Skip to main content
Log in

Robust Power Control for Multiuser Underlay Cognitive Radio Networks Under QoS Constraints and Interference Temperature Constraints

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Since the nature of mobility and unreliability in wireless communication system may degrade the communication performance, robustness is one of the main concerns in cognitive radio networks (CRNs). In CRNs, the existing power control algorithms based on the assumption of exact system information may not guarantee the communication requirements due to the parameter uncertainties in real system. In this paper, we propose a robust distributed power control algorithm for underlay CRNs. The novelty in our paper is that we consider all possible parameter uncertainties: channel uncertainty and interference uncertainty. Our objective is to maximize the total throughput of secondary users while channel gain and interference plus noise are uncertain. According to the robust optimization theory, uncertain parameters are modeled by additive uncertainties with bounded errors. Through the worst case principle, we transform the robust power control problem into a deterministic optimization one, which is solved by using Lagrange dual decomposition method. Numerical simulation results show that the proposed algorithm can satisfy the QoS requirements of both secondary users and primary users for all uncertainty realizations.

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.

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

Similar content being viewed by others

References

  1. Li, D. (2012). Joint power and rate control combined with adaptive modulation in cognitive radio networks. Wireless Personal Communications, 63(3), 549–559.

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. Rohde, J., & Toftegaard, T. S. (2011). Adaptive cognitive radio technology for low power wireless personal area network devices. Wireless Personal Communications, 58(1), 111–123.

    Article  Google Scholar 

  4. Pang, J., Scutari, g, Palomar, D. P., & Facchinei, F. (2010). Design of cognitive radio systems under temperature-interference constraints: A variational inequality approach. IEEE Transactions on Signal Processing, 58(6), 3251–3271.

    Article  MathSciNet  Google Scholar 

  5. He, A., Srikanteswara, S., Bae, K. K., Newman, T. R., Reed, H., Tranter, W. H., et al. (2011). Power consumption minimization for MIMO systems a cognitive radio approach. IEEE Journal on Selected Areas in Communications, 29(2), 469–479.

    Article  Google Scholar 

  6. Zheng, L., & Tan, C. W. (2013). Cognitive radio network duality and algorithms for utility maximization. IEEE Journal on Selected Areas in Communications, 31(3), 500–5013.

    Article  Google Scholar 

  7. Yang, C., Li, J., & Tian, Z. (2010). Optimal power control for cognitive radio networks under coupled interference constraints: A cooperative game theoretic perspective. IEEE Transaction on Vehicular Technology, 59(4), 1696–1706.

    Article  Google Scholar 

  8. Chen, X., Zhao, Z., Zhang, H., & Chen, T. (2012). Reinforcement learning enhanced iterative power allocation in stochastic cognitive wireless mesh networks. Wireless Personal Communications, 57(1), 89–104.

    Article  Google Scholar 

  9. Zhou, P., Chang, Y., & Copeland, J. A. (2012). Reinforcement learning for repeated power control game in cognitive radio networks. IEEE Journal on Selected Areas in Communications, 30(1), 54–69.

    Article  Google Scholar 

  10. Kang, X., Liang, Y. C., Nallanathan, A., Garg, H. K., & 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 

  11. Son, K., Jung, B. C., Chong, S., & Sung, D. K. (2009). Opportunistic underlay transmission in multi-carrier cognitive radio systems. In Proceedings of IEEE, wireless communications and networking conference (pp. 1–6).

  12. Yang, M., & Grace, D. (2011). Cognitive radio with reinforcement learning applied to multicast downlink transmission with power adjustment. Wireless Personal Communications, 57(1), 73–87.

    Article  Google Scholar 

  13. Dantig, G. B. (1995). Linear programming under uncertainty. Management Science, 1, 197–206.

    Article  Google Scholar 

  14. Bertsimas, D., Brown, D. B., & Caramanis, C. (2011). Theory and application of robust optimization. SIAM Review, 53(3), 464–501.

    Article  MATH  MathSciNet  Google Scholar 

  15. Tal, A. B., Ghaoui, L. E., & Nemirovski, A. (2009). Robust optimization. Princeton, NJ: Princeton University Press.

    MATH  Google Scholar 

  16. Soltani, N. Y., Kim, S. J., & Giannakis, G. B. (2012). Chance-constrained optimization of uplink parameters for OFDMA cognitive radios. In Proceedings of IEEE international conference on acoustics, speech and signal processing (pp. 2813–2816).

  17. Anese, E. D., Kim, S. J., Giannakis, G. B., & Pupolin, S. (2011). Power control for cognitive radio networks under channel uncertainty. IEEE Transactions on Wireless Communications, 10(10), 3541–3551.

    Article  Google Scholar 

  18. Anandkumar, A. J. G., Anandkumar, A., Lambotharan, S., & Chamber, J. (2011). Robust rate-maximization game under bounded channel uncertainty. IEEE Transactions on Vehicular Technology, 60(9), 4471–4486.

    Article  Google Scholar 

  19. Parsaeefard, S., & Sharafat, A. R. (2012). Robust worst-case interference control in underlay cognitive radio network. IEEE Transactions on Vehicular Technology, 61(8), 3731–3745.

    Article  Google Scholar 

  20. Rahulamathavan, Y., Cumanan, K., & Lambotharan, S. (2011). A mixed SINR-balancing and SINR-target-constraints-based beamformer design techniques for spectrum-sharing networks. IEEE Transactions on Vehicular Technology, 60(9), 4403–4414.

    Article  Google Scholar 

  21. Parsaeefard, S., & Sharafat, A. R. (2013). Robust distributed power control in cognitive radio networks. IEEE Transactions on Mobile Computing, 12(4), 609–620.

    Article  Google Scholar 

  22. Xing, Y., Mathur, C. N., Haleem, M. A., & Subbalakshmi, K. P. (2007). Dynamic spectrum access with QoS and interference temperature constraints. IEEE Transactions on Mobile Computing, 6(4), 423–433.

    Article  Google Scholar 

  23. Boyd, S., & Vandenberghe, L. (2004). Convex optimization. New York: Cambridge University Press.

    Book  MATH  Google Scholar 

  24. Chiang, M., Tan, C. W., Polamar, D. P., Neill, D. O., & Julian, D. (2007). Power control by geometric programming. IEEE Transactions on Wireless Communications, 6(7), 2640–2651.

    Article  Google Scholar 

  25. Nadkar, T., Thumar, V., Tej, G. P. S., & Desai, U. B. (2012). Distributed power allocation for secondary users in a cognitive radio scenario. IEEE Transactions on Wireless Communications, 11(4), 1576–1586.

    Article  Google Scholar 

  26. Wu, Y., & Tsang, D. H. K. (2009). Distributed power allocation algorithm for spectrum sharing cognitive radio networks with QoS guarantee. In Proceedings of IEEE INFOCOM (pp. 981–989).

  27. Reemtsen, R., & Ruckmann, J. J. (1998). Semi-infinite programming. Boston, Massachusetts: Kluwer Academic Publishers.

    Book  MATH  Google Scholar 

Download references

Acknowledgments

This work is supported by National Nature Science Foundation of China, Grant Number (61171079). We thank the reviewers for their detailed, constructive and valuable reviews and comments.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Xiaohui Zhao.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Xu, Y., Zhao, X. Robust Power Control for Multiuser Underlay Cognitive Radio Networks Under QoS Constraints and Interference Temperature Constraints. Wireless Pers Commun 75, 2383–2397 (2014). https://doi.org/10.1007/s11277-013-1472-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-013-1472-6

Keywords

Navigation