1. IEEE 802.11 Protocol: Design and Performance Evaluation of An Adaptive Backoff Mechanism
JSAC, vol.18, No.9, Sept. 2000
Authors: F. Cali, M. Conti and E. Gregori
April 05, 2006
Chunyu Hu, University of Illinois at Urbana-Champaign

2. Chunyu Hu, University of Illinois at Urbana-Champaign
Outline
Overview of backoff algorithm
Cali’s model
Optimality condition
The adaptive backoff algorithm
Performance evaluation
Conclusion
April 05, 2006
Chunyu Hu, University of Illinois at Urbana-Champaign

3. Overview of 802.11 Backoff Algorithm
The time after a busy period is slotted
A node transmits if its backoff timer counts down to 0
Freeze the backoff timer if the medium becomes busy
Resume the backoff time if the medium becomes idle for DIFS time
April 05, 2006
Chunyu Hu, University of Illinois at Urbana-Champaign

4. Chunyu Hu, University of Illinois at Urbana-Champaign
Cali’s Model
Independent access assumption:
Every node transmits at the beginning of idle slots with probability p
The access processes are regenerative:
idle period, virtual transmission time
The protocol capacity (i.e. total throughput):
Avg. msg length
virtual transmission time
April 05, 2006
Chunyu Hu, University of Illinois at Urbana-Champaign

5. Cali’s Model (Continued)
Derive E[tv] (renewal process)
Derive them based on the independent access assumption
Nc, r.v. ~ Negative Binomial (1, PColl/PSuss)
Idle_p, r.v. ~ Negative Binomial (1, PIdle)
April 05, 2006
Chunyu Hu, University of Illinois at Urbana-Champaign

6. The Optimality Condition
Choosing p (=pmin) can maximize max
pmin approximately satisfies the following condition:
Left: the average channel time spent in collision
Right: the average channel time spent in idle
April 05, 2006
Chunyu Hu, University of Illinois at Urbana-Champaign

7. Chunyu Hu, University of Illinois at Urbana-Champaign
Properties
The pmin value from the optimality condition guarantees that
E[NC] < 1
Throughput obtained > 0
Proof of the above and derivation of the condition, see
[1] F. Cali, M. Conti and E. Gregori, “Dynamic Tuning of the IEEE Protocol to Achieve a Theoretical Throughput Limit,” IEEE Trans. on Networking, Vol.8, No.6, Dec. 2000
[2] R.G. Gallager, “A Perspective on Multiaccess Channels”, IEEE Trans. Information Theory, vol.31, pp , 1985
April 05, 2006
Chunyu Hu, University of Illinois at Urbana-Champaign

8. An Illustration of pmin Estimate [1]
April 05, 2006
Chunyu Hu, University of Illinois at Urbana-Champaign

9. Protocol Capacity Comparision
Study the protocol capacity of theoretical bound, IEEE and the proposed dynamic IEEE
April 05, 2006
Chunyu Hu, University of Illinois at Urbana-Champaign

10. From the Optimality Condition to the Adaptive Backoff Algorithm
Given the probability p, can estimate M from measured E[Idle_p]:
M known, can compute p to satisfy the optimality condition from measured E[Coll]:
April 05, 2006
Chunyu Hu, University of Illinois at Urbana-Champaign

11. Chunyu Hu, University of Illinois at Urbana-Champaign
Outline
Overview of backoff algorithm
Cali’s model
Optimality condition
The adaptive backoff algorithm
Performance evaluation
Conclusion
April 05, 2006
Chunyu Hu, University of Illinois at Urbana-Champaign

12. The Adaptive Backoff Algorithm – Measurements
Measured variables (E[Idle_p] and E[Coll]) and estimated variables (M, p) are updated using the moving averaging window, e.g.,
 -- smoothing factor
April 05, 2006
Chunyu Hu, University of Illinois at Urbana-Champaign

13. The Adaptive Backoff Algorithm – Algorithm
Performed by each node at the end of every transmission interval
April 05, 2006
Chunyu Hu, University of Illinois at Urbana-Champaign

14. Chunyu Hu, University of Illinois at Urbana-Champaign
Outline
Overview of backoff algorithm
Cali’s model
Optimality condition
The adaptive backoff algorithm
Performance evaluation
Conclusion
April 05, 2006
Chunyu Hu, University of Illinois at Urbana-Champaign

15. Performance Evaluation (1)
The estimates of variables under different α, M = 10.
April 05, 2006
Chunyu Hu, University of Illinois at Urbana-Champaign

16. Performance Evaluation (2)
The estimates of variables under different α, M = 20.
April 05, 2006
Chunyu Hu, University of Illinois at Urbana-Champaign

17. Performance Evaluation (3)
Study the convergence rate.
April 05, 2006
Chunyu Hu, University of Illinois at Urbana-Champaign

18. Performance Evaluation (4)
Study the convergence rate when the start state is wrong. M = 10.
April 05, 2006
Chunyu Hu, University of Illinois at Urbana-Champaign

19. Chunyu Hu, University of Illinois at Urbana-Champaign
Conclusion
Model MAC and study the protocol capacity
Estimate pmin with an optimality condition
Design an adaptive backoff algorithm
April 05, 2006
Chunyu Hu, University of Illinois at Urbana-Champaign