Skip to main content
Log in

An improved fair channel hopping protocol for dynamic environments in cognitive radio networks

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

Rendezvous is a fundamental challenge in cognitive radio networks where users can find each other on a specific channel and hence establish a communication link. Most previous works are based on the strong assumption that users are able to find a set of available channels after the spectrum sensing stage and the status of these channels are stable all the time, which, however, may be unrealistic in some scenarios. As a solution, we design a fair channel hopping protocol with dynamic channel state, by adopting the concepts of Markov process, Jenkins Hash and Josephus recursive. Two protocols (FCH_S, FCH_A) are proposed for synchronous clock and asynchronous clock network model, respectively. The channel activity model is built with the aid of Markov process. By taking advantage of Jenkins Hash and Josephus recursive, the fairness of protocol is guaranteed. We assume that (1) a secondary user, SU\(_A\), rendezvous with SU\(_B\); (2) corresponding channels available probability are \(p_a\) and \(p_b\). According to these assumptions, we can prove that expect rendezvous time for FCH_S and FCH_A are \(\dfrac{1}{p_{a}p_{b}}\) and \(\dfrac{1}{p_{a}}+\dfrac{1}{p_{b}}-1\). Simulation results demonstrate that FCH_S and FCH_A can achieve better performance in contrast to the exiting channel hopping protocols (e.g. H.Tan and HHCH).

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

Similar content being viewed by others

References

  1. 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 

  2. Ghasemi, A., & Sousa, E. S. (2008). Spectrum sensing in cognitive radio networks: Requirements, challenges and design trade-offs. IEEE Communications Magazine, 46(4), 32–39.

    Article  Google Scholar 

  3. Khan, A. A., Rehmani, M. H., & Reisslein, M. (2015). Cognitive radio for smart grids: Survey of architectures, spectrum sensing mechanisms, and networking protocols. IEEE Communications Surveys & Tutorials, 18(1), 860–898.

    Article  Google Scholar 

  4. Akhtar, F., Rehmani, M. H., & Reisslein, M. (2016). White space: Definitional perspectives and their role in exploiting spectrum opportunities. Telecommunications Policy, 40(4), 319–331.

    Article  Google Scholar 

  5. Theis, N. C., Thomas, R. W., & DaSilva, L. A. (2011). Rendezvous for cognitive radios. IEEE Transactions on Mobile Computing, 10(2), 216–227.

    Article  Google Scholar 

  6. Xiaogang, Q., Rong, G., Lifang, L., & Wei, Y. (2017). ADFC-CH: Adjusted disjoint finite cover rendezvous algorithms for cognitive radio networks. Wireless Networks, 2017(4), 1–10.

    Google Scholar 

  7. Jia, J., Zhang, Q., & Shen, X. (2008). HC-MAC: A hardware-constrained cognitive mac for efficient spectrum management. IEEE Journal on Selected Areas in Communications, 26(1), 106–117.

    Article  Google Scholar 

  8. Zou, Y., & Yoo, S. J. (2015). A cooperative attack detection scheme for common control channel security in cognitive radio networks. In IEEE International Conference on Ubiquitious and Futures Networks (pp. 606–611).

  9. Yoo, S. J., Nan, H., & Hyon, T. I. (2009). DCR-MAC: Distributed cognitive radio mac protocol for wireless ad hoc networks. Wireless Communications & Mobile Computing, 9(5), 631–653.

    Article  Google Scholar 

  10. Li, J., Zhao, H., Wang, H., Zhou, L., & Wei, J. (2016). Demo: Multi-channel access and rendezvous in CRNS. In ACM MOBIHOC.

  11. Maryam, R. (2015). Frequency hopping in cognitive radio networks: A survey. In IEEE international conference on wireless for space and extreme environments (pp. 1–6).

  12. Haosen, P., Zhaoquan, G., Xiao, L., & Qiang Sheng, H. (2016). Dynamic rendezvous algorithms for cognitive radio networks. In IEEE International Conference on Communications (pp. 1–6).

  13. Rahman, M. J. A., Rahbari, H., & Krunz, M. (2012). Adaptive frequency hopping algorithms for multicast rendezvous in DSA networks. In IEEE International Symposium on Dynamic Spectrum Access (pp. 517–528).

  14. Tan, H., Yu, J., Liang, H., Wang, R., & Han, Z. (2015). Optimal rendezvous strategies for different environments in cognitive radio networks. In ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (pp. 65–72).

  15. Chao, C. M., Fu, H. Y., & Zhang, L. R. (2015). A fast rendezvous-guarantee channel hopping protocol for cognitive radio networks. IEEE Transactions on Vehicular Technology, 64(12), 1–1.

    Article  Google Scholar 

  16. Paul, R., & Choi, Y.-J. (2016). Adaptive rendezvous for heterogeneous channel environments in cognitive radio networks. IEEE Transactions on Wireless Communications, 15(11), 7753–7765.

    Article  Google Scholar 

  17. Jia, J., & Zhang, Q. (2013). Rendezvous protocols based on message passing in cognitive radio networks. IEEE Transactions on Wireless Communications, 12(11), 5594–5606.

    Article  Google Scholar 

  18. Pal, S. K., Bhardwaj, D., Kumar, R., & Bhatia, V. (2009). A new cryptographic hash function based on latin squares and non-linear transformations. In Advance Computing Conference, 2009. IACC 2009. IEEE International (pp. 862–867). IEEE.

  19. Menezes, A. J., Van Oorschot, P. C., & Vanstone, S. A. (1996). Handbook of applied cryptography. Boca Raton: CRC Press.

    Book  MATH  Google Scholar 

  20. Dowdy, J., & Mays, M. E. (1989). Josephus permutations. Journal of Combinatorial Mathematics and Combinatorial Computing, 6, 125–130.

    MathSciNet  MATH  Google Scholar 

  21. Cormen, T. H. (2009). Introduction to algorithms. Cambridge: MIT Press.

    MATH  Google Scholar 

  22. Saleem, Y., & Rehmani, M. H. (2014). Primary radio user activity models for cognitive radio networks: A survey. Journal of Network and Computer Applications, 43, 1–16.

    Article  Google Scholar 

  23. Canberk, B., Akyildiz, I. F., & Oktug, S. (2011). Primary user activity modeling using first-difference filter clustering and correlation in cognitive radio networks. IEEE/ACM Transactions on Networking (TON), 19(1), 170–183.

    Article  Google Scholar 

Download references

Acknowledgements

Project supported by the National Natural Science Foundation of China (Grants Nos. 61572435, 61472305, 61473222), the Natural Science Foundation of Shaanxi Province (Grants Nos. 2015JZ002, 2015JM6311), Ningbo Natural Science Foundation (Grant Nos. 2016A610035, 2017A610119), Complex Electronic System Simulation Laboratory (DXZT-JC-ZZ-2015-015).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Rong Gao.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Qi, X., Gao, R., Liu, L. et al. An improved fair channel hopping protocol for dynamic environments in cognitive radio networks. Wireless Netw 25, 903–911 (2019). https://doi.org/10.1007/s11276-017-1601-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-017-1601-3

Keywords

Navigation