On Backoff in Fading Wireless Channels

  • SeonYeong Han
  • Nael B. Abu-Ghazaleh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5198)


We consider the impact of transmission errors on the backoff algorithm behavior in the IEEE 802.11 protocol. Specifically, since the backoff algorithm assumes that all packet losses are due to collisions, it unnecessarily backs off when a packet is lost due to a transmission error. Two performance problems arise as a result: (1) low throughput, due to unnecessary loss of transmission time; and (2) unfairness when two competing links have different transmission error rates. In this paper, we characterize this problem and propose three solutions to it. The solutions aim to provide discrimination between transmission errors and collisions such that the sender can back off appropriately. The first algorithm relies on receiver discrimination and feedback; the receiving radio can in many instances differentiate between collisions and transmission errors. The second algorithm estimates the clear channel quality, and backs off if the observed quality deviates from the clear channel quality (indicating collisions). The third algorithm develops the probability of collision as a function of the number of observed idle slots during contention, and uses this probability to control the backoff algorithm. We show via simulation that the techniques significantly improve both performance and fairness of IEEE 802.11 in the presence of transmission errors.


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  1. 1.
    IEEE 802.11 – The Working Group for Wireless LANs: Ieee 802.11 standard, edition (1999) Including IEEE 802.11a and IEEE 802.11b Extension (1999), http://www.ieee802.org/11/index.html
  2. 2.
    Tobagi, F., Klienrock, L.: Packet switching in radio channels: Part II: The hidden terminal problem in carrier sense multiple access and the busy tone solution. IEEE Transactions on Communication, 1417–1433 (1975)Google Scholar
  3. 3.
    Garetto, M., Shi, J., Knightly, E.W.: Modeling media access in embedded two-flow topologies of multi-hop wireless networks. In: MobiCom 2005, pp. 200–214. ACM Press, New York (2005)CrossRefGoogle Scholar
  4. 4.
    Heusse, M., Rousseau, F., Guillier, R., Duda, A.: Idle sense: an optimal access method for high throughput and fairness in rate diverse wireless lans. SIGCOMM Comput. Commun. Rev. 35(4), 121–132 (2005)CrossRefGoogle Scholar
  5. 5.
    Woo, G., Kheradpour, P., Shen, D., Katabi, D.: Beyond the bits: Cooperative packet recovery using physical layer information. In: Proc. ACM International Conference on Mobile Computing and Networking (Mobicom) (2007)Google Scholar
  6. 6.
    Burns, L., Podell, A., Fisher, D., Ramachandran, R. (Radio based collision detection for wireless communication system) US Patent Issued on August 12 (1997)Google Scholar
  7. 7.
    Information Sciences Institute: NS-2 network simulator. Software Package (2005), http://www.isi.edu/nsnam/ns/
  8. 8.
    Bianchi, G.: Performance analysis of the ieee 802.11 distributed coordinationfunction. IEEE Journal on Selected Areas in Communications (2000)Google Scholar
  9. 9.
    Chua, K.C., Lye, K.: Backoff considerations in csma/cd lan with single time-varying channel. Electronics Letters 27(9), 747–748 (1991)CrossRefGoogle Scholar
  10. 10.
    Nadeem, T., Agrawala, A.: Ieee 802.11 dcf enhancements for noisy environments. In: International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) (2004)Google Scholar
  11. 11.
    Biaz, S., Vaidya, N.H.: Discriminating congestion losses from wireless losses using inter-arrival times at the receiver. In: ASSET 1999: Proceedings of the 1999 IEEE Symposium on Application - Specific Systems and Software Engineering and Technology, p. 10. IEEE Computer Society Press, Washington (1999)CrossRefGoogle Scholar
  12. 12.
    Li, Y., Su, F., Fan, Y., Xu, H.: End-to-end differentiation of congestion and wireless losses using a fuzzy arithmetic based on relative entropy. In: International Conference on Systems and Networks Communication (ICSNC 2006), p. 15 (2006)Google Scholar
  13. 13.
    Biaz, S., Vaidya, N.H.: “de-randomizing” congestion losses to improve tcp performance over wired-wireless networks. IEEE/ACM Trans. Netw. 13(3), 596–608 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • SeonYeong Han
    • 1
    • 2
  • Nael B. Abu-Ghazaleh
    • 1
    • 2
  1. 1.Computer Science Dept.State University of New York at Binghamton 
  2. 2.School of Computer ScienceCarnegie Mellon University - Qatar 

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