Resolving Intra-Class Unfairness in 802.11 EDCA

Wireless Pers CommunDOI 10.1007/s11277-010-0141-2

Resolving Intra-Class Unfairness in 802.11 EDCA

Jiwoong Jeong · Jaehyuk Choi ·Sunghyun Choi · Chong-kwon Kim

© Springer Science+Business Media, LLC. 2010

Abstract The de facto quality-of-service provisioning mechanism for wireless LANs isthe Enhanced Distributed Channel Access (EDCA), which provides enhanced service differ-entiation based on four different prioritized Access Categories (ACs). In this paper, we showthat the multiple category mechanism of EDCA causes a new type of unfairness problemamong the ACs of same priority class when the numbers of active ACs in competing nodesare different. We present a simple analytical model to capture the impact of virtual collision,external collision, and Arbitration Interframe Space on the degree of this intra-class unfair-ness. Based on its analytical insight, we present a simple remedy scheme for resolving theunfairness and evaluate its efficiency using ns-2 simulations.

Keywords WLAN QoS · 802.11 EDCA · Intra-class unfairness · Virtual collision

1 Introduction

The de facto quality-of-service (QoS) provisioning mechanism for wireless LANs (WLANs)is the Enhanced Distributed Channel Access (EDCA), which provides enhanced servicedifferentiation based on multiple access category mechanism consisting of four Access

J. Jeong (B) · S. Choi · C. KimSchool of Electrical Engineering and Computer Science,Seoul National University, Gwanak-ro 599, Gwanak-gu, Seoul, South Koreae-mail: [email protected]

S. Choie-mail: [email protected]

C. Kime-mail: [email protected]

J. ChoiDepartment of Electrical Engineering and Computer Science,University of Michigan, Ann Arbor, MI 48109-2122, USAe-mail: [email protected]

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Categories (ACs). Each AC contends for the medium access separately via an EnhancedDistributed Channel Access Function (EDCAF) with a different priority. The principle ofQoS differentiation among ACs is to give more transmission opportunity to higher priorityclasses by assigning the different channel access parameters. Accordingly, a high priorityclass benefits from a greater throughput than another one even if some low priority trafficscoexist for channel contention.

While EDCA does achieve QoS differentiation through several enhancement features overthe legacy Distributed Coordination Function (DCF) such as the multiple access categorymechanism, it still suffers from certain performance problems [5, 11, 13]. In particular, it hasbeen shown that EDCA does not utilize their maximum potential capacity, and its throughputperformance and degree of fairness degrade significantly as the number of contending nodesincreases [13].

In this paper, we first point out a new unfair problem among ACs of the same prioritylevel, namely inter-class unfairness. Our key finding is that EDCA has inherent limitations inguaranteeing the fair channel access among the Channel Access Functions (CAFs) belong-ing to the same priority AC class when the numbers of activated CAFs on each contendingnode are different. In particular, when a node with a single active class contends with nodeshaving multiple active classes, the node suffers from low per-class throughput—even if itspriority level is identical to others—due to the asymmetry in external channel contentioncondition.

Our simulation results show that this phenomenon can be a critical obstacle to provide QoSservice to some delay sensitive applications. Unlike the prior unfairness issues [5, 11, 13]caused by a generic coordination problem of CSMA-based random access protocols, thisintra-class unfairness problem is a peculiar problem of EDCA induced from the multipleaccess category mechanism. Therefore, there is a compelling need to understand and resolvethe inter-class unfairness problem for more flexible and efficient QoS support in WLANs.To this end, we first present an analytic model that can explain the source of intra-classunfairness. The key feature of our proposed model is that the Markov chain of ArbitrationInterframe Space (AIFS) behavior can be coupled with various channel access rules1 and itcan model the system where the numbers of activated ACs on contending nodes are heteroge-neous. Based on the insight obtained from our analytical findings, we also propose a simpleremedy mechanism which achieves the fairness among CAFs. We validate our analysis modeland remedy scheme with ns-2 simulations.

