Mobile Networks and Applications

, Volume 11, Issue 6, pp 847–860 | Cite as

Market Driven Dynamic Spectrum Allocation over Space and Time among Radio-Access Networks: DVB-T and B3G CDMA with Heterogeneous Terminals

  • Virgilio Rodriguez
  • Klaus Moessner
  • Rahim Tafazolli
Article

Abstract

The radio frequency spectrum is a naturally limited resource of extraordinary value, as the key to the provision of important communication and information services. Traditionally, spectrum has been allocated first to specific access technologies, and then sub-allocated to specific access networks, on very long term basis (up to decades). The traditional scheme can be very inefficient when demand patterns (“loads”) exhibit high temporal and spatial variations. Dynamic spectrum allocation (DSA) improves radio spectrum efficiency by adjusting the allocation as demand changes in time and/or space. In previous work, we introduced a DSA scheme in which a spectrum manager periodically auctions short-term spectrum licenses. The scheme can be supported by a realistic “pooling” business model, and can work with many radio-access technologies. But our previous analysis only considers a code-division multiple access (CDMA) technology; and DSA provides the greatest benefits with the participation of networks having complementary “busy hours,” such as video entertainment services and cellular telephony. Here, a digital video broadcast (DVB) terrestrial network joins the scheme. A typical DVB terrestrial cell is (much) larger than a UMTS cell. This brings to the forefront inter-cell interference, and inter-related auctions in different cells. To capture the essence of these issues we focus first on a situation where one DVB terrestrial cell overlays two adjacent CDMA cells. Subsequently we discuss extensions to richer scenarios. The contributions of the present work over our previous publications include to : (i) address the impact of inter-cell interference among several CDMA cells, (ii) introduce the DVB access technology into the DSA scheme, (iii) modify the auction scheme to consider that a DVB cell overlays several CDMA cells, (iv) characterise analytically the marketing and bidding behaviour of the DVB network.

