1. QoS Provisioning for IEEE 802.11 MAC Protocols
Ye Ge
The Ohio State University
Jennifer C. Hou
University of Illinois at Urbana-Champaign
Sunghyun Choi
Seoul National University
Presented by Andrew Tzakis

2. Outline Motivation Overview of IEEE 802.11 MAC Protocols
PMAC --- An Analytical Model for Multi-Class p-persistent version of DCF to Achieve Flow Rate Differentiation
Implementation Issues for PMAC
Analysis of Arbitration IFS (AIFS) in e
Conclusion

3. Motivation
Wireless Local Area Networks (WLANs) based on IEEE standard are getting very popular.
Existing Applications
Future Applications s
File Transfer
Web Access and Browsing ……
Audio/Video Streaming
HDTV
Smart Home Networking
Best Effort
Require QoS Support !
(delay, rate etc.)

4. Existing Solutions
Legacy IEEE MAC

5. Existing Solutions - DCF
Distributed Coordination Function (DCF)
Based on Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA)
ACK frames are used to acknowledge the successful reception of data frames
Each station maintains a Contention Window (CW) and a Backoff Timer

6. Existing Solutions - DCF
In the backoff stage, the station decreases the Backoff Timer by one time slot for every consecutive idle slot after the medium has been idle for a DIFS. The backoff timer decreasing is suspended whenever the channel is sensed busy and resumed after the channel has been idle for a DIFS again.
The station is allowed to transmit whenever the backoff timer reaches zero
The Backoff Timer is initialized according to the Binary Exponential Backoff (BEB) algorithm
Backoff Timer = Random() × aSlotTime
Random() is a random integer drawn from an uniform distribution on the interval [0, CW]
CW is double every unsuccessful transmission until reaching its maximal value allowed, that is,
CWnew = min(CWold * 2 – 1, CWmax )
CWinitial = CWmin= 31, CWmax= 1023
Limited retry times
Basic Access

7. Existing Solutions - PCF
Point Coordinated Function (PCF)
Contention Free Period (CFP) and Contention Period (CP) alternatively appear periodically.
Within CFP, the Access Point gains control of the channel using PCF Inter-frame Space (PIFS), which is shorter than DIFS.
AP may poll each station to transmit in CFP without incurring frame collision.

8. Problems with the current solutions
Why is DCF not enough?
It treats all data traffic in a FCFS, best-effort manner
All stations contend for the wireless medium with the same priority
No differentiation between data flows with QoS requirements
Why is PCF not enough?
Periodical appearance of CFP and CP limiting its flexibility (because it is difficult to find a repetition period fits all flow requirements)
Lacking mechanism to specify traffic requirement

9. IEEE e Draft
EDCA is very similar to DCF which already has many analytical models
to characterize its data transmission. EDCA will be the focus of this paper.

10. IEEE 802.11e Draft - EDCA Enhanced Distributed Channel Access
Multiple Access Priorities
Multiple Queues
Per-queue channel access
Internal collision resolution
AIFS[i] = a SIFSTime + AIFSN[i] × aSlotTime

11. IEEE 802.11e Draft – EDCA Default EDCA Parameter Set AC CWmin CWmax
AIFSN
AC_BK
aCWmin
aCWmax 7
AC_BE 3
AC_VI
(aCWmin-1)/2 2
AC_VO
(aCWmin-3)/4

12. Proposed Solution - PMAC
Provide (proportional) service differentiation and achieve pre-specified targeted throughput ratios, while still maximizing the total system capacity.
Through tuning IEEE e EDCA parameters.
Less is more.
When trying to achieve QoS guarantees, it may not be desirable to tune multiple parameters.

13. An Analytical Model for Multi-Class Service Differentiation
PMAC
An Analytical Model for Multi-Class Service Differentiation
p-persistent model:
After idle for DIFS time, each station transmits at the start of each idle slot with fixed probability p.
(backoff interval is sampled from a geometric distribution)
1-p p
DIFS

14. PMAC virtual transmission time (Tv)
successful transmission
virtual transmission time (Tv)
collision
DIFS
Channel throughput ρ can be expressed as
where m is the data packet payload size

15. PMAC
Assumptions:
There are P classes of stations, each of which contains Ni stations.
A class-i station transmits its frame in a slot with probability pi in the p-persistent version of IEEE
All the stations always have packets ready for transmission (i.e., the asymptotic condition holds).
The size, mi, of a packet sent by a class-i station is uniformly distributed between (x0, x1).

16. PMAC - virtual transmission length
Average virtual transmission time length E(TV) can be expressed as
virtual transmission time (Tv)
collision
successful
Ncol 1
Ncol 2 …
packet collision times in a virtual transmission time
the duration of a collision period
Where:
the length of an idle period
the length of the successful transmission
To find E(Tv),only need to calculate E(Ncol), E(Tc) and E(I)

17. PMAC - E(Ncol) Derivation of E(Ncol)
Pcollision is the probability that more than one will transmit given that at least one is transmitting
E(Ncol) is the number of transmission attempts times the probability of colliding raised to the number of collisions, time the probability for one success.

