Skip to main content

Advertisement

SpringerLink
Log in

Heuristic Algorithms for Underlay Spectrum Sharing in Cognitive Radio Networks

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Cognitive radio is the enabling technology that allows the design of dynamic spectrum sharing algorithms that makes unlicensed users able to use frequency bands owned by license holders. Hence, this technology can be considered as the perfect candidate to enhance the spectrum allocation for the next generation of wireless networks. In this paper, we propose two low complexity heuristic algorithms for dynamic spectrum sharing in cognitive radio networks assuming the spectrum underlay paradigm. We mean by spectrum underlay a wireless system where primary and secondary users can transmit simultaneously on the same frequency bands at the condition that the interference perceived by the primary receivers is kept below a determined threshold. The formulated spectrum sharing is shown to be equivalent to a variant of knapsack problems which is \({\mathcal {NP}}\)-hard. Therefore, resolving the tradeoff between computational complexity and system performance is the main concern in the design of the proposed algorithms. The first algorithm is based on a graph-theory. First, the cognitive radio network is modeled as an undirected weighted graph. The spectrum sharing problem is then reduced to the one of finding a sensitive vertex coloring of a subgraph on the constructed graph. The second algorithm involves the use of genetic algorithms and uses a mixed integer coding. We show that the proposed algorithms reduce considerably the computational complexity of finding a near optimal solution.

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

Similar content being viewed by others

References

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

    Article  Google Scholar 

  2. Matinmikko, M., Mustonen, M., Roberson, D., Paavola, J., Hoyhtya, M., Yrjola, S., et al. (2014). Overview and comparison of recent spectrum sharing approaches in regulation and research: From opportunistic unlicensed access towards licensed shared access. In Proceedings of IEEE DYSPAN’14 (pp. 92–102).

  3. Goldsmith, A., Jafar, S., Maric, I., & Srinivasa, S. (2009). Breaking spectrum gridlock with cognitive radios: An information theoretic perspective. Proceedings of the IEEE, 97(5), 894–914.

    Article  Google Scholar 

  4. Song, M., Xin, C., Zhao, Y., & Cheng, X. (2012). Dynamic spectrum access: From cognitive radio to network radio. IEEE Wireless Communications, 19(1), 23–29.

    Article  Google Scholar 

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

    Article  Google Scholar 

  6. Maric, I., Goldsmith, A., Kramer, G., & Shamai, S. (2007). On the capacity of interference channels with a partially-cognitive transmitter. In Proceedings of IEEE ISIT’07 (pp. 2156–2160).

  7. Zhang, R., Kang, X., & Liang, Y.-C. (2009). Protecting primary users in cognitive radio networks: Peak or average interference power constraint? In Proceedings of IEEE ICC’09 (pp. 1–5).

  8. Jazaie, M., & Sharafat, A. (2015). Downlink capacity and optimal power allocation in hybrid underlay-interweave secondary networks. IEEE Transactions on Wireless Communications, 14(5), 2562–2570.

    Article  Google Scholar 

  9. Le, T., & Navaie, K. (2015). On the interference tolerance of the primary system in cognitive radio networks. IEEE Wireless Communications Letters, 4(3), 281–284.

    Article  Google Scholar 

  10. Le, L. B., & Hossain, E. (2008). Resource allocation for spectrum underlay in cognitive radio networks. IEEE Transactions on Wireless Communications, 7(12), 5306–5315.

    Article  Google Scholar 

  11. El Ferkouss, O., & Ajib, W. (2012). Game theory based resource allocation for cognitive radio networks. In Proceedings of IEEE GLOBECOM’12 (pp. 1–6).

  12. Zheng, H., & Peng, C. (2005). Collaboration and fairness in opportunistic spectrum access. In Proceedings of IEEE ICC’05 (pp. 3132–3136).

  13. Luo, Z. Q., & Zhang, S. (2008). Dynamic spectrum management: Complexity and duality. IEEE Journal of Selected Topics in Signal Processing, 2(1), 57–73.

    Article  Google Scholar 

  14. Zheng, L., & Tan, C. W. (2014). Maximizing sum rates in cognitive radio networks: Convex relaxation and global optimization algorithms. IEEE Journal on Selected Areas in Communications, 32(3), 667–680.

    Article  Google Scholar 

  15. Driouch, E., Ajib, W., & Jalloul, T. (2012). A novel antenna assignment algorithm for spectrum underlay in cognitive MIMO networks. In Proceedings of IEEE VTC Fall’12 (pp. 1–5).

  16. Yates, R., Raman, C., & Mandayam, N. (2006). Fair and efficient scheduling of variable rate links via a spectrum server. In Proceedings of IEEE ICC’06 (pp. 5246–5251).

  17. Ileri, O., Samardzija, D., & Mandayam, N. (2005). Demand responsive pricing and competitive spectrum allocation via a spectrum server. In Proceedings of IEEE DySPAN’05 (pp. 194–202).

