Wireless Networks

, Volume 25, Issue 8, pp 4663–4682 | Cite as

A game theoretical approach to model the channel selection dynamics in non-coordinated IEEE 802.11 networks

  • Sérgio L. D. L. Gramacho
  • Gustavo B. FigueiredoEmail author
  • Lasaro Camargos


The massive deployment of Wireless Local Area Networks has made interference mitigation between neighboring networks a challenging issue. These uncoordinated access networks aim at improving their operation by choosing the best wireless channel available, characterizing a competition over the restricted set of possible channels. This work analyses this competition using Game Theory and Markov Chains models, showing that such competitive behavior can lead to Nash Equilibria and that outcomes mostly will not be maximal. Additionally, partially and fully cooperative models are proposed and evaluated, allowing (a) individual players to increase global results using arbitrarily computed and non-rational moves, and (b) achieving maximal outcomes when considering the cooperation of up to all players.


Interference mitigation Heuristics IEEE 802.11 WLAN 



This research was partially supported by CAPES-Brazil.


  1. 1.
    Achanta, M. (2006). Method and apparatus for least congested channel scan for wireless access points. CA Patent App. CA 2, 582,406.Google Scholar
  2. 2.
    Akl, R., & Arepally, A. (2007). Dynamic channel assignment in IEEE 802.11 networks. In 2007 IEEE international conference on portable information devices (pp. 1–5). IEEE. URL
  3. 3.
    Al-Rizzo, H., Haidar, M., Akl, R., & Chan, Y. (2007). Enhanced channel assignment and load distribution in IEEE 802.11 WLANs. In IEEE international conference on signal processing and communications (pp. 768–771).
  4. 4.
    Analitycs, S. (2014). Global broadband and Wlan (Wi-Fi) networked households forecast 2009–2018. Tech. rep., Strategy Analitycs available online (
  5. 5.
    Chieochan, S., Hossain, E., & Diamond, J. (2010). Channel assignment schemes for infrastructure-based 802.11 WLANs: A survey. IEEE Communications Surveys & Tutorials, 12(1), 124–136. Scholar
  6. 6.
    Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2001). Introduction to algorithms (2nd ed., Vol. 7). Cambridge, MA: The MIT Press.zbMATHGoogle Scholar
  7. 7.
    Deng, F., Davis, M., & Qin, Z. (2014). An autonomous channel selection algorithm based upon neighbour forcing for multi-channel IEEE 802.11 networks. In 10th international conference on wireless communications, networking and mobile computing (WiCOM 2014), (pp. 356–360).
  8. 8.
    Dolińska, I., Masiukiewicz, A., & Rządkowski, G. (2014). Channel selection in home 802.11 standard networks. In The 10th international conference on digital technologies 2014, (pp. 57–63).
  9. 9.
    Ergin, M. A., Ramachandran, K., & Gruteser, M. (2008). An experimental study of inter-cell interference effects on system performance in unplanned wireless \(\{\text{ LAN }\}\) deployments. Computer Networks, 52(14), 2728–2744. Scholar
  10. 10.
    Ghahfarokhi, B. S. (2015). Distributed QoE-aware channel assignment algorithms for IEEE 802.11 WLANs. Wireless Networks, 21(1), 21–34. Scholar
  11. 11.
    Hou, Y., Li, M., & Yang, D. (2016). A game theoretical approach to coexistence of heterogeneous MIMO wireless networks with interference cancellation. In 2016 international conference on computing, networking and communications (ICNC) (pp. 1–5).
  12. 12.
    Hua, C., & Zheng, R. (2010). On link-level starvation in dense 802.11 wireless community networks. Computer Networks, 54(17), 3159–3172. Scholar
  13. 13.
    Jabri, I., Krommenacker, N., Divoux, T., & Soudani, A. (2008). IEEE 802.11 load balancing: An approach for QoS enhancement. International Journal of Wireless Information Networks, 15(1), 16–30. Scholar
  14. 14.
    Levin, D., Peres, Y., & Wilmer, E. (2009). Markov chains and mixing times. Providence, RI: American Mathematical Society.zbMATHGoogle Scholar
  15. 15.
    Mahmood, A., & Jäntti, R. (2011). A decision theoretic approach for channel ranking in crowded unlicensed bands. Wireless Networks, 17(4), 907–919. Scholar
  16. 16.
    Meharouech, A., Elias, J., & Mehaoua, A. (2016). A two-stage game theoretical approach for interference mitigation in body-to-body networks. Computer Networks, 95, 15–34. Scholar
  17. 17.
    Minhas, Q., Mahmood, H., & Malik, H. (2016). Channel selection for simultaneous move game in cognitive radio ad hoc networks. Wireless Networks, 22(1), 61–68. Scholar
  18. 18.
    Norris, J. R. (1998). Markov chains. Cambridge: Cambridge University Press. URL
  19. 19.
    Osborne, M., & Rubinstein, A. (1994). A course in game theory. Cambridge, MA: The MIT Press.zbMATHGoogle Scholar
  20. 20.
    Qin, Z., Wang, J., Chen, J., Sun, Y., Du, Z., & Xu, Y. (2016). Opportunistic channel access with repetition time diversity and switching cost: A block multi-armed bandit approach. Wireless Networks,. Scholar
  21. 21.
    Wang, X., Derakhshani, M., & Le-Ngoc, T. (2014). Self-organizing channel assignment for high density 802.11 WLANs. In 2014 IEEE 25th annual international symposium on personal, indoor, and mobile radio communication (PIMRC) (pp. 1637–1641). IEEE. URL
  22. 22.
    Wang, Y., Qian, M., Han, X., Zhou, Y., & Shi, J. (2014). Game-theoretic power control for interference mitigation in two-tier small cell networks. In IEEE vehicular technology conference (Vol. 2015).
  23. 23.
    Weibull, J. W. (1997). Evolutionary game theory. Cambridge, MA: MIT Press.zbMATHGoogle Scholar
  24. 24.
    Young, H. P. (1993). The evolution of conventions. Econometrica, 1, 57–84. Scholar
  25. 25.
    Yue, X., Wong, C. F., & Chan, S. H. G. (2011). CACAO: Distributed client-assisted channel assignment optimization for uncoordinated WLANs. IEEE Transactions on Parallel and Distributed Systems, 22(9), 1433–1440. Scholar
  26. 26.
    Zheng, J., Cai, Y., & Anpalagan, A. (2015). A stochastic game-theoretic approach for interference mitigation in small cell networks. IEEE J WCOML, 19(2), 251–254. Scholar
  27. 27.
    Zhou, K., Jia, X., Xie, L., Chang, Y., & Tang, X. (2012). Channel assignment for WLAN by considering overlapping channels in SINR interference model. In International conference on computing, networking and communications (ICNC 2012) (pp. 1005–1009). IEEE, Maui, HI, US. URL

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Sérgio L. D. L. Gramacho
    • 1
  • Gustavo B. Figueiredo
    • 2
    Email author
  • Lasaro Camargos
    • 3
  1. 1.Department of Mathematics and Computer ScienceEmory UniversityAtlantaUSA
  2. 2.Department of Computer ScienceFederal University of BahiaSalvadorBrazil
  3. 3.Faculty of ComputingFederal University of UberlandiaUberlândiaBrazil

Personalised recommendations