Skip to main content

Advertisement

SpringerLink
Log in

A robust energy-efficient power control algorithm for cognitive radio networks

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

To achieve energy efficiency is very important for future cognitive radio networks since we need less power consumption and much more transmitted information. In this paper, we propose a power allocation scheme with robust energy efficiency consideration for appropriately guaranteeing target signal to interference plus noise ratio (SINR) requirement for cognitive users and the received interferences at primary receivers below a threshold. A time-varying interference threshold protection factor and a protection margin to the SINR targets are introduced for the above purpose. This problem is formulated as a fraction programming problem solved by an iterative algorithm based on Lagrange dual approach by convex transformation. Simulation results show the validity of the proposed algorithm on both energy efficiency and robustness under channel gain disturbance.

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Ozcan, G., & Gursoy, M. C. (2015). Optimal power control for underlay cognitive radio systems with arbitrary input distributions. IEEE Transactions on Wireless Communications, 14(8), 4219–4233.

    Article  Google Scholar 

  2. Ahmad, A., Ahmad, S., Rehmani, M. H., & Hassan, N. U. (2015). A survey on radio resource allocation in cognitive radio sensor networks. IEEE Communication Surveys & Tutorials, 17(2), 888–917.

    Article  Google Scholar 

  3. Xu, Y. J., Zhao, X. H., & Liang, Y. C. (2015). Robust power control and beamforming in cognitive radio networks: A survey. IEEE Communication Surveys & Tutorials, 17(4), 1834–1857.

    Article  Google Scholar 

  4. Wang, J., Chen, J. S., Lu, Y., Gerla, M., & Cabric, D. (2015). Robust power control under location and channel uncertainty in cognitive radio networks. IEEE Wireless Communications Letters, 4(2), 113–116.

    Article  Google Scholar 

  5. Han, S., Kim, H., Han, Y., Cilffi, J. M., & Leung, V. C. M. (2013). A distributed power allocation scheme for sum-rate maximization on cognitive GMACs. IEEE Transactions on Communications, 61(1), 248–256.

    Article  Google Scholar 

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

    Article  Google Scholar 

  7. Haykin, S., & Setoodeh, P. (2015). Cognitive radio networks: The spectrum supply chain paradigm. IEEE Transactions on Cognitive Communications and Networking, 1(1), 3–28.

    Article  Google Scholar 

  8. Ahmad, A., Ahmad, S., Rehmain, M. Husain, & Hassan, N. U. (2015). A survey on radio resource allocation in cognitive radio sensor networks. IEEE Communications Surveys & Tutorials, 17(2), 888–917.

    Article  Google Scholar 

  9. Xu, C., Sheng, M., Yang, C. G., Wang, X. J., & Wang, L. (2014). Pricing-based multiresource allocation in OFDMA cognitive radio networks: An energy efficiency perspective. IEEE Transactions on Vehicular Technology, 63(5), 2336–2348.

    Article  Google Scholar 

  10. Alabbasi, A., Rezki, Z., & Shihada, B. (2015). Energy efficient resource allocation for cognitive radios: A generalized sensing analysis. IEEE Transactions on Wireless Communications, 14(5), 2455–2469.

    Article  Google Scholar 

  11. Sboui, L., Rezki, Z., & Alouini, M. S. (2015). Energy-efficient power allocation for underlay cognitive radio systems. IEEE Transactions on Cognitive Communications and Networking, 1(3), 273–283.

    Article  Google Scholar 

  12. Hasan, Z., Bansal, G., Hossain, E., & Vijay, K. B. (2009). Energy-efficient power allocation in OFDM-based cognitive radio systems: A risk-return model. IEEE Transactions on Wireless Communications, 8(12), 6078–6088.

    Article  Google Scholar 

  13. Zhang, H. J., Huang, S. T., Jiang, C. X., Long, K. P., Leung, V. C. M., & Poor, H. V. (2017). Energy efficient user association and power allocation in millimeter wave based ultra dense networks with energy harvesting base stations. IEEE Journal on Selected Areas in Communications, 35(9), 1936–1947.

