Skip to main content
Log in

Improving Water-Filling Algorithm to Power Control Cognitive Radio System Based Upon Traffic Parameters and QoS

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Cognitive radio scenario is based upon the ability to determine the radio transmission parameters from its surrounding environment. Power allocation in cognitive radio systems improves secondary network capacity subject to primary receiver interference level threshold. In this paper, statistical property of the injected interference power in primary user channel is used to establish the container bottom for each subcarrier employing water filling algorithm. In other words, the container bottom level of each subcarrier depends on the injected interference in primary user (PU) (most probably from the overloaded neighbor subcarriers). Traffic statistical parameters are also employed to formulate power allocation problem. Within this context, quality of service constraint is considered also to improve performance of power allocation algorithm. Simulation Results show that the injected interference in PU is decreased while the secondary user capacity improves. Indeed, the proposed algorithm is more compatible than a waterfilling algorithm with cognitive radio system constraints.

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

Access this article

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. Hossain E., Bhargava V. (2007) Cognitive wireless communication network. Springer Science publication, Berlin

    Book  Google Scholar 

  2. Mitola J. (2009) Cognitive radio technology. Elsevier publication, Amsterdam

    Google Scholar 

  3. Xiao, Y., & Hu, F. (2009). Cognitive radio networks. Boca Raton, London and New York: CRC press.

  4. Zhao Q., Sadler B. M. (2007) A survey of dynamic spectrum access: signal processing, networking, and regulatory policy. In IEEE Signal Processing Magazine, 24(3): 79–89

    Article  Google Scholar 

  5. Khozeimeh F., Haykin S. (2009) Spectrum management for cognitive radio: An overview. Wireless Communications and Mobile Computing 9: 1447–1459

    Article  Google Scholar 

  6. Mamoud A., Yucek T., Arsalan H. (2009) OFDM for cognitive radio: Merits and challenges. IEEE Wireless Communications. 16(2): 6–15

    Article  Google Scholar 

  7. Kang X., Liang Y., Nallanathn 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 

  8. Xiao Y., Bi G., & Niyato D. (2011) A simple distributed power control algorithm for cognitive radio networks. IEEE Transactions on Wireless Communications. 10(11): 3594–3600

    Article  Google Scholar 

  9. Sun C., Alemseged Y., Tran H., Harada H. (2010) Transmit power control for cognitive radio over a Rayleigh fading channel. IEEE Transactions on Vehicular Technology. 59(4): 1847–1857

    Article  Google Scholar 

  10. Hoang, A. T., & Liang, Y. C. (2006). A two-phase channel and power allocation scheme for cognitive radio networks. In Proceedings on international symposium personal, indoor, and mobile radio communications (PIMRC ’06), Sept. 2006.

  11. Hoang, A. T., & Liang, Y. C. (2006). Maximizing spectrum utilization of cognitive radio networks using channel allocation and power control. In Proceedings of 64th IEEE vehicular technology conference, Sept. 2006.

  12. Wu, J., Yang, L., & Liu, X. (2011). Resource allocation based on linear waterfilling algorithm in CR systems, IEEE 2011. In 7th international conference on wireless communications, networking and mobile computing (WiCOM).

  13. Zhang Y., Leung C. (2009) Resource allocation in an OFDM-based cognitive radio system. IEEE Transactions on Communications. 57(7): 1928–1931

    Article  Google Scholar 

  14. Wang, P., Zhong, X., Xiao, L., Zhou, S., & Wang, J. (2009). A genral power allocation algorithm for OFDM-based cognitive radio systems. In IEEE international conference on communications.

  15. Alsawah, A., & Fijalkow, I. (2007). Weighted sum-rate maximization in multiuser-OFDM systems under differentiated quality-ofservice constraints. In 8th IEEE workshop on signal processing advances for wireless communications (pp. 1–5), June 2007.

  16. Tang Z., Wei G., Zhu Y. (2009) Weighted sum rate maximization for OFDM-based cognitive radio systems. Telecommunications and Systems. 42: 77–84

    Article  Google Scholar 

  17. Wang, P., Zhao, M., Xiao, L., Zhou, S, & Wang, J. (2007). Power allocation in OFDM-based cognitive radio. In Proceedings of IEEE global telecommunications conference (pp. 4061–4065), Washington, DC, Nov. 2007.

  18. Hasan, Z., Hossain, E., Despins, C., & Bhargava, K. (2008). Power allocation for cognitive radio based on primary user activity in an OFDM system. In IEEE global telecommunications conference.

  19. Sun, D., Zheng, B., Xu, X., Cui, J., & Tang, S. (2010). An improved single user bit allocation algorithm based on cognitive OFDM. InIWCMC’10, June 2010.

  20. Bansal G., Hossain M. J., Bhargava V. K. (2011) Adaptive power loading for OFDM-based cognitive radio systems with statistical interference constraint. IEEE Transactions on Wireless Communications 10(9): 2786–2791

    Article  Google Scholar 

  21. Kushwaha H., Xing Y. (2007) Reliable multimedia transmission over cognitive radio networks using fountain codes. In Proceeding of the IEEE. 96(1): 155–165

    Article  Google Scholar 

  22. Halunga S. V., Vizireanu D. N., Fratu O. (2010) Imperfect cross-correlation and amplitude balance effects on conventional multiuser decoder with turbo encoding. Digital Signal Processing, Elsevier 20(1): 191–200

    Article  Google Scholar 

  23. Halunga S. V., Vizireanu D. N. (2010) Performance evaluation for conventional and MMSE multiuser detection algorithms in imperfect reception conditions. Digital Signal Processing, Elsevier 20(1): 166–178

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Elham Hosseini.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Hosseini, E., Falahati, A. Improving Water-Filling Algorithm to Power Control Cognitive Radio System Based Upon Traffic Parameters and QoS. Wireless Pers Commun 70, 1747–1759 (2013). https://doi.org/10.1007/s11277-012-0778-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-012-0778-0

Keywords

Navigation