18. PMAC - E(Tc) Derivation of E(Tc) where
All combin. Time for a given combin Probability of a combin.
where
(Sum for every class and every number of nodes in each class){ Time it takes to collide given a specific combination of colliding stations * the probability that that specific combination of colliding stations would occur.

19. PMAC - E(I) Derivation of E(I)
Time for one slot * Sum of all i, i * ( ( Probability that at least one station is transmits )( Probability the number of transmitting stations is equal to zero )^(i))

20. PMAC – Ppkt(i)
Define
[( Number of stations of class i ( (Probability one from class i (pi) transmits * Probability that all others from class i do not transmit (1-pi)^(ni-1) ) ) Probability that all other classes do not transmit]
Sum of the probabilities that one from each class will transmit while all other stations do not transmit.

21. PMAC – Throughput ratios
The channel throughput attained by class-i stations can be expressed as
and the throughput ratio between class-i and class-j traffic can be derived as
and the throughput ratio between a class-i station and a class-j station can be expressed as

22. PMAC – Relative ratio to class 1
Suppose all the flow throughput ratios are given in terms of the relative ratio to a class 1 flow (i.e. ) and the data frame size of all traffic classes are of the same distribution ( E(Mi) = E(Mj) ).
Constraint!
To achieve the desired throughput ratio, pi has to be set to the value determined by the following equation
Protocol capacity can be optimized by finding optimal value of p1 to minimize E(Tv) subject to the constraint of the above relation between pi and p1.

23. Simulation Results (ns-2 simulation)
throughput
They choose different values of p1 and used the analytical model to get p2 using a ratio of 2.0 and then the throughput is measured.
each of which has a CBR trafc source that generates packets at a rate high enough to emulate the asymptotic condition
All stations send CBR packets of sizes 500 bytes to the base station. We do not consider TCP trafc, because we are primarily interested in the performance of IEEE EDCA
optimal value

24. Simulation Results
per flow throughput ratio

25. Implementation Issues for PMAC
Finding an Approximate Solution
Mapping p to Contention Window Size
Dealing with Network Dynamics

26. An Approximate Solution
Based on the observation that under normal, non-contention conditions, even if only class I nodes are active, the probability that at least one station starts to transmit is far less than one.
By assuming
we can make the following approximate

27. An Approx. Solution - simulation
Packet size = 500 bytes

28. Mapping p to cw
From “Performance evaluation and enhancement of the CSMA/CA MAC protocol for wireless LANs, the probability that a station transmits in a randomly-chosen slot is:
By setting CWmin and CWmax to CW* where CW* is chosen such that the probability that a station transmits in a slot is equal to the optimal transmission probability derived in our analytical model.
We can extend the analytical model results for the multiple class p-persistent MAC protocol to the contention window based MAC protocol

29. Dealing with Network Dynamics
To estimate the number of active stations in each class, we online count the number of active stations of class i from the channel access history overheard in the past Hi successful transmissions.
we set the value of Hi to the largest integer k such that the probability that the specific station finishes at least on successful transmission in the Hi virtual transmission times is larger than α , that is

30. Dealing with Network Dynamics

31. Dealing with Network Dynamics

32. Dealing with Network Dynamics
two classes (10 nodes in each class) and greedy traffic
the number of active nodes

33. Dealing with Network Dynamics
two classes (10 nodes in each class) and greedy traffic
p is adaptively determined p1 p2
the transmission probabilities

34. Dealing with Network Dynamics
two classes (10 nodes in each class) and greedy traffic
aggregated
class 1
class 2
the throughputs

35. Dealing with Network Dynamics
two classes and on-off traffic
the transmission probabilities

36. Dealing with Network Dynamics
two classes and on-off traffic
under-utilized
The failure to keeping the desired throughput differentiation during any time interval in the case of on-off traffic is, in part, due to the fact that the analytical
result is derived under the uid model assumption, which does not hold in the case of bursty traffic.
fully utilized
the per flow throughputs of two classes

37. Analysis of AIFS
Ratio of average per flow throughput is a function of:
Transmission probability (CW)
AIFS values
Study through Simulation
Set on class to a fixed AIFSN of 2, and vary the other from 2 – 8.
Set the ratio of class 2 to class 1 as 2.0
Use analytical model to calculate p1 and p2

38. Analysis of AIFS
Analysis of AIFS
PMAC m=5, n=15,

39. Analysis of AIFS Conclusion: Too many dimensions of design freedom
AIFS1=AIFS2= =AIFSP=DIFS may be a better choice

40. Conclusion Derived an analytical model
Through different transmission probabilities, service differentiation is achieved.
Low design dimensions are better.
Simulations show that targeted ratio’s are acquired and throughput is high.