The rest of the paper is organized as follows. Section 2 discusses the origin of intra-classunfairness. In Sect. 3, we present an analytic model that can capture the unfairness problem,and validate the proposed model. Section 4 presents a simple solution for resolving unfairnessand performance studies. Section 5 provides the related work. Finally, conclusions are drawnin Sect. 6.

2 Intra-Class Unfairness

In this section, we study the fairness problem of EDCA, particularly among the CAFs ofthe same priority level, in WLANs. First, we illustrate the problem via ns-2 simulationswith two contending nodes having different numbers of active CAFs under three scenariosas shown Fig. 1. In the simulations, node A generates multiple traffic classes while node Bgenerates only one traffic class for a single traffic stream, i.e., node A activates more number

1 See e.g., Bianchi’s [1] and Xiao’s [12] models.

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Fig. 1 Three scenarios for the simulation study with heterogeneous settings of ACs where node A generatestraffic of multiple classes while only one class is active in node B. In the simulation, we use the MAC and PHYparameters of IEEE 802.11b and assume ideal channel conditions, i.e., frame losses are only due to collisions

Fig. 2 Per-class throughput in each scenario

of CAFs than node B. The nodes are configured with same parameters for AC i (i = 0, 1, 2,and 3), where AC i (CWmin, CWmax, and AIFSn) are set to AC 3 (7, 255, 2), AC 2(15, 512, 2), AC 0 (31, 1023, 2). The two nodes are set to be always backlogged withpackets to send.

Figure 2 shows the per-class throughputs of nodes A and B under above three scenarios.In the first scenario, two nodes obtain the same throughput for their AC2s, i.e., fair sharebetween their same priority CAFs. On the other hand, under other scenarios, we can observethe discrepancy in throughput between the same priority CAFs for their AC3s. The CAFof AC3 in node A obtains higher throughput than that of node B even though the ACs areconfigured with the same parameters. The throughput difference becomes larger as the moreCAFs on node A are activated from 2 (scenario 2) to 3 (scenario 3).

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Table 1 Default EDCAparameters

CWmin CWmax AIFSn

AC3 (Voice) 7 15 2

AC2 (Video) 15 31 2

AC0 (Best effort) 31 1023 3

AC1 (Background) 31 1023 7

Fig. 3 Impact of intra-class unfairness on voice flow’s throughput

Our key observation is that this intra-class unfair share is caused by the asymmetry inthe external collision probabilities of the CAFs for the same priority ACs in the two nodes.In particular, the external collision probability of node B’s CAF is much higher than thatof node A’s CAF for their AC3s, because node B’s CAF is contending the channel accesswith all the CAFs used in node A (i.e., AC2 as well as AC3) while the CAF of node A’sAC3 contends with only one CAF of node B (i.e., AC3). Even though the CAF of node A’sAC3 virtually collides with the other ACs inside the node, it becomes always the winner andthus its channel attempt rate remains high. Therefore, the CAF of node A’s AC3 accesses thechannel more frequently than that of node B. Note that this new type of unfairness problemis an inherent feature of EDCA induced by multiple access categories and virtual collisionmechanisms, which has not been observed in single access category-based MAC protocolssuch as legacy DCF.

Next, we investigate the impact of intra-class unfairness on QoS provisioning through ns-2simulation study. In the simulation, we have varied the number of contending nodes whereone node generates only voice traffic with a highest priority class AC3 while the other nodesgenerate traffic of three classes with AC3, AC2, and AC0. For the configuration of ACs,we employ the default EDCA parameters in Table 1. Each voice flow on AC3 is generatedby a constant inter-arrival time 20 ms with a fixed payload size of 208 bytes correspondingto G.711-coded VoIP over RTP/UDP/IP/SNAP. Each video flow on AC2 is generated by aconstant inter-arrival time 2.5 ms with a mean payload size of 1464 bytes. Data frames onAC0 are generated according to an exponential distribution with a mean inter-arrival time12 ms and its payload size is 1,500 bytes [13].

