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Dynamic Tuning of the IEEE 802.11 Protocol to Achieve a Theoretical Throughput Limit

Explore tuning IEEE 802.11 back-off to maximize network capacity using geometric distribution for contention windows. Analyze model and optimize constant back-off for near-maximum performance.

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Dynamic Tuning of the IEEE 802.11 Protocol to Achieve a Theoretical Throughput Limit

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  1. Dynamic Tuning of the IEEE 802.11 Protocol to Achieve a Theoretical Throughput Limit Frederico Calì Marco Conti Enrico Gregori Presented by Andrew Tzakis

  2. Motivation • Wireless networks are slower than Wired networks • Mainly do to the use of a common medium • MAC – Coordinator of use • IEEE 802.11 Currently uses Binary back off • It can be improved

  3. Main Idea • Define an anylitical model in order to maximize the capacity of the network • This can be achieved through tuning of the IEEE 802.11 back off algorithm • Instead of using binary back off, sample contention window (CW) from a geometric distribution.

  4. Outline • Show that depending on network conditions, IEEE 802.11 can be far from max capacity. • Analytical model • Model IEEE 802.11 • Compare to max • An optimal constant back off can operate at close to the theoretical max • Find optimal CW • Define distributed algorithm

  5. Capacity • Capacity: Maximum value of bandwidth • ρmax = m/tv • m = Average time busy sending a successful message • tv = Virtual Transmission Window

  6. Virtual transmission length virtual transmission time (Tv) collision collision successful Ncol 1 Ncol 2 Average virtual transmission time length E(TV) can be expressed as …

  7. Math – Geometric Distribution • Run an experiment until a success is found • Success occurs with a probability of: p • Expected attempts before a success is 1/p

  8. Virtual transmission length (2) • Since the model is based on the CW being sampled from a geometric distribution Future behavior does not depend on past we can simplify:

  9. More on p • Assumption: Every node has a packet ready to send at all times (asymptotic condition) • In this case, p is the attempt probability • At the beginning of a slot this is the probability that the back off timer is equal to zero • P = 1/(E[B]+1) (property of geometric dist.) • E[B] = number of slots before a success (expected value) • p can be used to define each part of tv

  10. Virtual transmission length (3)

  11. Applying IEEE 802.11 to Model • Need to find corresponding p value • p(i)= 2/(E[CW(i)]+1) • This means we can find E[CW] for IEEE 802.11 which is based on binary back off • Approximate with linear sequence: |E[CW(n)] - E[CW(n-1)]| < ε

  12. Using Calculated CW Analytic and simulated results are very close! Model provides good approx.

  13. Capacity of IEEE 802.11 • ρmax is a function of p,M,q • p(i)= 2/(E[CW(i)]+1) • M = Number of nodes • q = Geometric distributed size of message q Ranges from 0.5 – 0.99

  14. Where is the room for improvement? High number of idle slots High number of collisions Balanced idle slots and collations

  15. Using the optimal value of p

  16. IEEE 802.11+ To find best CW • Through monitoring the radio, the collision length and number of collisions can be found • Use minimization algorithm to find p • This is too time consuming to solve • Create an approximation • Given: • When p is low tv is determined by E[Idle_p] • When p is high tv is determined by number of collisions • Balance should be when collision time equals idle time • E[Coll]*E[Nc] = (E[Nc]+1)*(E[Idle_p])

  17. Results of Estimation Estimated values are close To the optimal values

  18. Capacity with IEEE 802.11+ IEEE 802.11+ is much closer to the analytical bound

  19. Distributed algorithm to get M • SinceTotal_Idle_p = (E[Nc]+1)E[Idle_p] Total_Idle_p = (1-p)/M*p M = (1-p)/p*Total_Idle_p • Through tracking the idle time, and out estimated p we can calculate M

  20. Conclusion • Derived theoretical limit • Closely approximates IEEE 802.11 • Demonstrated that it is possible to tune the CW at runtime • Finding the pmincan be used in other algorithms as well (can make a hybrid)

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