Skip to main content
SpringerLink
Log in

Interference Bound for Local Channel Allocation

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

A lower bound of the total co-channel interference is proposed for the channel allocation problem when applied to a reduced set of nodes. The rest of the network nodes remain unaffected. This bound is independent of the particular channel allocation algorithm employed and no assumptions are made about the propagation model or the deployment scenario. Assuming that the bound is tight to the interference generated by the optimal channel allocation, its computation may help, for example, to estimate the minimum set of nodes for which channel allocation performs nearly-optimal while minimizing node reconfigurations. Another example of usage is the estimation of the minimum number of channels required for a given performance. The tightness of the proposed bound is evaluated through simulations, with a difference lower than 1 % in the conducted simulations. In addition, a sample use case—adaptive local channel allocation—is also provided.

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

Similar content being viewed by others

Notes

  1. Although we argue that channel allocation is triggered by a new node being turned on, it is only an example and the problem formulation makes no assumptions about when channel allocation is executed.

  2. Although we argue that only the new node and the most interfering nodes are subject to modify their channels, this is only a typical scenario explained for the sake of readability. The problem formulation requires no assumptions about the nodes belonging to sets N and S.

References

  1. Cisco visual networking index: Global mobile data traffic forecast update, 2014–2019 (2015). http://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/white_paper_c11-520862.html.

  2. Gabriel, C. (2013). WBA industry report 2013: Global trends in public Wi-Fi. http://www.wballiance.com/wba/wp-content/uploads/downloads/2013/11/WBA-Industry-Report-2013.pdf.

  3. Yilmaz, H., Tugcu, T., Alagöz, F., & Bayhan, S. (2013). Radio environment map as enabler for practical cognitive radio networks. IEEE Communications Magazine, 51(12), 162–169. doi:10.1109/MCOM.2013.6685772.

    Article  Google Scholar 

  4. Evolved universal terrestrial radio access (E-UTRA); further advancements for E-UTRA physical layer aspects, TR. 36.814 v9.0.0. Technical report, 3GPP (2010)

  5. Nie, N., & Comaniciu, C. (2006). Adaptive channel allocation spectrum etiquette for cognitive radio networks. Mobile Networks and Applications, 11(6), 779–797. doi:10.1007/s11036-006-0049.

    Article  Google Scholar 

  6. Federal Communications Commission. (2003). Establishment of interference temperature metric to quantify and manage interference and to expand available unlicensed operation in certain fixed mobile and satellite frequency bands. ET Docket 03-289, Notice of inquiry and proposed rulemaking.

  7. Kann, V., Khanna, S., Lagergren, J., & Panconesi, A. (1997). On the hardness of approximating max k-cut and its dual. Chicago Journal of Theoretical Computer Science,. doi:10.4086/cjtcs.1997.002.

    MathSciNet  MATH  Google Scholar 

  8. Audhya, G. K., Sinha, K., Ghosh, S. C., & Sinha, B. P. (2011). A survey on the channel assignment problem in wireless networks. Wireless Communications and Mobile Computing, 11(5), 583–609. doi:10.1002/wcm.898.

    Article  Google Scholar 

  9. Cheeneebash, J., Lozano, J. A., & Rughooputh, H. C. (2012). A survey on the algorithms used to solve the channel assignment problem. Recent Patents on Telecommunication, 1(1), 54–71. doi:10.2174/2211740711201010054.

    Article  Google Scholar 

  10. Newton, M. A. H., Pham, D. N., Tan, W. L., Portmann, M., Sattar, A. (2013) Principles and practice of constraint programming. In Proceedings of the 19th international conference, CP 2013, Uppsala, Sweden, September 16–20. Chapter Stochastic local search based channel assignment in wireless mesh networks (pp. 832–847). Berlin: Springer. doi:10.1007/978-3-642-40627-0_61

  11. Kamal, H., Coupechoux, M., & Godlewski, P. (2012). Tabu search for dynamic spectrum allocation in cellular networks. Transactions on Emerging Telecommunications Technologies, 23(6), 508–521. doi:10.1002/ett.2506.

    Article  Google Scholar 

  12. Yu, M., Ma, X. (2013) A new radio channel allocation strategy using simulated annealing and gibbs sampling. In: Global communications conference (GLOBECOM), 2013 IEEE (pp. 1259–1264). doi:10.1109/GLOCOM.2013.6831247

  13. Lima, M. P., Rodrigues, T. B., Alexandre, R. F., Takahashi, R. H. C., Carrano, E. G. (2014) Using evolutionary algorithms for channel assignment in 802.11 networks. In 2014 IEEE symposium on computational intelligence for communication systems and networks (CIComms) (pp. 1–8). doi:10.1109/CICommS.2014.7014634

  14. Wang, J., Cai, Y., & Yin, J. (2011). Multi-start stochastic competitive hopfield neural network for frequency assignment problem in satellite communications. Expert Systems with Applications, 38(1), 131–145. doi:10.1016/j.eswa.2010.06.027.

    Article  Google Scholar 

  15. Narayanan, L. (2002). Channel assignment and graph multicoloring. Hoboken: Wiley. doi:10.1002/0471224561.ch4.

    Book  Google Scholar 

  16. Kim, S. J., & Cho, I. (2013). Graph-based dynamic channel assignment scheme for femtocell networks. IEEE Communications Letters, 17(9), 1718–1721. doi:10.1109/LCOMM.2013.071013.130585.

    Article  Google Scholar 

  17. Wang, B., Wu, Y., & Liu, K. R. (2010). Game theory for cognitive radio networks: An overview. Computer Networks, 54(14), 2537–2561.

    Article  MATH  Google Scholar 

  18. Frieze, A. M., Jerrum, M. (1995). Improved approximation algorithms for max k-cut and max bisection. In: Proceedings of the 4th international conference on integer programming and combinatorial optimization (IPCO) (pp. 1–13). London: Springer.

Download references

Acknowledgments

The finantial support of the Spanish Ministry of Economy and Competitiveness (Project TIN2013-46223-P) is gratefully acknowledged.

Author information

Authors and Affiliations

  1. ETSI Informatica y de Telecomunicacion, C/Periodista Daniel Saucedo Aranda s/n, 18071, Granada, Spain

    Jorge Navarro-Ortiz, Pablo Ameigeiras, Juan J. Ramos-Munoz, Jonathan Prados-Garzon & Juan M. Lopez-Soler

Authors
  1. Jorge Navarro-Ortiz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pablo Ameigeiras
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Juan J. Ramos-Munoz
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jonathan Prados-Garzon
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Juan M. Lopez-Soler
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jorge Navarro-Ortiz.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Navarro-Ortiz, J., Ameigeiras, P., Ramos-Munoz, J.J. et al. Interference Bound for Local Channel Allocation. Wireless Pers Commun 92, 1559–1574 (2017). https://doi.org/10.1007/s11277-016-3621-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-016-3621-1

Keywords

Search

Navigation