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The simulation result in Fig. 3 shows the throughput of voice flows, i.e., AC3, of thesingle-traffic and multi-traffic nodes. We can observe the voice flow of the node with a singleclass suffers from the unfair throughput when it contends with larger numbers of the nodeswith multiple traffic classes.

3 Analytic Model

We now present an analytical model that can capture the unfairness problem among the CAFsof the same priority level in EDCA. In the conventional analytic models for EDCA [4, 6–9, 12], all CAFs belonging to the same AC are modeled as a single shared homogenous priorityclass. However, as observed in Sect. 2, although the CAFs belong to the same AC, their behav-iors may be different depending on both the intra-node and the inter-node CAF conditions.To address this issue, our proposed model provides flexibility that can decouple the rela-tion between CAF and AC, and thus the CAFs belonging to the same AC can be mappedinto different classes according to their intra-node (virtual contention among ACs) or inter-node (external contention for the medium access) conditions. In other words, while mostprevious works have modeled the priority levels in the terms of AC rather than CAF,we model the CAFs based on their actual priority levels among the CAFs by consid-ering the intra and inter-node contention condition. For example, in conventional mod-els, the AC3’s CAFs of nodes A and B in Fig. 1b and c are classified into the sameclass since they are connected to the same AC (i.e., AC3), meanwhile in our model theCAFs are represented with different classes by considering the asymmetry in channelcontention.

In our analysis, we focus on the scenarios in which each node uses multiple ACs in a dis-tributed manner. Although the EDCA defines four priority classes (ACs), the actual prioritiesof CAFs belonging to the same priority level can be different depending on the inter-nodeCAF condition as shown in the previous section. Thus, we categorize CAFs into N differentpriority classes according to their intra-CAF condition as well as the priority level of theircorresponding ACs: c = 1, 2, . . ., N , where N is given depending on the current node states.We assume saturated traffic condition, i.e., each node has always backlogged frames. Notethat typically QoS traffics do not operate in such a saturated environment, but the satura-tion throughput allows us to predict their bottom-line performance. We simplify a Markovchain by isolating the impact of AIFS from the states of backoff stage and residual backoffcounter while maintaining its prediction accuracy. This is achieved by the modular couplingtechnique which involves the following three steps: (i) each CAF is classified into differentclasses by the relative priority excluding its AIFS influence, (ii) each class is categorizedinto different sets by different AIFS lengths, and (iii) two sub-solutions are joined to form aglobally consistent solution. With this approach, we provide more comprehensive and simpleanalysis.

3.1 Notation

Let us define the following major notations for the analysis and its derivation.

N : total number of (relative) priority classesc: index of priority class for each CAF, c = 1, 2, …, NT : the number of different AIFS lengths, e.g. T = 3 for default EDCA parameter setsbecause AIFSn for AC3 and AC2 is identical

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AIFS[AC3]

AIFS[AC2]

AIFS[AC0]

AIFS[AC1]

d1 d2 d3 d4Slot time

Backoff Slots Next FrameBusy Medium

Fig. 4 AIFS relationship and various AIFS expiration instants

0, 0

i-1, 0

i, 0

0, 1

i, 1

0, 2

i, 2 i, Wc,i- 2

0, Wc,0- 2

i, Wc,i- 1

0, Wc,0- 1

rc, 0 rc, 1 rc, 2 rc,Wc,r_c- 2 rc,Wc,r_c- 1

ac· Dc,i(j)

ac· Dc,1(j)

ac· Dc,i(j)

ac· Dc,i+1(j)

ac· Dc,r_c(j)1· Dc,0(j)

(1-ac)· Dc,0(j)

(1-ac)· Dc,0(j)

(1-ac)· Dc,0(j)

ac· Dc,r_c(j)

