Power Allocation and Spectrum Sharing in Multi-User, Multi-Channel Systems

  • Ali Kakhbod
Part of the Springer Theses book series (Springer Theses)


As wireless communication devices become more pervasive, the demand for the frequency spectrum that serves as the underlying medium grows. Traditionally, the problem of allocating the resource of the frequency spectrum has been handled by granting organizations and companies licenses to broadcast at certain frequencies. This rigid approach leads to significant under-utilization of this scarce resource. Moreover, frequency utilization varies significantly with time and location. A cognitive radio is a wireless communication device that is aware of its capabilities, environment, and intended use, and can also learn new waveforms, models, or operational scenarios [1]. Recently, the Federal Communications Commission (FCC) has established rules (see [2]) that describes how cognitive radios can lead to more efficient use of the frequency spectrum. These rules along with the cognitive radio’s features and the fact that information in the wireless network is decentralized and users may be strategic give rise to a wealth of important and challenging research issues associated with power allocation and spectrum sharing.


Cognitive Radio Power Allocation Federal Communication Commission Spectrum Sharing Power Profile 
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.


  1. 1.
    Neel JO, Reed JH, Gilles RP (2004) Convergence of cognitive radio networks. In: Proceedings of wireless communications and networking conferenceGoogle Scholar
  2. 2.
    FCC (2003) Facilitating opportunities for flexible, efficient, and reliableS spectrum use employing cognitive radio technologies, (ET docket no. 03–108). Technical report, FCC, WashingtonGoogle Scholar
  3. 3.
    Etkin R, Parekh A, Tse D (2007) Spectrum sharing for unlicensed bands. IEEE J Sel Areas Commun 25(3):517–528CrossRefGoogle Scholar
  4. 4.
    Yu W, Ginis G, Cioffi JM (2002) Distributed multiuser power control for digital subscriber lines. IEEE J Sel Areas Commun 20(5):1105–1115CrossRefGoogle Scholar
  5. 5.
    Huang J, Berry R, Honig ML (2006) Distributed interference compensation for wireless networks. IEEE J Sel Areas Commun 24(5):1074–1084CrossRefGoogle Scholar
  6. 6.
    Tekin C, Liu M, Southwell R, Huang J, Ahmad S (2012) Atomic congestion games on graphs and their applications in networking. IEEE/ACM Trans Netw (to appear)Google Scholar
  7. 7.
    Walker M (1981) A simple incentive compatible scheme for attaining Lindahl allocations. Econometrica 49(1):65–71MathSciNetMATHCrossRefGoogle Scholar
  8. 8.
    Hurwicz L (1979) Outcome functions yielding Walrasian and Lindahl allocations at Nash equilibrium points. Rev Econ Stud 46:217–225MathSciNetMATHCrossRefGoogle Scholar
  9. 9.
    Stoenescu T, Ledyard J (2008) Nash implementation for resource allocation network problems with production, manuscriptGoogle Scholar
  10. 10.
    Tian G (2010) Lecture notes microeconomic theory. On lineGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Electrical and System EngineeringUniversity of PennsylvaniaPhiladelphiaUSA

Personalised recommendations