A Multiphase Queueing Model for Performance Analysis of a Multi-hop IEEE 802.11 Wireless Network with DCF Channel Access

  • Andrey LarionovEmail author
  • Vladimir Vishnevsky
  • Olga Semenova
  • Alexander Dudin
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1109)


Methods of queueing theory are often used for telecommunication systems performance evaluation. According to this approach, all MAC and PHY layer protocols details are modelled with a given service time distribution, and the precision of the estimated properties depends on this distribution function selection. While for Ethernet and wireless relay networks the service time is roughly equal to the sum of packet and headers sizes divided by the channel bitrate, it is not so easy to estimate the service time for wireless networks channels based on CSMA/CA including IEEE 802.11 (WiFi) and IEEE 802.15.4 (ZigBee) due to random exponential backoff and collisions. In this paper we use a simple model of the saturated channel described as a semi-Markov random absorbing process, which is based on Bianchi model [2]. We use this process to find a service time phase-type (PH) distribution matching the first two moments of the semi-Markov process. We also use channel simulation model to sample transmission delays in unsaturated mode and find another PH-distribution using G-FIT [12] algorithm. Then we use these PH-distributions to estimate end-to-end delays, queue sizes and nodes utilization in a multi-hop wireless network with linear topology using \(M/PH/1/N \rightarrow \bullet /PH/1/N \bullet \dots \bullet /PH/1/N\) queueing network. All results are compared to wireless network simulation model, and demonstrate the cases in which the queueing network tends to provide results close to real network performance.


CSMA/CA DCF Semi-Markov random absorbing process PH-distribution Queueing networks Multi-hop wireless networks 


  1. 1.
    Banchs, A., Serrano, P., Azcorra, A.: End-to-end delay analysis and admission control in 802.11 DCF WLANs. Comput. Commun. 29(7), 842–854 (2006)CrossRefGoogle Scholar
  2. 2.
    Bianchi, G.: Performance analysis of the IEEE 802.11 distributed coordination function. IEEE J. Sel. Areas Commun. 18(3), 535–547 (2000)CrossRefGoogle Scholar
  3. 3.
    Chatzimisios, P., Vitsas, V., Boucouvalas, A.: Throughput and delay analysis of IEEE 802.11 protocol. In: Proceedings 3rd IEEE International Workshop on System-on-Chip for Real-Time Applications, pp. 168–174. IEEE (2002)Google Scholar
  4. 4.
    Dai, L., Sun, X.: A unified analysis of IEEE 802.11 DCF networks: stability, throughput, and delay. IEEE Trans. Mob. Comput. 12(8), 1558–1572 (2013)CrossRefGoogle Scholar
  5. 5.
    Dong, L.F., Shu, Y.T., Chen, H.M., Ma, M.D.: Packet delay analysis on IEEE 802.11 DCF under finite load traffic in multi-hop ad hoc networks. Sci. China Ser. F Inf. Sci. 51(4), 408–416 (2008)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Felemban, E., Ekici, E.: Single hop IEEE 802.11 DCF analysis revisited: accurate modeling of channel access delay and throughput for saturated and unsaturated traffic cases. IEEE Trans. Wirel. Commun. 10(10), 3256–3266 (2011)CrossRefGoogle Scholar
  7. 7.
    Haghani, E., Krishnan, M.N., Zakhor, A.: A method for estimating access delay distribution in IEEE 802.11 networks. In: 2011 IEEE Global Telecommunications Conference - GLOBECOM 2011, pp. 1–6. IEEE (December 2011)Google Scholar
  8. 8.
    Hung, F.Y., Marsic, I.: Access delay analysis of IEEE 802.11 DCF in the presence of hidden stations. In: IEEE GLOBECOM 2007–2007 IEEE Global Telecommunications Conference, pp. 2541–2545. IEEE (November 2007)Google Scholar
  9. 9.
    Issariyakul, T., Niyato, D., Hossain, E., Alfa, A.: Exact distribution of access delay in IEEE 802.11 DCF MAC. In: GLOBECOM 2005. IEEE Global Telecommunications Conference, 2005, p. 5. IEEE (2005). pp.-2538Google Scholar
  10. 10.
    Lauwens, B., Scheers, B., Van de Capelle, A.: Performance analysis of unslotted CSMA/CA in wireless networks. Telecommun. Syst. 44(1–2), 109–123 (2010)CrossRefGoogle Scholar
  11. 11.
    Sakurai, T., Vu, H.: MAC access delay of IEEE 802.11 DCF. IEEE Trans. Wirel. Commun. 6(5), 1702–1710 (2007)CrossRefGoogle Scholar
  12. 12.
    Thummler, A., Buchholz, P., Telek, M.: A novel approach for fitting probability distributions to real trace data with the EM algorithm. In: 2005 International Conference on Dependable Systems and Networks (DSN 2005), pp. 712–721. IEEE (2005)Google Scholar
  13. 13.
    Tickoo, O., Sikdar, B.: Modeling queueing and channel access delay in unsaturated IEEE 802.11 random access MAC based wireless networks. IEEE/ACM Trans. Netw. 16(4), 878–891 (2008)CrossRefGoogle Scholar
  14. 14.
    Vardakas, J., Papapanagiotou, I., Logothetis, M., Kotsopoulos, S.: On the end-to-end delay analysis of the IEEE 802.11 distributed coordination function. In: Second International Conference on Internet Monitoring and Protection (ICIMP 2007), pp. 16–16. IEEE (July 2007)Google Scholar
  15. 15.
    Vishnevsky, V., Dudin, A., Kozyrev, D., Larionov, A.: Methods of performance evaluation of broadband wireless networks along the long transport routes. In: Vishnevsky, V., Kozyrev, D. (eds.) DCCN 2015. CCIS, vol. 601, pp. 72–85. Springer, Cham (2016). Scholar
  16. 16.
    Vishnevsky, V., Larionov, A., Semenova, O., Ivanov, R.: State reduction in analysis of a tandem queueing system with correlated arrivals. In: Dudin, A., Nazarov, A., Kirpichnikov, A. (eds.) ITMM 2017. CCIS, vol. 800, pp. 215–230. Springer, Cham (2017). Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.V.A. Trapeznikov Institute of Control Sciences of RASMoscowRussia
  2. 2.Belarusian State UniversityMinskBelarus

Personalised recommendations