1 1 1

1 1 1

1 1 1

Fig. 5 Markov chain model with arbitrary backoff distribution

fc: the number of CAFs belonging to class cτc: the channel access probability or the transmission probability for class cpe

c : the external collision probability for class cpv

c : the virtual collision probability for class cMc: a set of priority classes being simultaneously activated with class c in the same nodeHc: a set of classes having higher priority than class cAi : a set of classes whose length of arbitration inter-frame space (AIFS) is AIFS[i] whereAIFS[i] = min1≤i≤T {AIFS[i]} + di for i = 1, 2, . . . , T , and A1 ∩ A2 ∩ · · · ∩ AT = φ,where di represents the elapsed number of idle slots after the smallest AIFS expires asshown in Fig. 4 (thus d1 = 0).

3.2 Channel Access and Collision Probabilities

To obtain the channel access probability for class c, we model the system using a two-stateMarkov chain in Fig. 5. The state {i , j} represents the state of each node in i-th transmissionattempt (or backoff stage i) and the backoff counter j . Dc,i ( j) depicted in the dotted circlein Fig. 5 represents the probability mass function (p.m.f) for the backoff distribution of the

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state transition after a transmission attempt. Specifically, when a frame transmission resultsin collision (or success) at the state {i , 0}, it triggers a transition from stage i to i +1 (or to 0)and the next state is determined according to the backoff distribution Dc,i ( j) (or Dc,0( j)),respectively.2 The non-null one step-transition probabilities are as follows.

⎧⎪⎪⎨

⎪⎪⎩

P{i, j |i, j + 1} = 1, j ∈ [0, Wc,i − 2] i ∈ [0, rc],P{0, j |i, 0} = (1 − ac) · Dc,0( j), j ∈ [0, Wc,0 − 1] i ∈ [0, rc − 1],P{i, j |i − 1, 0} = ac · Dc,i ( j), j ∈ [0, Wc,i − 1] i ∈ [1, rc],P{0, j |rc, 0} = 1 · Dc,0( j), j ∈ [0, Wc,0 − 1] .

(1)

Here, rc denotes the retry limit (i.e., short retry limit or long retry limit) for class c, ac

denotes the collision probability for class c, and Wc,i is the contention window for class c atthe backoff stage i . ac equals to pv

c + (1 – pvc )pe

c . We define{

Wc,i = 2i Wc,0, i ≤ mc,

Wc,i = 2mc Wc,0, i > mc,(2)

where mc means the exponent until the contention windows for class c reaches its CWmax .Let bc,i, j be the stationary probability of state {i , j} for class c. Then, we can derive thefollowing relationship:

bc,i−1,0 · ac = bc,i,0 → bc,i,0 = aic · bc,0,0, (0 ≤ i ≤ rc), (3)

where bc,0,0 denotes the stationary probability that the CAF in class c stays in the state {0,0}.From the chain regularities, we can obtain,

bc,i, j =Wi −1∑

n= j

Dc,i (n) · bc,i,0, (0 ≤ i ≤ rc and 0 ≤ j ≤ Wc,i − 1). (4)

We note that sum of the probabilities of all the states is equal to 1. Therefore, the followingrelationship can be derived.

1 =rc∑

i=0

Wc,i −1∑

j=0

bc,i, j =rc∑

i=0

bc,i,0

Wc,i −1∑

j=0

Wc,i −1∑

n= j

Dc,i (n)

=rc∑

i=0

bc,i,0

⎛

⎝

Wc,i −1∑

j=0

j · Dc,i ( j) + 1

⎞

⎠ . (5)

Because the term∑Wc,i −1

j=0 j · Dc,i ( j) in (5) equals to the mean of Dc,i ( j). The relationshipbetween the backoff distribution and kc is given by

Wc,i −1∑

j=0

j · Dc,i ( j) = kc · (Wc,i − 1) (0 ≤ kc ≤ 1) (6)

where kc denotes the normalized mean3 of backoff distribution Dc,i ( j) for class c. kc isthe CW scaling factor. In the case of the existing backoff procedure, the backoff counter isobtained from a uniform distribution whose normalized mean is 1/2.

