Towards Cognitive Radios

Getting the Best Out of the Radio and the Spectrum
Part of the Series on Integrated Circuits and Systems book series (ICIR)

In parallel with the need for cost- and energy-efficient reconfigurable radio implementations, there is as a matter of fact also a growing need to make next-generation terminals more intelligent and adaptive. Through appropriate radio management, these terminals should make flexible and efficient use of network/spectrum resources, so as to enable connectivity across complex and spectrum-constrained wireless networking environments. This has lead to the concept of cognitive radio. In this chapter we will preview on how SDRs are crucial to realize cognitive radios, and will explain the specific features that will need to be added. Also, we will give a glance on how further integration will make SDRs even more attractive for a wide range of wireless systems in the future.


Primary User Secondary User String Topology Cognitive Wireless Network Dynamic Spectrum Access 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    A. Dejonghe, B. Bougard, S. Pollin, L. Van der Perre, and F. Catthoor, Green Reconfig-urable Radio Systems: Creating and Managing Flexibility to Overcome Battery and Spectrum Scarcity, IEEE Signal Processing Magazine, special issue on Resource-Constrained Signal Processing, Communications, and Networking, Vol. 24, No. 3, May 2007.Google Scholar
  2. 2.
    S. Pollin, M. Ergen, A. Dejonghe, L. Van der Perre, F. Catthoor, I. Moerman, and A. Bahai, Distributed cognitive coexistence of 802.15.4 with 802.11 Crowncom 2006, Mykonos, Greece.Google Scholar
  3. 3.
    M. Timmers, A. Dejonghe, L. Van der Perre, and F. Catthoor, A Distributed Multichannel MAC for Cognitive Radio Networks with Primary User Recognition, presented at CrownCom 2007.Google Scholar
  4. 4.
    S. Pollin, Coexistence and Dynamic Sharing in Cognitive Radio Networks, to appear in Cognitive Wireless Communication Networks, ISBN: 979-0-387-68830.5.Google Scholar
  5. 5.
    FCC, Report of the Spectrum Efficiency Working Group.Google Scholar
  6. 6.
    Radio spectrum policy group opinion on Wireless Access Policy for Electronic Communications Services.Google Scholar
  7. 7.
    C. Cordeiro, K. Challapali, D. Birru, and S. Shankar, IEEE 802.22: An introduction to the first wireless standard based on cognitive radios, Journal of Communications, Vol. 1, No. 1, pp. 38–47, April 2006.Google Scholar
  8. 8.
    C. Cordeiro, M. Ghosh, D. Cavalcanti, and K. Challapali, Spectrum Sensing for Dynamic Spectrum Access of TV Bands, Proceedings of Crowncom, 2007, Orlando, FL.Google Scholar
  9. 9.
    K. Challapali, C. Cordeiro, and D. Birru, Evolution of Spectrum-Agile Cognitive Radios: First Wireless Internet Standard and Beyond, Proceedings of WICON, 2006, Boston, MA.Google Scholar
  10. 10.
    S. Sengupta, S. Brahma, M. Chatterjee, and S. Shankar N, Enhancements to cognitive radio based IEEE 802.22 air-interface, ICC 2007, Glasgow, UK.Google Scholar
  11. 11.
    C. Cordeiro, K. Challapali, D. Birru, and S. Shankar, IEEE 802.22: The first worldwide wireless standard based on cognitive radios, Proceedings of DySpan 2005, Baltimore, MA.Google Scholar
  12. 12.
    S. Haykin, Cognitive radio: Brain-empowered wireless communications, IEEE Journal on Selected Areas in Communications, Vol. 23, No. 2, pp. 201–220, 2005.CrossRefGoogle Scholar
  13. 13.
    J. Mitola et al., Cognitive radio: Making software radios more personal, IEEE Personal Communications, Vol. 6, No. 4, pp. 13–18, Aug. 1999.CrossRefGoogle Scholar
  14. 14.
    R. Brodersen, A. Wolisz, D. Cabri, S.M. Mishra, and D. Willkomm, A cognitive radio approach for usage of virtual unlicensed spectrum, CORVUS White Paper, July 2004.Google Scholar
  15. 15.
    Y. Xing, R. Chandramoulil, S. Mangold, and S.S.N, Dynamic spectrum access in open spectrum wireless networks, IEEE Transactions on Selected Areas in Communications, Vol. 24, No. 3, pp. 626–637, 2006.Google Scholar
  16. 16.
    C.-H. Lee and C.J. Yu, An Intelligent Handoff Algorithm for Wireless Communication Systems Using Grey Prediction and Fuzzy Decision System, Proceedings of the 2004 IEEE International Conference on Networking, Sensing & Control, Taipei, Taiwan, 2004.Google Scholar
  17. 17.
