Skip to main content
SpringerLink
Log in

Behavior modeling for spectrum sharing in wireless cognitive networks

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

Cognitive networks are designed based on the concept of dynamic and intelligent network management, characterizing the feature of self-sensing, self-configuration, self-learning, self-consciousness etc. In this paper, focusing on the spectrum sharing and competition, we propose a novel OODA (Orient-Observe-Decide-Act) based behavior modeling methodology to illustrate spectrum access problem in the heterogenous cognitive network which consists of multiple primary networks (PN, i.e. licensed networks) and multiple secondary networks (SN, i.e. unlicensed networks). Two different utility functions are designed for primary users and secondary users respectively based on marketing mechanism to formulate the decide module mathematically. Also, we adopt expectation and learning process in the utility design which considers the variance of channels, transmission forecasting, afore trading histories and etc. A double auction based spectrum trading scheme is established and implemented in two scenarios assorted from the supply-and-demand relationship i.e. LPMS (Less PNs and More SNs) and MPLS (More PNs and Less SNs). After the discussion of the Bayesian Nash Equilibrium, numerical results with four bidding strategies of SNs are presented to reinforce the effectiveness of the proposed utility evaluation based decision modules under two scenarios. Besides, we prove that the proposed behavior model based spectrum access method maintains frequency efficiency comparable with traditional centralized cognitive access approaches and reduces the network deployment cost.

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
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Thomas, R. W., Friend, D. H., DaSilva, L. A., & MacKenzie, A. B. (2006). Cognitive networks: Adaptation and learning to achieve end-to-end performance objectives. IEEE Communications Magazine, 44(12), 51–57.

    Article  Google Scholar 

  2. Letaief, K. B., & Zhang, W. (2009). Cooperative communications for cognitive radio networks. Proceedings of the IEEE, 97, 878–893.

    Article  Google Scholar 

  3. Li, Z., Yu, F. R., & Huang, M. (2010). A distributed consensus-based cooperative spectrum sensing in cognitive radios. IEEE Transactions Vehicular Technology, 59, 383–393.

    Article  Google Scholar 

  4. Zhao, Y., Mao, S., Neel, J. O., & Reed, J. (2009). Performance evaluation of cognitive radios: Metrics, utility functions and methodologys. Proceedings of the IEEE, 97(4), 642–659.

    Article  Google Scholar 

  5. Yu, F. R. (2011). Cognitive radio mobile Ad Hoc networks. New York: Springer.

    Book  Google Scholar 

  6. Devroye, N., Vu, M., & Tarokh, V. (2008). Cognitive radio networks. IEEE Signal Processing Magazine, 125(6), 12–23.

    Article  Google Scholar 

  7. Guan, Q., Yu, F. R., Jiang, S., & Wei, G. (2010). Prediction-based topology control and routing in cognitive radio mobile Ad hoc networks. IEEE Transactions Vehicular Technology, 59, 4443–4452.

    Article  Google Scholar 

  8. Yu, F. R., Sun, B., Krishnamurthy, V., & Ali, S. (2011). Application layer qos optimization for multimedia transmission over cognitive radio networks. Wireless Networks, 17, 371–383.

    Article  Google Scholar 

  9. Luo, C., Yu, F. R., Ji, H., & Leung, V. C. (2010). Cross-layer design for TCP performance improvement in cognitive radio networks. IEEE Transactions Vehicular Technology, 59(5), 2485–2495.

    Article  Google Scholar 

  10. Hall, M. W., Gil, Y., & Lucas R. F. (2008). Self-configuring applications for heterogeneous systems: Program composition and optimization using cognitive techniques. Proceedings of the IEEE, 96(5), 849–862.

    Article  Google Scholar 

  11. https://www.ict-e3.eu/.

  12. Mitola, J., & G. M. Jr. (1999). Cognitive radio: Making software radios more personal. IEEE Personal Communications, 6(4), 13–18.

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

    Article  Google Scholar 

  14. Yucek, T., & Arslan, H. (2009). A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Communications Surveys & Tutorials, 11(1), 116–130.

    Article  Google Scholar 

  15. Haykin, S., Thomson, D., & Reed, J. (2009). Spectrum sensing for cognitive radio. Proceedings of the IEEE, 97(5), 849–877.

    Article  Google Scholar 

  16. Yu, F. R., Huang, M., & Tang, H. (May 2010). Biologically inspired consensus-based spectrum sensing in mobile Ad hoc networks with cognitive radios. IEEE Networks, pp. 26–30.

  17. Si, P., Ji, H., Yu, F. R., & Leung, V. (2010). Optimal cooperative internetwork spectrum sharing for cognitive radio systems with spectrum pooling. IEEE Transactions Vehicular Technology, 59, 1760–1768.

    Article  Google Scholar 

  18. Akyildiz, I., Lee, W.-Y., Vuran, M. & Mohanty, S. (2008). A survey on spectrum management in cognitive radio networks. IEEE Communications Magazine, 46(4), 40–48.

    Article  Google Scholar 

  19. Niyato, D., & Hossain, E. (2008). Competitive spectrum sharing in cognitive radio networks: A dynamic game approach. IEEE Transactions on Wireless Communications, 7, 1–5.

