Skip to main content
SpringerLink
Log in

Capacity optimization through sensing threshold adaptation for cognitive radio networks

  • Original Paper
  • Published:
Optimization Letters Aims and scope Submit manuscript

Abstract

We propose capacity optimization through sensing threshold adaptation for sensing-based cognitive radio networks. The objective function of the proposed optimization is the maximization of the capacity at the secondary user subject to transmit power and sensing threshold constraints for protecting the primary user. After proving the concavity of capacity on sensing threshold, the problem is solved using the Lagrange duality decomposition method in conjunction with a subgradient iterative algorithm. The numerical results show that the proposed optimization can lead to significant capacity maximization for the secondary user as long as this is affordable to the primary user.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Haykin S.: Cognitive radio: Brain-empowered wireless communications. IEEE J. Sel. Areas Commun. 23(2), 201–220 (2005)

    Article  Google Scholar 

  2. Zhao Q., Sadler B.: A survey of dynamic spectrum access. IEEE Signal Process. Mag. 24(3), 79–89 (2007)

    Article  Google Scholar 

  3. Kang X., Liang Y.-C., Garg H.K., Zhang L.: Sensing-based spectrum sharing in cognitive radio networks. IEEE Trans. Veh. Tech. 58(8), 4649–4654 (2009)

    Article  Google Scholar 

  4. Musavian L., Aïssa S.: Capacity and power allocation for spectrum-sharing communications in fading channels. IEEE Trans Wirel. Commun. 8(1), 148–156 (2009)

    Article  Google Scholar 

  5. Xin K., Liang Y.-C., Nallanathan A., Garg H.K., Zhang R.: Optimal power allocation for fading channels in cognitive radio networks: ergodic capacity and outage capacity. IEEE Trans. Wirel. Commun. 8(2), 940–950 (2009)

    Article  Google Scholar 

  6. Oliveira, C., Pardalos, P.M.: A distributed optimization algorithm for power control in wireless ad hoc networks. In: IEEE 18th International Parallel and Distributed Processing Symposium (IPDPS’04), vol. 7, pp. 177–185. Santa Fe, New Mexico (2004)

  7. Choi, H., Jang, K., Cheong, Y.: Adaptive sensing threshold control based on transmission power in cognitive radio systems. In: 3rd International Conference on Cognitive Radio or Wireless Networks and Communication, CrownCom (2008)

  8. Digham F.F., Alouni M.-S., Simon M.K.: On the energy detection of unknown signals over fading channels. IEEE Trans. Commun. 55(1), 21–24 (2007)

    Article  Google Scholar 

  9. Zhang, R.: Optimal power control over fading cognitive radio channels by exploiting primary user CSI. In: Proceedings of the IEEE Global Communication Conference (Globecom), New Orleans, USA (2008)

  10. Chen Y., Zhao Q., Swami A.: Joint design and separation principle for opportunistic spectrum access in the presence of sensing errors. IEEE Trans. Inf. Theory 54(5), 2053–2071 (2008)

    Article  MathSciNet  Google Scholar 

  11. Stephen, B., Lieven, V.: Convex optimization. Cambridge University Press, London (2004). ISBN: 978-0-521-83378-3

  12. Nedić, A.: Subgradient methods for convex minimization. Ph.D. dissertation, MIT (2002)

  13. Andrea, G.: Wireless Communications. Campridge University Press, London (2005). ISBN: 978-0521837163

  14. Resende, M.G.C., Pardalos, P.M.: Handbook of Optimization in Telecommunications. Springer, New York (2006). ISBN: 9780387306629 (978-0387306629)

  15. Bertsekas, D.P.: Nonlinear programming, 2nd edn. Athena Scientific, Belmont (1995). ISBN: 1886529000 (1-886529-00-0)

  16. Pardalos, P.M., Resende, M.G.C.: Handbook of Applied Optimization. Oxford University Press, USA (2002). ISBN: 0195125940 (978-0195125948)

  17. Ribeiro A., Giannakis G.B.: Separation principles in wireless networking. IEEE Trans. Inf. Theory. 56(9), 4488–4505 (2010)

    Article  MathSciNet  Google Scholar 

  18. Palomar D.P., Chiang M.: A tutorial on decomposition methods for network utility maximization. IEEE J. Sel. Areas Commun. 24(8), 1439–1451 (2006)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Informatics and Telecommunications, National Kapodistrian University of Athens, Ilisia, Athens, Greece

    Fotis T. Foukalas & Lazaros F. Merakos

  2. Department of Information Technology and Telecommunications, TEI of Larissa, Larissa, Greece

    George T. Karetsos

Authors
  1. Fotis T. Foukalas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. George T. Karetsos
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lazaros F. Merakos
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to George T. Karetsos.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Foukalas, F.T., Karetsos, G.T. & Merakos, L.F. Capacity optimization through sensing threshold adaptation for cognitive radio networks. Optim Lett 6, 1499–1511 (2012). https://doi.org/10.1007/s11590-011-0345-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11590-011-0345-8

Keywords

Search

Navigation