Bio-inspired Cognitive Radio for Dynamic Spectrum Access

  • Giacomo Oliveri
  • Marina Ottonello
  • Carlo S. Regazzoni


Dynamic spectrum access (DSA) has raisedthe attention of industrial and academic researchers due to the fact thatit is seen as a technologyable to overcome the lack of available spectrum for new communication services.In particular, autonomic DSA (ADSA) systems are indicated as a solution to spectrumscarcity caused by the current “command and control” allocationparadigm. However, ADSA requires a higher level of reconfigurability with respect totraditional wireless systems. In this context, one of the technologies thatcan provide such flexibility is the promising cognitive radio (CR).In an ADSA scenario, CR should sense the spectrum to find the resources unused byprimary (licensed) users, which could then be exploited by secondary(unlicensed) CR users to increase the overall system efficiency.In this chapter, a comprehensive overview of CR applications to ADSA is carried out;in particular, attention is paid to the potentialities of autonomic bio-inspiredapproaches, and on their advantages in the solution of the challenges ofADSA systems.


Cognitive Radio Primary User Reinforcement Learning Federal Communication Commission Cognitive Radio User 
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.


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  1. 1.
    Adapt4 Inc (2008) XG1™ Cognitive Radio.
  2. 2.
    Akyildiz IF, Lee WY, Vuran MC, Mohanty S (2006) NeXt generation/dynamic spectrum access/cognitive radio wireless networks: a survey. Computer Networks 50:2127–2159MATHCrossRefGoogle Scholar
  3. 3.
    Anderson ML (2003) Embodied cognition: a field guide. Artificial Intelligence 149:91–130CrossRefGoogle Scholar
  4. 4.
    Baldo N, Zorzi M (2008) Learning and adaptation in cognitive radios usingneural networks. In: 5th IEEE Consumer Communications and NetworkingConference, pp 998–1003Google Scholar
  5. 5.
    Barbarossa S, Scutari G (2007) Bio-inspired sensor network design. IEEE SignalProcessing Magazine 44(3):26–35CrossRefGoogle Scholar
  6. 6.
    Bhargava KV, Hossain E (2007) Cognitive Wireless Communication Networks.Springer, BerlinGoogle Scholar
  7. 7.
    Bixio L, Oliveri G, Ottonello M, Raffetto M, Regazzoni CS (2007) Areinforcement learning approach to cognitive radio. In: Software DefinedRadio Technical Conference Proceedings, Denver, USAGoogle Scholar
  8. 8.
    Chapin JM, Doyle L (2007) A path forwards for cognitive radio research. In:Second International Conference on Cognitive Radio Oriented Wireless Networksand Communications, Orlando, USA, pp 127–132Google Scholar
  9. 9.
    Chapin JM, Lehr WH (2007) The path to market success for dynamic spectrumaccess technology. IEEE Comm Mag 45(5):96–103CrossRefGoogle Scholar
  10. 10.
    Clancy C, Hecker J, Stuntebeck E, O’Shea T (2007) Applications of machinelearning to cognitive radio networks. IEEE Wireless Communications14(4):47–52CrossRefGoogle Scholar
  11. 11.
    Cliff D (2003) Biologically-inspired computing approaches to cognitive systems:a partial tour of the literature. Tech. Rep. HPL-2003-11, Digital MediaSystems Laboratory, HP Laboratories, BristolGoogle Scholar
  12. 12.
    Cordeiro C, Daneshrad B, Evans J, Mandayam N, Marshall P, Shankar S (eds) (2007) Special issue on adaptive, spectrum agile, and cognitive wireless networks. IEEE J Sel Area Comm 25(3):513–516Google Scholar
  13. 13.
    Costa MHM (1983) Writing on dirty paper. IEEE Trans Inform Theory29(3):439–441MATHCrossRefMathSciNetGoogle Scholar
  14. 14.
    CRN Workshop (2008) 2nd IEEE International Workshop on Cognitive RadioNetworks.
  15. 15.
    CROWNCom (2008) International conference on cognitive radio oriented wirelessnetworks and communications.
  16. 16.
