Skip to main content
SpringerLink
Log in

An uplink power control algorithm using traditional iterative model for cognitive radio networks

  • Published:
Journal of Central South University Aims and scope Submit manuscript

Abstract

Based on the analysis of the feature of cognitive radio networks, a relevant interference model was built. Cognitive users should consider especially the problem of interference with licensed users and satisfy the signal-to-interference noise ratio (SINR) requirement at the same time. According to different power thresholds, an approach was given to solve the problem of coexistence between licensed user and cognitive user in cognitive system. Then, an uplink distributed power control algorithm based on traditional iterative model was proposed. Convergence analysis of the algorithm in case of feasible systems was provided. Simulations show that this method can provide substantial power savings as compared with the power balancing algorithm while reducing the achieved SINR only slightly, since 6% SINR loss can bring 23% power gain. Through further simulations, it can be concluded that the proposed solution has better effect as the noise power or system load increases.

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

Similar content being viewed by others

References

  1. IEEE 802.22 Working Group. IEEE P802.22/D1.0 draft standard for wireless regional area networks part 22: cognitive wireless RAN medium access control (MAC) and physical layer (PHY) specifications: policies and procedures for operation in the TV bands [R]. 2008.

  2. GUAN Q S, YU F R, JIANG S M, WEI G. Prediction-based topology control and routing in cognitive radio mobile ad hoc networks [J]. IEEE Transactions on Vehicular Technology, 2010, 59(9): 4443–4452.

    Article  Google Scholar 

  3. HAYKIN S. Cognitive radio: Brain-empowered wireless communications [J]. IEEE Journal of Select Areas Communications, 2005, 23(2): 201–220.

    Article  Google Scholar 

  4. AEIN J M. Power balancing in systems employing frequency reuse [J]. COMSAT Tech Rev, 1973, 3(2): 277–299.

    Google Scholar 

  5. MEYERHOFF H J. Method for computing the optimum power balance in multibeam satellites [J]. COMSAT Tech Rev, 1974, 4(1): 139–146.

    Google Scholar 

  6. NETTLETON R W, ALAVI H. Power control for a spread spectrum cellular mobile radio system [C]// Proc IEEE Veh Technol Conf. Toronto: IEEE Press, 1983: 242–246.

    Google Scholar 

  7. ZANDER J. Performance of optimum transmitter power control in cellular radio systems [J]. IEEE Transactions on Vehicular Technology, 1992, 41(1): 57–62.

    Article  Google Scholar 

  8. GRANDHI S A, ZANDER J. Constrained power control in cellular radio systems [C]// Proc IEEE 44th Veh Technol Conf, 1994, 2: 824–828.

    Google Scholar 

  9. YATES R D. A framework for uplink power control in cellular radio systems [J]. IEEE Journal of Select Areas Communications, 1995, 13(7): 1341–1347.

    Article  MathSciNet  Google Scholar 

  10. GRANDHI S, ZANDER J, YATES R D. Constrained power control [J]. Journal of Wireless Personal Communications, 1995, 1(4): 257–270.

    Article  Google Scholar 

  11. HUANG M, CAINES P E, CHARALAMBOUS C D, MALHAME R P. Power control in wireless systems: A stochastic control formulation [C]// Proc Amer Control Conf. Portland: IEEE Press, 2005, 2: 750–755.

    Google Scholar 

  12. HUANG M, CAINES P E, CHARLAMBOUS C D, MALHAME R P. Stochastic power control for wireless systems: Classical and viscosity solutions [C]// Proc 40th IEEE Conf Decision and Control. Orlando: IEEE Press, 2001: 1037–1042.

    Google Scholar 

  13. SU H J, GERANIOTIS E. Adaptive closed-loop power control with quantized feedback and loop filtering [J]. IEEE Transactions on Wireless Communications, 2002, 1(1): 76–86.

    Article  Google Scholar 

  14. WU J, CHEN C Y. An adjustable fuzzy power control architecture for a multirate WCDMA system [C]// Proc of the 27th Chinese Control Conf. Kunming: Chinese Association of Automation, 2008: 633–637.

