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Min-max BER Based Power Control for OFDM-Based Cognitive Cooperative Networks with Imperfect Spectrum Sensing

  • Hangqi LiEmail author
  • Xiaohui Zhao
  • Yongjun Xu
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 172)

Abstract

In this paper, a power control (PC) algorithm for multiuser Orthogonal Frequency Division Multiplexing (OFDM)-based cognitive cooperative networks under the imperfect spectrum sensing is studied to minimize total Bit Error Rate (BER) of secondary users (SUs) under the consideration of maximum transmit power budgets, signal-to-interference-and-noise ratio (SINR) constraints and interference requirements to guarantee quality of service (QoS) of primary user (PU). And a cooperative spectrum sensing (CSS) strategy is considered to optimize sensing performance. The worst-channel-state-information (worst-CSI) PC algorithm is introduced to limit the BER of SUs, which only needs to operate the algorithm in one link that CSI is worst, while the interference model is formulated under the consideration of spectrum sensing errors. In order to obtain optimal solution, the original min-max BER optimization problem is converted into a max-min SINR problem solved by Lagrange dual decomposition method. Simulation results demonstrate that the proposed scheme can achieve good BER performance and the protection for PU.

Keywords

Cooperative transmission Imperfect spectrum sensing OFDM-based cognitive radio networks The worst-CSI PC algorithm 

Notes

Acknowledgment

The work of this paper is supported by National Natural Science Foundation of China under grant No. 61571209.

References

  1. 1.
    Haykin, S.: Cognitive radio: brain-empowered wireless communications. IEEE J. Sel. Areas Commun. 23(2), 201–220 (2005)CrossRefGoogle Scholar
  2. 2.
    Xu, Y.J., Zhao, X.H., Liang, Y.-C.: Robust power control and beamforming in cognitive radio networks: A survey. IEEE Commun. Surveys Tuts. 17(4), 1834–1857 (2015)CrossRefGoogle Scholar
  3. 3.
    Letaief, K.B., Zhang, W.: Cooperative communications for cognitive radio networks. Proc. IEEE 97(5), 878–893 (2009)CrossRefGoogle Scholar
  4. 4.
    Cover, T.M., Gamal, A.E.: Capacity theorems for the relay channel. IEEE Trans. Inf. Theory 25(5), 572–584 (1979)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    El, G.A., Cover, T.M.: Multiple user information theory. Proc. IEEE 68(12), 1466–1483 (1980)CrossRefGoogle Scholar
  6. 6.
    Rasti, M., Hasan, M., Le, L.B., Hossain, E.: Distributed uplink powercontrol for multi-cell cognitive radio networks. IEEETrans. Commun. 63(3), 628–642 (2015)CrossRefGoogle Scholar
  7. 7.
    Xu, Y.J., Zhao, X.H.: Robust power control for underlay cognitive radio networks under probabilistic quality of service and interference constraints. IET Commun. 8(18), 3333–3340 (2014)CrossRefGoogle Scholar
  8. 8.
    Wu, Y., Tsang, D.H.: Joint bandwidth and power allocations for cognitive radio networks with imperfect spectrum sensing. Wireless Per. Commun. 57(1), 19–31 (2011)CrossRefGoogle Scholar
  9. 9.
    Wang, S., Zhou, Z.H., Ge, M., Wang, C.: Resource allocation for heterogeneous multiuser ofdm-based cognitive radio networks with imperfect spectrum sensing. In: IEEE INFOCOM, pp. 2264–2272 (2012)Google Scholar
  10. 10.
    Tan, X., Zhang, H., Hu, J.: Capacity maximisation of the secondary link in cognitive radio networks with hybrid spectrum access strategy. IET Commun. 8(5), 689–696 (2014)CrossRefGoogle Scholar
  11. 11.
    Digham, F.F., Alouini, M.-S., Simon, M.K.: On the energy detection of unknown signals over fading channels. IEEE Trans. Commun. 55(1), 21–24 (2007)CrossRefGoogle Scholar
  12. 12.
    Liu, Z., Yuan, H., Li, H., Guan, X., Yang, H.: Robust power control for amplify-and-forward relaying scheme. IEEE Commun. Lett. 19(2), 263–266 (2015)CrossRefGoogle Scholar
  13. 13.
    Xu, X., Bao, J., Cao, H., Yao, Y., Hu, S.: Energy efficiency based optimalrelay selection scheme with a ber constraint in cooperative cognitive radionetworks. IEEE Trans. Veh. Technol., January 2015. doi: 10.1109/TVT.2015.2389810
  14. 14.
    Ge, M., Wang, S.: Energy-efficient power allocation for cooperative relaying cognitive radio networks. In: IEEE Wireless Communications and Networking Conference (WCNC), pp. 691–696, April 2013Google Scholar
  15. 15.
    Boyd, S., Vandenberghe, L.: Convex Optimization. Cambridge Univ. Press, Mar, Cambridge (2004)CrossRefzbMATHGoogle Scholar
  16. 16.
    Setoodeh, P., Haykin, S.: Robust transmit power for cognitive radio. Proc. IEEE 97(5), 915–939 (2009)CrossRefGoogle Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2016

Authors and Affiliations

  1. 1.College of Communication EngineeringJilin UniversityChangchunChina

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