    Cybenko G, Berk VH, Gregorio-De Souza ID, Behre C (2006) Practical autonomiccomputing. In: Proceedings of the 30th Annual International Computer Softwareand Applications Conference, Washington, DC, USA, pp 3–14Google Scholar
  17. 17.
    Damasio A (1999) The Feeling of What Happens: Body and Emotion in the Making ofConsciousness. Harcourt Brace, San DiegoGoogle Scholar
  18. 18.
    De Castro LN, Von Zuben FJ (2005) Recent Developments in BiologicallyInspired Computing. Idea Group Publishing, New YorkGoogle Scholar
  19. 19.
    De Mello RF, Cuenca RG, Yang LT (2006) Genetic algorithms applied to organizewireless sensor networks aiming good coverage and redundancy. In: FirstInternational Conference on Communications and Networking in China, pp 1–5Google Scholar
  20. 20.
    Dobre O, Abdi A, Bar-Ness Y, Su W (2007) Survey of automatic modulationclassification techniques: classical approaches and new trends. IETCommunications 1(2):137–156Google Scholar
  21. 21.
    Dore A, Pinasco M, Regazzoni CS (2007) A bio-inspired learning approach for theclassification of risk zones in a smart space. In: Online Learning forClassification Workshop, Minneapolis, USA, pp 1–8,\urlprefix
  22. 22.
    Fehske A, Gaeddert J, Reed JH (2005) A new approach to signal classificationusing spectral correlation and neural networks. In: First IEEE InternationalSymposium on New Frontiers in Dynamic Spectrum Access Networks, pp 144–150Google Scholar
  23. 23.
    Fette BA (2006) Cognitive Radio Technology. Newnes, OxfordGoogle Scholar
  24. 24.
    Geirhofer S, Tong L, Sadler BM (2007) Dynamic spectrum access in the timedomain: modeling and exploiting white space. IEEE Comm Mag 45(5):66–72CrossRefGoogle Scholar
  25. 25.
    Han T, Kobayashi K (1981) A new achievable rate region for the interferencechannel. IEEE Trans Inform Theory 27(1):49–60MATHCrossRefMathSciNetGoogle Scholar
  26. 26.
    Haupt RL, Haupt SE (2004) Practical Genetic Algorithms, 2nd edn. Wiley, NewYorkMATHGoogle Scholar
  27. 27.
    Haykin S (2005) Cognitive radio: brain-empowered wireless communications. IEEEJ Sel Area Comm 23(2):201–220CrossRefGoogle Scholar
  28. 28.
    Haykin S (2006) Cognitive dynamic systems. Proceedings of theIEEE 94(11):1910–1911Google Scholar
  29. 29.
    Haykin S (2006) Cognitive radar: a way of the future. IEEE SigProc Mag 23(1):30–40CrossRefGoogle Scholar
  30. 30.
    Haykin S, Li G, Shafi M (eds) (2008) Special issue on Cognitive Radio,Proceedings of the IEEEGoogle Scholar
  31. 31.
    Ibrahim MT, Anthony RJ, Eymann T (2006) Exploring adaptation & self-adaptationin autonomic computing systems. In: Proceedings of the 17th InternationalWorkshop on Database and Expert Systems Applications, Los Alamitos, USA, pp129–138Google Scholar
  32. 32.
    IEEE 80216 License-Exempt (LE) Task Group (2008) Web site.
  33. 33.
    IEEE 80222 Working Group (2008) Web site.
  34. 34.
    IEEE Communications Society TCCN (2008) Web site.
  35. 35.
    IEEE Standards Coordinating Committee 41 (2008) Web site.
  36. 36.
    Ileri O, Mandayam N (2008) Dynamic spectrum access models: Toward anengineering perspective in the spectrum debate. IEEE Communication Magazine46(1):153–160CrossRefGoogle Scholar
  37. 37.
    Le B, Garcia P, Chen Q, Li B, Ge F, El Nainay M, Rondeau T, Bostian C (2007)A public safety cognitive radio node system. In: Software Defined RadioTechnical Conference Proceedings, Denver, USA, URL
  38. 38.
    Liang YC, Chen HH, Mitola J, Mahonen P, Kohno R, Reed JH (eds) (2008) Specialissue on cognitive radio theory and application, vol 26, IEEE J. Sel. AreaComm.Google Scholar
  39. 39.
    Miorandi D, Yamamoto L, Dini P (2006) Service evolution in bio-inspiredcommunication systems. International Transactions on Systems Science andApplications Journal 2(1):51–60Google Scholar