2 For example, Dc,i ( j) is given as 1/Wc,i for i ∈ [0, rc] if the backoff counter is uniformly selected.3 i.e., E[Bc,i ] = kc(Wc,i – 1), where the random variable Bc,i represents the backoff counter selected atstage i .

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0(=d1)

1P1

idle P1idle

1-P4idle1-P3

idle1-P2

idle1-P2idle

1-P1idle

l(=d2)

l+1P2

idle P2idle

m(=d3)

m+1P3

idle P3idle

n(=d4)

n+1P4

idleP4

idle

1-P3idle 1-P4

idle

AIFS for the classes in A1

expires at d1


expires at d2


expires at d3


expires at d4

π1 π2 π3 π4

l-1P1

idle

1-P1idle

Fig. 6 State diagram for AIFS analysis

Therefore, from (2), (3), and substituting (6) for (5), we can derive

bc,0,0 = 1/ rc∑

i=0

aic · (

kc · (Wc,i − 1) + 1)

(7)

Now, from the definition of τc and using (3), τc in a given virtual slot is given by

τc =rc∑

i=0

bc,i,0 = 1 − arc+1c

1 − ac· bc,0,0, (8)

where bc,0,0 is given in (7). At the stationary state, each CAF carrying class c transmits apacket with probability τc, which also depends on the collision probability ac. Now, becauseexternal collision occurs when there are two or more simultaneous transmissions on themedium, pe

c for class c is as follows

pec = 1 −

∏i∈N (1 − τi )

fi

∏j∈c∪Mc

(1 − τ j ). (9)

If a class c has the highest priority in a node or a single CAF is activated, pvc = 0.

Otherwise,pvc = 1 − ∏

i∈Hc∩Mc(1 − τi ).

3.3 AIFS Analysis

To study the effect of AIFS differentiation, we now present a simple, yet accurate modelfor capturing AIFS differentiation. As depicted in Fig. 4, the expirations of different AIFSsoccur subsequently at d1, d2, d3, and d4 according to priority.

We describe such behavior of AIFS differentiation using a Markov Chain model as shownin Fig. 6. Each state represents the number of elapsed idle slots after DATA + SIFS + ACK+ the shortest AIFS. The state comes back to state 0 if the channel becomes busy again. Ifthe current state stays between dx and dx+1–1, only the classes in Ax and the priority sets(i.e., A1, A2,…, Ax−1) higher than Ax can access the channel. For example, let’s assumethat there are three sets where A1, A2, and A3 consisting of classes expiring at d1, d2, andd3, respectively. If the current state stays between l (i.e., d2) and m–1 (i.e., d3–1), only theclasses in A1 and A2 are activated and can access the channel. Meanwhile, the classes in A3

remain frozen.In Fig. 6, Pidle

x is the probability that the classes in Ax and the higher priority sets thanAx remain idle. Pidle

x is obtained as follows:

Pidlex =

∏

i∈x⋃

k=1Ak

(1 − τi )fi (10)

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The stationary probability q0 remaining in state 0 is given by

q0 = 1/ T∑

i=1

⎛

⎝1 − (

Pidlei

)di+1−di

1 − Pidlei

·⎧⎨

⎩

1 if i = 1i∏

j=2

(Pidle

j−1

)d j −d j−1if i ≥ 2

⎞

⎠, (11)

where dT = ∞.Let πx be the stationary probability staying in states from dx to dx+1–1, which is given by:

πx =

⎧⎪⎪⎪⎨

⎪⎪⎪⎩

dx+1−1∑

k=dx

(Pidlex )k−dx · q0 if x = 1,

dx+1−1∑

k=dx

(Pidlex )k−dx · ∏x−1

i=1 (Pidlei )di+1−di · q0 if x ≥ 2.