    Article  Google Scholar 

  14. Zhang, Z. C., Zhang, H. J., Lu, Z. M., Zhao, Z. M., & Wen, X. M. (2013). Energy-efficient resource optimization in OFDMA-based dense femtocell networks. In 2013 20th international conference on telecommunications (ICT 2013) (pp. 1–5).

  15. Zhang, H. J., Liu, H., Cheng, J. L., & Leung, V. C. M. (2017). Downlink energy efficiency of power allocation and wireless backhaul bandwidth allocation in heterogeneous small cell networks. IEEE Transactions on Communications. https://doi.org/10.1109/TCOMM.2017.2763623.

    Google Scholar 

  16. Wang, L., Sheng, M., Zhang, Y., & Wang, X. J. (2015). Robust energy efficiency maximization in cognitive radio networks: The worst-case optimization approach. IEEE Transactions on Communications, 63(1), 51–65.

    Google Scholar 

  17. Wang, S. W., Shi, W. J., & Wang, C. G. (2015). Energy-efficient resource management in OFDM-based cognitive radio networks under channel uncertainty. IEEE Transactions on Communications, 63(9), 3092–3102.

    Article  Google Scholar 

  18. Zhang, H. J., Nie, Y. N., Cheng, J. L., Leung, V. C. M., & Nallanathan, A. (2017). Sensing time optimization and power control for energy efficient cognitive small cell with imperfect hybrid spectrum sensing. IEEE Transactions on Wireless Communications, 16(2), 730–743.

    Article  Google Scholar 

  19. Wang, Y., Xu, W. J., Yang, K. W., & Lin, J. R. (2012). Optimal energy-efficient power allocation for OFDM-based cognitive radio networks. IEEE Communications Letters, 16(9), 1420–1423.

    Article  Google Scholar 

  20. Haykin, S. (2005). Cognitive radio: Brain-empowered wireless communications. IEEE Journal on Selected Areas in Communications, 23(2), 201–219.

    Article  Google Scholar 

  21. Tan, C. W., Palomar, D. P., & Chiang, M. (2009). Energy-robustness tradeoff in cellular network power control. IEEE Transactions on Networking, 17(3), 912–925.

    Article  Google Scholar 

  22. Naeem, M., IIIanko, K., Karmokar, A., Anpalagan, A., & Jaseemuddin, M. (2013). Optimal power allocation for green cognitive radio: Fractional programming approach. IET Communications, 7(12), 1279–1286.

    Article  Google Scholar 

  23. Werner, Dinkelbach. (1964). On nonlinear fractional programming. Management Science, 13(7), 492–498.

    MathSciNet  Google Scholar 

  24. Boyd, S., & Vandenberghe, L. (2004). Convex optimization. Cambridge, UK: Cambridge University press.

    Book  MATH  Google Scholar 

  25. Bazaraa, M. S., Sherali, H. D., & Shetty, C. M. (2006). Nonlinear programming: Theory and algorithms (3rd ed.). New York: Wiley-Interscience.

    Book  MATH  Google Scholar 

  26. Sun, S. Q., Ni, W. M., & Zhu, Y. (2011). Robust power control in cognitive radio networks: A distributed way. In 2011 IEEE international conference on communications (ICC 2011) (pp. 1–13).

  27. Setoodeh, P., & Haykin, S. (2009). Robust transmit power control for cognitive radio. Proceedings of the IEEE, 97(5), 915–939.

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the Nation Natural Science Foundation of China (No. 61501059) and Jilin province Education Office (No. 2016343). We would like to thank the anonymous reviewers and the editor, whose invaluable comments helped improve the presentation of this paper substantially.

Author information

Authors and Affiliations

  1. College of Computer Science and Engineering, Changchun University of Technology, No. 2055 Yanan Street, Changchun, China

    Mingyue Zhou & Hao Yin

  2. College of Communication Engineering, Jilin University, No. 5372 Nanhu Road, Changchun, China

    Xiaohui Zhao

Authors
  1. Mingyue Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaohui Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hao Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mingyue Zhou.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhou, M., Zhao, X. & Yin, H. A robust energy-efficient power control algorithm for cognitive radio networks. Wireless Netw 25, 1805–1814 (2019). https://doi.org/10.1007/s11276-017-1631-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-017-1631-x

Keywords

Search

Navigation