Skip to main content

Advertisement

SpringerLink
Log in

A Robust Energy Aware Power Control Algorithm with SINR-Flexible Requirement in Cognitive Radio Networks

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Energy efficiency power control for dynamical cognitive radio networks is a challenging problem. In this paper, our objective is to minimize total transmit power of multiuser cognitive system while keeping the time-varying target signal-to-interference-noise-ratio (SINR) requirements constraints satisfied. In order to compensate the perturbations from new coming users, a SINR strengthen factor is introduced to keep minimum acceptable SINR for SUs. A robust energy aware distributed power control scheme with SINR-flexible requirements is proposed in which a tradeoff between power consumption and received SINR is considered. Simulation results show that our proposed scheme provides better SINR protection and robustness than the existing work which does not consider the entry of new users and the perturbation of channel gains.

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

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

Similar content being viewed by others

References

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

    Article  Google Scholar 

  2. Rasti, M., Sharafat, A. R., & Zander, J. (2010). A distributed dynamic target-SIR-tracking power control algorithm for wireless cellular networks. IEEE Transactions on Vehicular Technology, 59(2), 906–916.

    Article  Google Scholar 

  3. Kang, X., Garg, H. K., Liang, Y.-C., & Zhang, R. (2010). Optimal power allocation for OFDM-based cognitive radio with new primary transmission protection criteria. IEEE Transactions on Wireless Communications, 9(6), 2066–2075.

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  6. Almalfouh, S. M., & Stüber, G. L. (2012). Joint spectrum-sensing design and power control in cognitive radio networks: A stochastic approach. IEEE Transactions on Wireless Communications, 11(12), 4372–4380.

    Article  Google Scholar 

  7. Lapiccirella, F. E., Liu, X., & Ding, Z. (2013). Distributed control of multiple cognitive radio overlay for primary queue stability. IEEE Transactions on Wireless Communications, 12(1), 112–122.

    Article  Google Scholar 

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

    Article  Google Scholar 

  9. Mao, J., Xie, G., Gao, J., & Liu, Y. (2013). Energy efficiency optimization for OFDM-based cognitive radio systems: A water-filling factor aided search method. IEEE Transactions on Wireless Communications, 12(5), 2366–2375.

    Article  Google Scholar 

  10. Wang, Y., Wenjun, X., Yang, K., & Lin, J. (2012). Optimal energy-efficient power control for OFDM-based cognitive radio networks. IEEE Communications Letters, 16(9), 1420–1423.

    Article  Google Scholar 

  11. Wang, S. (2010). Efficient resource allocation for cognitive OFDM systems. IEEE Communications Letters, 14(8), 725–727.

    Article  Google Scholar 

  12. Wang, S., Ge, M., & Zhao, W. (2013). Energy-efficient resource allocation for OFDM-based cognitive radio networks. IEEE Transactions on Communications, 61(8), 3181–3191.

    Article  Google Scholar 

  13. Keshavarz, H., Hossain, E., Noghanian, S., & Kim, D. I. (2010). Perturbation analysis for spectrum sharing in cognitive radio networks. IEEE Transactions on Wireless Communications, 9(5), 1564–1570.

    Article  Google Scholar 

  14. Ben-Tal, A., & Nemirovski, A. (2002). Robust optimization-methodology and applications. Mathematical Programming, 92(92), 453–480.

    Article  MathSciNet  MATH  Google Scholar 

  15. Wang, J., Chen, J., Yon, L., 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 

  16. Gong, S., Wang, P., & Duan, L. (2015). Distributed power control with robust protection for PUs in cognitive radio networks. IEEE Transactions on Wireless Communications, 14(6), 3247–3258.

    Article  Google Scholar 

  17. Xu, Y., & Zhao, X. (2014). Robust power control for underlay cognitive radio networks under probabilistic quality of service and interference constraints. IET Communications, 8(18), 3333–3340.

    Article  Google Scholar 

  18. Xiao, H., Yang, K., & Wang, X. (2013). Robust power control under channel uncertainty for cognitive radios with sensing delays. IEEE Transactions on Wireless Communications, 12(2), 646–655.

    Article  Google Scholar 

  19. de Sousa Chaves, F., Abbas-Turki, M., Abou-Kandil, H., & Romano, J. M. T. (2013). Transmission power control for opportuistic QoS provision in wireless networks. IEEE Transactions on Control Systems Technology, 21(2), 315–331.

    Article  Google Scholar 

  20. 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 

  21. Sun, S., Ni, W., & Zhu, Y. (2011). Robust power control in cognitive radio networks: A distributed way. In 2011 IEEE international conference on communications (ICC), Kyoto, pp. 1–6.

  22. Boyd, S., & Vandenberghe, L. (2004). Convex optimiztion. Cambridge: Cambridge University Press.

    Book  MATH  Google Scholar 

  23. Zhou, M., & Zhao, X. (2014). A new power allocation algorithm in cognitive radio networks. China Communications, 11(4), 64–72.

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China and Jilin province Education Office (Nos. 61501059 and 2016343). We would like to thank the anonymous reviewers and editors for their valuable suggestions.

Author information

Authors and Affiliations

  1. College of Computer Science and Engineering, Changchun University of Technology, Changchun, China

    Mingyue Zhou

  2. College of Communication Engineering, Jilin University, Changchun, China

    Mingyue Zhou & 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

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. A Robust Energy Aware Power Control Algorithm with SINR-Flexible Requirement in Cognitive Radio Networks. Wireless Pers Commun 96, 5809–5823 (2017). https://doi.org/10.1007/s11277-017-4448-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-017-4448-0

Keywords

Search

Navigation