(12)

Therefore, we can derive the following equation from Eq. (8).

τc =T∑

k=x

πk · (1 − ar+1c )

(1 − ac)· bc,0,0 if c ∈ Ax for x = 1, 2, . . . , T (13)

Then, Eq. (9) is rewritten as follows.

pec =

T∑

k=x

(πk

∑Tm=x πm

)

×(

1 −∏

i∈⋃kl=x Al

(1 − τi )fi

∏j∈{c∪Mc}∩⋃k

n=x An(1 − τ j )

)

if c ∈ Ax for x = 1, 2, . . . , T (14)

3.4 Normalized Throughput and Successful Probability

The probability Pb that the channel is busy, the probability Psc that a successful transmission

for class c occurs, the probability Ps that a successful transmission for any class c occurs,and the normalized system throughput Sc of class c (i.e., per-class throughput) are obtainedas follows.

Pb = 1 −N∏

i=1

(1 − τi )fi ,

Psc = fcτc ·

∏Ni=1 (1 − τi )

fi

∏j /∈Hc, j∈Mc

(1 − τ j ),

Ps =N∑

i=1

Psi ,

Sc = Psc E[P]

(1 − Pb)σ + Ps T suc + (Pb − Ps)T col,

(15)

where E[P], T suc, and T col represent the average packet payload size, the average success-ful transmission time, and the average collision time, respectively. In this paper, the moredetailed derivation for obtaining the throughput is omitted. Note that the remaining derivationis easily obtained by referring the previous works [1, 2, 4, 7–9, 12].

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Fig. 7 Model validation. a Per-class throughput with the configuration of AC3(7, 255, 2), AC2(15, 512, 2),AC0(31, 1023, 3), and AC1(31, 1023, 4). b Per-class throughput with the configuration of the default EDCAparameters

3.5 Model Validation

In this section, we validate the proposed model using ns-2 simulations. Figure 7 shows per-class throughput as the total number of CAFs increases. There are two types of nodes; (i)nodes in the first group generate traffic of multiple classes with AC3, AC2, AC0, and AC1(type A) and (ii) nodes in the other group generate traffic of a single class with only AC3(type B). Total number of nodes increases while keeping the ratio between type A nodes andtype B nodes to 1:1. We consider two sets of configurations for Fig. 7a and b. First, the valuesof parameters (i.e., CWmin, CWmax, and AIFSn) for each AC in Fig. 7a are set to AC3(7,255, 2), AC2(15, 512, 2), AC0(31, 1023, 3), and AC1(31, 1023, 4), respectively. Second,each AC in Fig. 7b employs the default EDCA parameters listed in Table 1. The payload sizeof all the flows is set to 1,500 bytes.

From the results in Fig. 7a and b, we can observe that the CAFs of AC3 for the nodes oftype A (which have more active ACs) achieves higher throughput performance than those

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of type B (which have single active ACs) even though their priory level is identical. This isbecause the AC3 of type B suffers from more frequent external collisions as explained ear-lier. The degree of unfairness becomes weaker as the number of contending nodes increasesbecause the external collision probabilities of all the nodes become equalized when the chan-nel contention is severe. From the results, we can see that the analytic model is very accurateto predict the performance of each traffic class.

4 A Simple Solution for the Unfairness Elimination

In this section, we propose a simple solution to mitigate the intra-class unfairness problemby exploiting the proposed analytic model. We consider an 802.11 WLAN consisting of aQoS-AP (QAP) and N clients. Let’s consider two CAFs of the same priority used in twodifferent nodes where the first CAF is used in a node with other multiple CAFs, denoted as aclass i , and the other CAF is solely activated in the other node, denoted as a class j . The CWscaling factors of classes i and j are ki and k j , respectively. Their default values are givento 1/2 because the backoff procedure is based on the uniform distribution (see Eq. 6).