Keywords

dynamic spectrum allocation (DSA) code-division multiple access (CDMA) digital video broadcast (DVB) auctions 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Binmore K, Klemperer P (2002) The biggest auction ever: The sale of the british 3G telecom licences. Econ J 112:74–96, MarchCrossRefGoogle Scholar
  2. 2.
    Rodriguez V, Moessner K, Tafazolli R (2005) Market-driven dynamic spectrum allocation: Optimal end-user pricing and admission control for CDMA. In: 14th IST mobile and wireless communications summit, Dresden, Germany, JuneGoogle Scholar
  3. 3.
    Rodriguez V, Moessner K, Tafazolli, R (2005) Auction driven dynamic spectrum allocation: Optimal bidding, pricing and service priorities for multi-rate, multi-class CDMA. In: 16th IEEE international symposium on personal, indoor and mobile radio communications, Berlin, Germany, SeptemberGoogle Scholar
  4. 4.
    Vickery W (1961) Counterspeculation, auctions and com- petitive sealed tenders. J Finance 16:8–37CrossRefGoogle Scholar
  5. 5.
    McMillan J (1994) Selling spectrum rights. J Econ Perspect 8:145–162Google Scholar
  6. 6.
    Rodriguez V, Moessner K, Tafazolli R (2006) Auction driven dynamic spectrum allocation over space and time: DVB-T and multi-rate, multi-class CDMA over a two-island geography. In: 15th IST mobile and wireless communications summit, Myconos, Greece, JuneGoogle Scholar
  7. 7.
    Leaves P, Ghaheri-Niri S, Tafazolli R, Christodoulides L, Sammut T, Staht W, Huschke, J (2001) Dynamic spectrum allocation in a multi-radio environment: Concept and algorithm. In: Second international conference on 3G mobile communication technologies, London, UK, pp. 53–57 MarchGoogle Scholar
  8. 8.
    Dinan E, Jabbari B (1998) Spreading codes for direct sequence CDMA and wideband CDMA cellular networks. IEEE Commun Mag 36:48–54, SeptemberCrossRefGoogle Scholar
  9. 9.
    First IEEE international symposium on new frontiers in dynamic spectrum access networks, Baltimore, Maryland, November 2005Google Scholar
  10. 10.
    Leaves P, Moessner K, Tafazolli R, Grandblaise D, Bourse D, Toenjes R, Breveglieri M (2004) Dynamic spectrum allocation in composite reconfigurable wireless networks. IEEE Commun Mag 42:72–81, MayCrossRefGoogle Scholar
  11. 11.
    Marcus MJ (2005) Real time spectrum markets and interruptible spectrum: New concepts of spectrum use enabled by cognitive radio. In: First IEEE international symposium on new frontiers in dynamic spectrum access networks, Baltimore, Maryland, pp. 512–517, NovemberGoogle Scholar
  12. 12.
    Dimitrakopoulos G, Demestichas P (2005) Spectrum exchange in a reconfigurable radio context. In: First IEEE international symposium on new frontiers in dynamic spectrum access networks, Baltimore, Maryland, pp. 676–679, NovemberGoogle Scholar
  13. 13.
    Buddhikot M, Kolodzy P, Miller S, Ryan K, Evans J (2005) DIMSUMnet: New directions in wireless networking using coordinated dynamic spectrum. In: Sixth IEEE international symposium on a world of wireless mobile and multimedia networks, Messina, Italy, pp. 78–85, JuneGoogle Scholar
  14. 14.
    Ileri O, Samandzija D, Sizer T, Mandayam N (2005) Demand responsive pricing and competitive spectrum allocation via a spectrum server. In: First IEEE international symposium on new frontiers in dynamic spectrum access networks, Baltimore, Maryland, pp. 194–202, NovemberGoogle Scholar
  15. 15.
    Weiss T, Jondral F (2004) Spectrum pooling: an innovative strategy for the enhancement of spectrum efficiency. IEEE Commun Mag 42:8–14, MarchCrossRefGoogle Scholar
  16. 16.
    Peha JM, Panichpapiboon S (2003) Real-time secondary markets for spectrum. In: 31st telecommunications policy research conference, Arlington, Virginia, SeptemberGoogle Scholar
  17. 17.
    Papadimitratos P, Sankaranarayanan S, Mishra A (2005) A bandwidth sharing approach to improve licensed spectrum utilization. IEEE Commun Mag 43:Sup10–Sup14, DecemberCrossRefGoogle Scholar
  18. 18.
    Maille P, Tuffin B (2004) Multibid auctions for bandwidth allocation in communication networks. In: INFOCOM. Twenty-third annual joint conference of the IEEE computer and communications societies, vol 1, Hong Kong, China, pp. 65–76, MarchGoogle Scholar
  19. 19.
    Maille P (2004) Auctioning for downlink transmission power in CDMA cellular systems. In: 7th ACM international symposium on modeling, analysis and simulation of wireless and mobile systems, Venice, Italy, OctoberGoogle Scholar
  20. 20.
    Kloeck C, Jaekel H, Jondral F (2005) Auction sequence as a new resource allocation mechanism. In: IEEE 62nd vehicular technology conference, vol 1, Dallas, Texas, pp. 240–244, FallGoogle Scholar
  21. 21.
    Huang J, Berry R, Honig M (2006) Auction-based spectrum sharing. Mob Netw Appl 11:405–418, JuneCrossRefGoogle Scholar
  22. 22.
    Peha JM (1998) Spectrum management policy options. IEEE Commun Surveys 1:2–8CrossRefGoogle Scholar
  23. 23.
    Youssef AM, Kalman E, Benzoni L (1995) Technico-economic methods for radio spectrum assignment. IEEE Commun Mag 33:88–94, JuneCrossRefGoogle Scholar
  24. 24.
    Webb W (1998) The role of economic techniques in spectrum management. IEEE Commun Mag 36:102–107, MarchCrossRefMathSciNetGoogle Scholar
  25. 25.
    Evci C, Fino B (2001) Spectrum management, pricing, and efficiency control in broadband wireless communications. Proc IEEE 89:105–115, JanuaryCrossRefGoogle Scholar
  26. 26.
    Spiller PT, Cardilli C (1999) Towards a property rights approach to communications spectrum. Yale J Regul 16:53–83Google Scholar
  27. 27.
    Noam EM (1995) Taking the next step beyond spectrum auctions: Open spectrum access. IEEE Commun Mag 33:66–73, DecemberCrossRefGoogle Scholar
  28. 28.
    Mitola III J, Maguire Jr GQ (1999) Cognitive radio: Making software radios more personal. IEEE Pers Commun 6(4):13–18CrossRefGoogle Scholar
  29. 29.
    Benkler Y (2002) Some economics of wireless communications. Harv J Law and Technol 16:25–83Google Scholar
  30. 30.
    Jones S, Levine P, Rickman N (2004) The economic of radio spectrum: Final report to OfCom. Technical Report, Department of Economics, University of Surrey, England, UK, JuneGoogle Scholar
  31. 31.
    Kloeck C, Martoyo I, Grandblaise D, Luo J, Moessner K, Sallent O, Dimitrakopoulos G, Demesticha P (2005) Functional architecture of reconfigurable systems. In: Wireless world research forum. 14th meeting WG6, San Diego, California, JulyGoogle Scholar
  32. 32.
    Clearwater SH (ed) (1996) Market-based control: A paradigm for distributed resource allocation. Singapore, World ScientificGoogle Scholar

Copyright information

© Springer Science + Business Media, LLC 2006

Authors and Affiliations

  • Virgilio Rodriguez
    • 1
  • Klaus Moessner
    • 1
  • Rahim Tafazolli
    • 1
  1. 1.CCSRThe University of SurreyGuildford, SurreyUK

Personalised recommendations