Skip to main content
SpringerLink
Log in

Performance Analysis of Cognitive Radio Multiple-Access Channels Over Dynamic Fading Environments

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

In dynamically changing environments, the spectrum-sharing method is a promising method to address the spectrum underutilization problem for cognitive radio (CR) systems. This paper investigates the capacity of cognitive radio multiple-access channel (CR-MAC) over a dynamic fading environment. Multiple secondary users (SUs) transmit to the secondary base station under the transmit power (TP) and interference temperature (IT) at the primary base station constraints. In order to perform a general analysis, a theoretical dynamic fading model termed hyper-fading model, which is suitable to the dynamic nature of cognitive radio channel, is considered. The optimal power allocation method is employed to maximize the capacity of CR-MAC for hyper-fading channel with TP and IT constraints and full channel side information. Through the numerical simulations, the capacity of the hyper-fading channels are compared with that of other channel fading models such as Rayleigh, Nakagami-2, and with an additive white Gaussian noise channel. Additionally, the impacts of the number of SUs on capacity is investigated.

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. Mitola J. III, Maguire G. Q. Jr. (1999) Cognitive radio: Making software radios more personal. IEEE Personal Communications 6(4): 13–18

    Article  Google Scholar 

  2. Ekin, S., Yilmaz, F., Celebi, H., Qaraqe, K. A., Alouini, M.-S., & Serpedin, E. (2009). Achievable capacity of a spectrum sharing system over hyper fading channels. In IEEE global communications conference (GLOBECOM), Honolulu, HI, 30 Nov–4 Dec 2009.

  3. Facilitating opportunities for flexible, efficient, and reliable spectrum use employing cognitive radio technologies. Federal Communications Commission (FCC), Technical report ET Docket no. 03-108, Mar 2005.

  4. Cognitive radio technology—a study for ofcom. http://www.ofcom.org.uk/research.

  5. Weiss T. A., Jondral F. K. (2004) Spectrum pooling: An innovative strategy for the enhancement of spectrum efficiency. IEEE Communications Magazine 42(3): 8–14

    Article  Google Scholar 

  6. Akyildiz I. F., Lee W. Y., Vuran M. C., Mohanty S. (2006) Next generation/dynamic spectrum access/cognitive radio wireless networks: A survey. Computer Networks 50(13): 2127–2159

    Article  MATH  Google Scholar 

  7. Haykin S. (2005) Cognitive radio: Brain-empowered wireless communications. IEEE Journal on Selected Areas in Communications 23(2): 201–220

    Article  Google Scholar 

  8. Ghasemi A., Sousa E. (2007) Fundamental limits of spectrum-sharing in fading environments. IEEE Transactions on Wireless Communications 6(2): 649–658

    Article  Google Scholar 

  9. Zhang R., Cui S., Liang Y.-C. (2009) On ergodic sum capacity of fading cognitive multiple-access and broadcast channels. IEEE Transactions on Information Theory 55(11): 5161–5178

    Article  MathSciNet  Google Scholar 

  10. Ji, J., Chen, W., Wan, H., & Liu, Y. (2010). Capacity analysis of multicast network in spectrum sharing systems. In IEEE international conference on communications (ICC), 2010 (pp. 1–5). IEEE.

  11. Suraweera H., Smith P., Shafi M. (2010) Capacity limits and performance analysis of cognitive radio with imperfect channel knowledge. IEEE Transactions on Vehicular Technology 59(4): 1811–1822

    Article  Google Scholar 

  12. Kolodzy P. (2006) Interference temperature: A metric for dynamic spectrum utilization. International Journal of Network Management 16(2): 103–113

    Article  Google Scholar 

  13. Gastpar, M. (2004). On capacity under received-signal constraints. In Proceedings of the 42nd annual allerton conference on communication, control and computing.

  14. Yang L., Alouini M. (2006) Performance analysis of multiuser selection diversity. IEEE Transactions on Vehicular Technology 55(6): 1848–1861

    Google Scholar 

  15. Yang L., Kang M., Alouini M. (2007) On the capacity-fairness tradeoff in multiuser diversity systems. IEEE Transactions on Vehicular Technology 56(4 Part 1): 1901–1907

    Article  Google Scholar 

  16. Ban T. W., Choi W., Jung B. C., Sung D. K. (2009) Multi-user diversity in a spectrum sharing system. IEEE Transactions on Wireless Communications 8(1): 102–106

    Article  Google Scholar 

  17. Ban T. W., Choi W., Sung D. K. (2009) Capacity and energy efficiency of multi-user spectrum sharing systems with opportunistic scheduling. IEEE Transactions on Wireless Communications 8(6): 2836–2841

    Article  Google Scholar 

  18. Simon M., Alouini M. (2004) Digital communication over fading channels (2nd ed.). Wiley-Interscience, New York

    Book  Google Scholar 

  19. Abdi A., Lau W., Alouini M., Kaveh M. (2003) A new simple model for land mobile satellite channels: First-and second-order statistics. IEEE Transactions on Wireless Communications 2(3): 519–528

    Article  Google Scholar 

  20. Alouini M., Goldsmith A. (2000) Adaptive modulation over Nakagami fading channels. Springer, Wireless Personal Communications 13(1): 119–143

    Article  Google Scholar 

  21. Boyd S., Vandenberghe L. (2004) Convex optimization. Cambridge University Press, Cambridge

    MATH  Google Scholar 

  22. Kang X., Liang Y., Nallanathan A., Garg H., Zhang R. (2009) Optimal power allocation for fading channels in cognitive radio networks: Ergodic capacity and outage capacity. IEEE Transactions on Wireless Communications 8(2): 940–950

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Texas A&M University at Qatar, Education City, Doha, Qatar

    Khalid A. Qaraqe

  2. Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA

    Sabit Ekin, Tarun Agarwal & Erchin Serpedin

Authors
  1. Khalid A. Qaraqe
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sabit Ekin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tarun Agarwal
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Erchin Serpedin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sabit Ekin.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Qaraqe, K.A., Ekin, S., Agarwal, T. et al. Performance Analysis of Cognitive Radio Multiple-Access Channels Over Dynamic Fading Environments. Wireless Pers Commun 68, 1031–1045 (2013). https://doi.org/10.1007/s11277-011-0497-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-011-0497-y

Keywords

Search

Navigation