    Google Scholar 

  20. Ji, Z., & Liu, K. (2008). Multi-stage pricing game for collusion-resistant dynamic spectrum allocation. IEEE Journal on Seleted Areas in Communications, 26, 182–191.

    Article  Google Scholar 

  21. Niyato, D., & Hossain, E. (2008). Spectrum trading in cognitive radio networks: a market-equilibrium-based approach. IEEE Wireless Communications, 15, 71–80.

    Article  Google Scholar 

  22. Zhu, J., & Liu, K. (2007). Cognitive radios for dynamic spectrum access—dynamic spectrum sharing: A game theoretical overview. IEEE Communications Magazine, 45, 88–94.

    MathSciNet  Google Scholar 

  23. Malanchini, I., Cesana, M., & Gatti, N. (2009). On spectrum selection games in cognitive radio networks. In Proceedings of the IEEE GLOBECOM, pp. 1–7.

  24. Niyato, D., & Hossain, E. (2008). Competitive spectrum sharing in cognitive radio networks: A dynamic game approach. IEEE Transactions on Wireless Communications, 7(7), 2651–2660.

    Article  Google Scholar 

  25. Wang, S., Xu, P., & Xu, X. (2010). Toda: Truthful online double auction for spectrum allocation in wireless networks. In 2010 IEEE symposium on new frontiers in dynamic spectrum, pp. 1–10.

  26. Zhou, X., & Zheng, H. (2009). Trust: A general framework for truthful double spectrum auctions. In Proceedings of the IEEE GLOBECOM, pp. 999–1007.

  27. Hung-Bin, C., & Chen, K.-C. (2010). Auction-based spectrum management of cognitive radio networks. IEEE Transactions on Vehicular Technology, 59(4), 1923–1935.

    Article  Google Scholar 

  28. Niyato, D., & Hossain, E. (2007). “A game-theoretic approach to competitive spectrum sharing in cognitive radio networks,” in Proceedings of the IEEE WCNC, pp. 16–20.

  29. Xing, Y., & Chandramouli, R. (2008). Human behavior inspired cognitive radio network design. IEEE Communications Magazine, 46, 122–127.

    Article  Google Scholar 

  30. Roy, N., Roy, A., & Das, S. (2007). Cluster-based cooperative spectrum sensing in cognitive radio systems. In Proceedings of IEEE ICC Conference, pp. 2511–2515.

  31. Guo, C., Peng, T., Qi, Y., & Wang, W. (2009). “Adaptive channel searching scheme for cooperative spectrum sensing in cognitive radio networks. In Proceedings of IEEE WCNC Conference, pp. 1–6.

  32. Qusay, H. M. (2007). Cognitive networks: Towards self-aware networks. London: Wiley

    Google Scholar 

  33. Niyato, D., Hossain, E., & Han, Z. (2009). Dynamics of multiple-seller and multiple-buyer spectrum trading in cognitive radio networks: A game-theoretic modeling approach. IEEE Transactions on Mobile Computing, 8(8), 1009–1022.

    Article  Google Scholar 

  34. Fangwen, F., & der Schaar, M. (2009). Learning to compete for resources in wireless stochastic games. IEEE Transactions on Vehicular Technology, 58(4), 1904–1919.

    Article  Google Scholar 

  35. Xing, Y., Chandramouli, R., & Cordeiro, C. M. (2007). Price dynamics in competitive agile spectrum access markets. IEEE Journal on Selected Areas in Communications, 25, 613–621.

    Article  Google Scholar 

  36. Shankar, S., Chou, C. T., & Challapali, K., Mangold, S. (2005). Spectrum agile radio: Capacity and QoS implications of dynamic spectrum assignment. In Proceedings of IEEE Globecom’05, pp. 2510–2516.

  37. Corlett, R. (1986). Features of artificial intelligence languages and their environments. Software Engineering Journal, 1(4), 159–164.

    Article  Google Scholar 

  38. Iosifidis, G., & Koutsopoulos, I. (2010). Double auction mechanisms for resource allocation in autonomous networks. IEEE Journal on Selected Areas in Communications, 28(1), 95–102.

    Article  Google Scholar 

  39. Gibbons, R. (1992). game theory for applied economists. London: Princeton University Press.

    Google Scholar 

Download references

Acknowledgment

This work is supported by the National Natural Science Foundation of China under Grant No. 60971083, 61171097 and 61101107, the National International Science and Technology Cooperation Project under Granted NO.2010DFA11322, and the Chinese Universities Scientific Fund under Granted NO.2012RC0306.

Author information

Authors and Affiliations

  1. Beijing University of Posts and Telecommunications, Beijing, China

    Yinglei Teng, Yifei Wei, Li Wang & Yong Zhang

  2. Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada

    F. Richard Yu

Authors
  1. Yinglei Teng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. F. Richard Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yifei Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Li Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yinglei Teng.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Teng, Y., Yu, F.R., Wei, Y. et al. Behavior modeling for spectrum sharing in wireless cognitive networks. Wireless Netw 18, 929–947 (2012). https://doi.org/10.1007/s11276-012-0443-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-012-0443-2

Keywords

Search

Navigation