP TS 36.331, Evolved Universal Terrestrial Radio Access (E-UTRA); radio resource control (RRC); protocol specificationGoogle Scholar
  23. 23.
    3GPP TS 25.331, Evolved Universal Terrestrial Radio Access (E-UTRA); radio resource control (RRC); protocol specificationGoogle Scholar
  24. 24.
    Baum DS et al (2005) An interim channel model for beyond-3 g systems: extending the 3 gpp spatial channel model (SCM), Proceedings of VTC-Spring 2005Google Scholar
  25. 25.
  26. 26.
    3GPP TS 36.211, Evolved Universal Terrestrial Radio Access (E-UTRA); physical channels and modulationGoogle Scholar
  27. 27.
    3GPP TS 36.355, Evolved Universal Terrestrial Radio Access (E-UTRA); LTELTE positioning protocol (LPP)Google Scholar
  28. 28.
    Grimoud S, Sayrac B, Ben Jemaa S, Moulines E (2011) An algorithm for fast REM construction, Proceedings of CrownCom 2011Google Scholar
  29. 29.
    Grimoud S, Sayrac B, Ben Jemaa S, Moulines E (2011) Best sensor selection for an iterative REM construction, accepted for publication in proceedings of VTC-Fall 2011Google Scholar
  30. 30.
    Ben Hadj Alaya-Feki A et al (2008) Informed spectrum usage in cognitive radio networks: interference cartography (invited paper), Proceedings of IEEE CRNETS, PIMRC 2008Google Scholar
  31. 31.
    Rappaport TS (2002) Wireless communications, principles and practice, 2nd edn. Prentice Hall, Uppler Saddle River chapter 4Google Scholar
  32. 32.
    Zhao Y et al (2006) Overhead analysis for radio environment map-enabled cognitive radio networks, Proceedings of 1st IEEE workshop on networking technologies for software defined radio networks, pp 18–25Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  1. 1.Orange LabsIssy les MoulineauxFrance

Personalised recommendations