  18. Kellerer, H., Pferschy, U., & Pisinger, D. (2004). Knapsack problems. Berlin: Springer.

    Book  MATH  Google Scholar 

  19. Elliott, R., & Krzymien, W. (2009). Downlink scheduling via genetic algorithms for multiuser single-carrier and multicarrier MIMO systems with dirty paper coding. IEEE Transactions on Vehicular Technology, 58(7), 3247–3262.

    Article  Google Scholar 

  20. Sigdel, S., Elliott, R., Krzymien, W., & Al-Shalash, M. (2009). Greedy and genetic user scheduling algorithms for multiuser MIMO systems with block diagonalization. In Proceedings of IEEE VTC’09 Fall (pp. 1–6).

  21. Li, F., Zhu, D., Tian, F., & Li, H. (2011). Cognitive radio spectrum sharing using improved quantum genetic algorithm. In Proceedings of international conference on wireless communications and signal processing (WCSP) (pp. 1–6).

  22. Ngo, D., Tellambura, C., & Nguyen, H. (2009). Efficient resource allocation for ofdma multicast systems with spectrum-sharing control. IEEE Transactions on Vehicular Technology, 58(9), 4878–4889.

    Article  Google Scholar 

  23. Yang, M., Li, Y., Liu, J., Jin, D., Yuan, J., & Zeng, L. (2014). Opportunistic spectrum sharing for wireless virtualization. In Proceedings of IEEE WCNC’14 (pp. 1803–1808).

  24. Chen, G., Yu, X.-H., & Wang, J. (2001). Adaptive channel estimation and dedicated pilot power adjustment based on the fading-rate measurement for a pilot-aided CDMA system. IEEE Journal on Selected Areas in Communications, 19(1), 132–140.

    Article  Google Scholar 

  25. Goussevskaia, O., Oswald, Y. A., & Wattenhofer, R. (2007). Complexity in geometric SINR. In Proceedings of ACM MobiHoc’07 (pp. 100–109).

  26. Bretthauer, K. M., & Shetty, B. (2002). The nonlinear knapsack problem: Algorithms and applications. European Journal of Operational Research, 138(3), 459–472.

    Article  MathSciNet  MATH  Google Scholar 

  27. Garey, M. R., & Johnson, D. S. (1979). Computers and intractability: A guide to the theory of NP-completeness. San Francisco, CA: Freeman.

    MATH  Google Scholar 

  28. Sakai, S., Togasaki, M., & Yamazaki, K. (2003). A note on greedy algorithms for the maximum weighted independent set problem. Discrete Applied Mathematics, 126(23), 313–322.

    Article  MathSciNet  MATH  Google Scholar 

  29. Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2009). Introduction to algorithms (2nd ed.). Cambridge: MIT Press.

    MATH  Google Scholar 

  30. Holland, J. H. (1975). Adaptation in natural and artificial systems. Ann Arbor: University of Michigan Press.

    Google Scholar 

  31. Wright, A. H. (1991). Genetic algorithms for real parameter optimization. In Proceedings of the 1st workshop on foundations of genetic algorithms (Vol. 1) (pp. 205–218). Morgan Kaufmann.

  32. Deep, K., Singh, K. P., Kansal, M., & Mohan, C. (2009). A real coded genetic algorithm for solving integer and mixed integer optimization problems. Applied Mathematics and Computation, 212(2), 505–518.

    Article  MathSciNet  MATH  Google Scholar 

  33. Goldberg, D. E., & Deb, K. (1991). A comparative analysis of selection schemes used in genetic algorithms. In Proceedings of the 1st workshop on foundations of genetic algorithms (Vol. 1) (pp. 69–93). Morgan Kaufmann.

  34. Deep, K., & Thakur, M. (2007). A new crossover operator for real coded genetic algorithms. Applied Mathematics and Computation, 188(1), 895–911.

    Article  MathSciNet  MATH  Google Scholar 

  35. Deep, K., & Thakur, M. (2007). A new mutation operator for real coded genetic algorithms. Applied Mathematics and Computation, 193(1), 211–230.

    Article  MathSciNet  MATH  Google Scholar 

  36. Jones, D. R. (2009). DIRECT global optimization algorithm. In C. Floudas & P. Pardalos (Eds.), Encyclopedia of optimization (2nd ed.). New York, NY: Springer.

    Google Scholar 

  37. Bjorkman, M., & Holmstrom, K. (1999). Global optimization using the direct algorithm in matlab. Advanced Modeling and Optimization, 1(2), 17–37.

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Université du Québec à Montréal, Montreal, Canada

    Elmahdi Driouch & Wessam Ajib

Authors
  1. Elmahdi Driouch
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wessam Ajib
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Elmahdi Driouch.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Driouch, E., Ajib, W. Heuristic Algorithms for Underlay Spectrum Sharing in Cognitive Radio Networks. Wireless Pers Commun 96, 2563–2583 (2017). https://doi.org/10.1007/s11277-017-4312-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-017-4312-2

Keywords

Search

Navigation