    Google Scholar 

  15. CHANG P R, WANG B C. Adaptive fuzzy power control for CDMA mobile radio systems [J]. IEEE Transactions on Vehicular Technology, 1996, 45(2): 225–236.

    Article  Google Scholar 

  16. TSAY M K, LEE Z S, LIAO C H. Fuzzy power control for downlink CDMA-based LMDS network [J]. IEEE Transactions on Vehicular Technology, 2008, 57(6): 3917–3920.

    Article  Google Scholar 

  17. CANALES M, GALLEGO J. Potential game for joint channel and power allocation in cognitive radio networks [J]. IEEE Electronics Lett, 2010, 24: 9–10.

    Google Scholar 

  18. XU B Y, LI S Q. Joint power control in cognitive radio system [J]. Journal of University of Electronic Science and Technology of China, 2008, 37(5): 649–652.

    Google Scholar 

  19. HOANG A T, LIANG Y C. Downlink channel assignment and power control for cognitive radio networks [J]. IEEE Transactions on Wireless Communications, 2008, 7(8): 3106–3117.

    Article  Google Scholar 

  20. SHI Y, THOMAS Y, ZHOU H, MIDKIFF S F. Distributed crosslayer optimization for cognitive radio networks [J]. IEEE Transactions on Vehicular Technology, 2010, 59(8): 4058–4069.

    Article  Google Scholar 

  21. KOSKIE S, GAJIC Z. A nash game algorithm for SIR-based power control in 3G wireless CDMA networks [J]. IEEE Transactions on Networking, 2005, 13(5): 1017–1026.

    Article  Google Scholar 

  22. HUANG C, CHEN K. Dual-observation time-division spectrum sensing for cognitive radios [J]. IEEE Transactions on Vehicular Technology, 2011, 60(8): 3712–3725.

    Article  Google Scholar 

  23. KANG H G, SONG I, YOON S, KIM Y H. A class of spectrum-sensing schemes for cognitive radio under impulsive noise circumstances: Structure and performance in non-fading and fading environments [J]. IEEE Transactions on Vehicular Technology, 2010, 59(9): 4322–4339.

    Article  Google Scholar 

  24. CHEN H, TSE C K, ZHAO F. Optimal quantization bit budget for a spectrum sensing scheme in bandwidth-constrained cognitive sensor networks [J]. IET Wireless Sensor Systems, 2011, 1(3): 144–150.

    Article  Google Scholar 

  25. FCC. Notice of Inquiry and notice of proposed rule making [R]. ET Docket, 2003.

  26. FOSCHINI G J, MILJANIC Z. A simple distributed autonomous power control algorithm and its convergence [J]. IEEE Transactions on Vehicular Technology, 1993, 42(4): 641–646.

    Article  Google Scholar 

  27. SAAD Y. Iterative methods for sparse linear systems [M]. Second Edition. Society for Industrial and Applied Mathematics, 2003: 41–46.

Download references

Author information

Authors and Affiliations

  1. School of Electronics Information Engineering, Harbin Institute of Technology, Harbin, 150080, China

    Feng Li  (李枫) & Xue-zhi Tan  (谭学治)

  2. Department of Automation Measurement and Control, Harbin Institute of Technology, Harbin, 150080, China

    Li Wang  (王丽)

Authors
  1. Feng Li  (李枫)
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xue-zhi Tan  (谭学治)
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Li Wang  (王丽)
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Feng Li  (李枫).

Additional information

Foundation item: Project(61071104) supported by the National Natural Science Foundation of China

Rights and permissions

Reprints and permissions

About this article

Cite this article

Li, F., Tan, Xz. & Wang, L. An uplink power control algorithm using traditional iterative model for cognitive radio networks. J. Cent. South Univ. 19, 2816–2822 (2012). https://doi.org/10.1007/s11771-012-1347-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11771-012-1347-0

Key words

Search

Navigation