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

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

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.

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

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

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

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

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:

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

Virtual transmission length (3)

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

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

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

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

Using the optimal value of p

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])

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

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

Distributed algorithm to get M Since Total_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

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