    C. Prehofer, N. Nafisi, and Q. Wei, A framework for context-aware handover decisions, Proceedings of PIMRC, Bejing, China, 2003.Google Scholar
  18. 18.
    L. Dimopoulou, G. Leoleis, and I.O. Venieris, Fast handover support in a WLAN environment: challenges and perspectives, IEEE Network, 2005, Vol. 19, No. 3, May-June 2005, pp. 14–20.Google Scholar
  19. 19.
    W. Zhang, J. Jaehnert, and K. Dolzer, Design and evaluation of a handover decision strategy for 4th generation mobile networks, Proceedings of VTC spring 2003, Jeju, Korea, April 2003, 2003.Google Scholar
  20. 20.
    Q. Wei, K. Farkas, P. Mendes, C. Prehofer, B. Plattner, and N. Nafisi, Context-Aware Handover Based on Active Network Technology, In Proceedings of the Fifth Annual International Working Conference on Active Networks (IWAN 2003). Lecture Notes in Computer Science, Springer Verlag, Kyoto, Japan, 2003.Google Scholar
  21. 21.
    K. Murray and D. Pesch, Intelligent network access and inter-system handover control in heterogeneous wireless networks for smart space environments, 1st International Symposium on Wireless Communication Systems, Mauritius, Sept. 2004.Google Scholar
  22. 22.
    O. Ormond, J. Murphy, and G.-M. Muntean, Utility-based intelligent network selection in beyond 3G systems, Proceedings of ICC, 2005, Seoul, Korea.Google Scholar
  23. 23.
    M. Kassar, B. Kervella, and G. Pujolle, Architecture of an intelligent inter-system handover management scheme, Future Generation Communication and Networking, Vol.1, pp. 332–337, 2007.Google Scholar
  24. 24.
    L.D. Chou, W.C. Lai, C.H. Lin, Y.C. Lin, and C.M. Huang, Seamless handover in WLAN and cellular networks through intelligent agents, Journal of Information Science and Engineering, Vol. 23, No. 4, pp. 1087–1101, 2007.Google Scholar
  25. 25.
    H. Celebi and H. Arslan, Utilization of location information in cognitive wireless networks, IEEE Wireless Communication Magazine-Special Issue on Cognitive Wireless Networks, Vol. 14, No. 4, pp. 6–13, August 2007.Google Scholar
  26. 26.
    M. Buddhikot, Understanding dynamic, spectrum access: Models, taxonomy and challenges, Proceedings of Dyspan 2007, Dublin, pp. 649–663, 2007.Google Scholar
  27. 27.
    Q. Zhao, A survey of dynamic spectrum access. Signal processing, networking and regulatory policy, Signal Processing Magazine, May 2007.Google Scholar
  28. 28.
    W. Gardner, Exploitation of spectral redundancy in cyclostationary signals, IEEE Signal Processing Magazine, Vol. 8, No. 2, pp. 14–32, April 1991.Google Scholar
  29. 29.
    J.G. Proakis, Digital Communications, 4th ed., McGraw Hill, New York, 2001.Google Scholar
  30. 30.
    M.I. Skolnik, Radar Handbook, McGraw-Hill, New York, 1990.Google Scholar
  31. 31.
    F. Hlawatsch and G.F. Boudreaux-Bartels, Linear and quadratic time-frequency signal representation, IEEE Signal Processing Magazine, Vol. 9, No. 2, pp. 21–67, April 1992.CrossRefGoogle Scholar
  32. 32.
    J. O'Neill and W.J. Williams, A function of time, frequency, lag, and Doppler, IEEE Transactions on Signal Processing, Vol. 47, No. 3, pp. 789–799, March 1999.CrossRefGoogle Scholar
  33. 33.
    H. Darabi, A blocker filtering technique for SAW-less wireless receivers, IEEE Journal of Solid-State Circuits, Vol. 42, No. 12, December 2007.Google Scholar
  34. 34.
    J. Craninckx and S. Donnay, 4g terminals: How are we going to design them? Proceedings Design Automation Conference, Anaheim, CA, pp. 79–84, 2003.Google Scholar
  35. 35.
    N. Vun and A.B. Premkumar, Adc systems for sdr digital front-end, Proceedings of the Ninth International Symposium on Consumer Electronics, Macau, pp. 359–363, 2005.Google Scholar
  36. 36.
    P.B. Kenington and L. Astier, Power consumption of a/d converters for software radio applications, IEEE Transactions on Vehicle Technology, Vol. 49, No. 2, pp. 643–650, March 2000.CrossRefGoogle Scholar
  37. 37.
    J.G. Proakis and D.G. Manolakis, Digital signal processing: Principles, algorithms, and applications, Prentice-Hall, Upper Saddle River, NJ, 1996.Google Scholar
  38. 38.
    R.G. Vaughan, N.L. Scott, and D. Rod White, The theory of bandpass sampling, IEEE Transactions on Signal Processing, Vol. 39, No. 9, pp. 1973–1984, September 1991.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Personalised recommendations