  40. 40.
    Mitchell TM (1997) Machine Learning. McGraw-Hill, New YorkMATHGoogle Scholar
  41. 41.
    Mitola J (1992) Software radios-survey, critical evaluation and futuredirections. In: National Telesystems Conference, pp 13/15–13/23Google Scholar
  42. 42.
    Mitola J (1995) The software radio architecture. IEEE Comm Mag 33(5):26–38CrossRefGoogle Scholar
  43. 43.
    Mitola J (2000) Cognitive radio: An integrated agent architecturefor software defined radio. PhD thesis, Royal Institute of Technology (KTH),SwedenGoogle Scholar
  44. 44.
    Mitola J (2000) Software Radio Architecture: Object-OrientedApproaches to Wireless Systems Engineering. Wiley, New YorkGoogle Scholar
  45. 45.
    Mitola J (2006) Cognitive Radio Architecture: The Engineering Foundations ofRadio XML. Wiley, New YorkCrossRefGoogle Scholar
  46. 46.
    Mitola J, Maguire GQ (1999) Cognitive radio: Making software radios morepersonal. IEEE Pers Commun 6(4):13–18CrossRefGoogle Scholar
  47. 47.
    Pfeifer R, Lungarella M, Iida F (2007) Self-organization, embodiment, andbiologically inspired robotics. Science 318(5853):1088–1093CrossRefGoogle Scholar
  48. 48.
    Reed JH (2002) Software Radio: A Modern Approach to Radio Engineering. PrenticeHall, New YorkGoogle Scholar
  49. 49.
    Regazzoni CS, Gandetto M (2007) Spectrum sensing: a distributed approach forcognitive terminals. IEEE J Sel Area Comm 25(3):546–557CrossRefGoogle Scholar
  50. 50.
    Renk T, Kloeck C, Burgkhardt D, Jondral FK, Grandblaise D, Gault S, Dunat JC(2007) Bio-inspired algorithms for dynamic resource allocation in cognitivewireless networks. In: International Conference on Cognitive Radio OrientedWireless Networks and Communications, Orlando, FL, USA, pp 351–356Google Scholar
  51. 51.
    Rieser CJ (2004) Biologically inspired cognitive radio engine model utilizingdistributed genetic algorithms for secure and robust wireless communicationsand networking. Phd thesis, Virginia State UniversityGoogle Scholar
  52. 52.
    Shared Spectrum Company (2008) Web site.
  53. 53.
    Spectrum Policy Task Force (2002) Report of the spectrumefficiency working group. Tech. rep., Federal Communications CommissionGoogle Scholar
  54. 54.
    Spectrum Policy Task Force (2002) Report of the spectrum rightsand responsibilities working group. Tech. rep., Federal CommunicationsCommissionGoogle Scholar
  55. 55.
    Srinivasa S, Jafar SA (2007) The throughput potential of cognitive radio: atheoretical perspective. IEEE Comm Mag 45(5):73–79CrossRefGoogle Scholar
  56. 56.
    Sterrit R, Bustard D (2003) Towards an autonomic computing environment. In:Proceedings of the 14th International Workshop on Database and Expert SystemsApplications, Prague, Czech Republic, pp 694–698Google Scholar
  57. 57.
    Sutton RS, Barto AG (1998) Reinforcement learning. The MIT Press, Cambridge,MassachusettsGoogle Scholar
  58. 58.
    Zhang H, Zhou X, Yazdandoost KY, Chlamtac I (2006) Multiple signal waveformsadaptation in cognitive ultra-wideband radio evolution. IEEE J Sel Area Comm24(4):878–884CrossRefGoogle Scholar
  59. 59.
    Zhao Q, Sadler BM (2007) A survey of dynamic spectrum access. IEEE Sig Proc Mag24(11):79–89CrossRefGoogle Scholar
  60. 60.
    Zhao Q, Tong L, Swami A (2005) Decentralized cognitive mac for dynamic spectrumaccess. In: Proceedings of the First IEEE International Symposium on NewFrontiers in Dynamic Spectrum Access Networks, Baltimore, USA, pp 224–232CrossRefGoogle Scholar

Copyright information

© Springer-Verlag US 2009

Authors and Affiliations

  • Giacomo Oliveri
    • 1
  • Marina Ottonello
    • 1
  • Carlo S. Regazzoni
    • 1
  1. 1.Department of Biophysical and Electronic EngineeringUniversity of Genova16145 GenovaItaly

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