Our basic idea to resolve the unfairness problem among the same level CAFs is to regulatethe CW scaling factor of the CAFs dynamically (instead of using the uniform backoff rule)so that all CAFs of same priority have the equal channel access probability. Initially, the QAPcan gather the information of activated CAFs on the nodes by observing incoming frames.Based on the gathered information, the QAP regulates the backoff distribution of advantagedclass i (i.e., restraining the attempt rate by adjusting ki ). To find an appropriate ki for whichthe advantaged class i achieves the fair channel share with class j , we need to satisfy at leastthe following condition using Eqs. (13)–(15).

Psi

fi= Ps

j

f j(16)

Since all other parameters involved in Eq. (16) except ki (k j is given as 1/2) are givenaccording to Eqs. (3)–(15), we can find out the value of ki satisfying Eq. (16). Then, the QAPbroadcasts the EDCA parameter sets including the CW scaling factor via a beacon frameperiodically. After receiving the information, all the nodes apply the CW scaling factor tocontrol the backoff mechanism of their CAFs according to the number of activated CAFs.For instance, we regulate the contention window size for the random backoff selection ofthe advantage class i proportional to ki so as to adjust its relatively higher channel accessprobability over that of class j as follows.

BC = round(2 · ki · uni f orm(0, CW)), (17)

where uniform(0, CW) is a function returning a integer selected from a uniform distributionover the range of [0,CW] and CW denotes the current contention window size. Note that Eq.(17) is an instance of how to regulate the channel access probability based on ki and variousmethods may be applied for real implementation.

We have evaluated the effectiveness of the proposed method via ns-2 simulations. We com-pare the throughput performance of remedy using the value of CW scaling factor obtainedfrom Eq. (16) (i.e., ki adjustment) with that of normal EDCA using uniform distribution (i.e.,ki =1/2). For simulation scenarios, we consider WLAN environments consisting of 4 nodeswith the values of same parameters employed in Fig. 7a. There are two kinds of nodes whereone is with four CAFs (i.e., type A) and the other with only one CAF (i.e., type B). For

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Fig. 8 Addressing unfairness via adjusting the normalized mean kc of backoff distribution

(a) (b)

Fig. 9 Delay performance as a function of elapsed time. a EDCA b proposed remedy

simulation runs, we have employed 3 scenarios with different ratio of type A nodes to typeB nodes, in particular 1:3, 2:2, and 3:1.

Figure 8 shows the ratio of throughput of class 2 (i.e., AC3 of type B node) to that ofclass 1 (i.e., AC3 of type A node) and the aggregate throughput of both the proposed remedyand the legacy EDCA. From the results, we can see that the throughput ratio moves closerto 1 with our solution while it has remained below 0.82 with the EDCA, which means thesignificant improvement of fairness without the throughput reduction.

Figure 9 illustrates the delay performance of AC3 with the original EDCA and the pro-posed remedy as a function of elapsed time. The simulation is conducted for 300 s, and theratio of type A nodes to type B nodes varies over time. The simulation run starts at time 0when the ratio is 1:3. At time 100, the ratio changes from 1:3 to 2:2. At time 200, the ratiobecomes 3:1. Delay is defined as the time interval from the time when a frame arrives at theMAC layer to the time when it is received by the receiver.

In Fig. 9a, we observe that the EDCA consistently reveals the delay gap between AC3of type A node (class 1) and AC3 of type B node (class 2). This phenomenon is incurredby the intra-class unfairness. On the other hand, Fig. 9b shows that with the proposed remedy,the delay gap disappears. This is because the CAF corresponding to class 1 adjusts its ki to

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the value announced by the QAP. Due to this reaction, AC3 of type A node and AC3 of typeB node obtain an equal channel access opportunity.

5 Related Work

There have been many performance studies for 802.11 MAC in the literature. Remarkably,[1] and [3] have motivated a significant amount of subsequent MAC development work. In[1], Bianchi has proposed a simple and accurate Markov chain model for the DCF under thesaturation condition after the BEB behavior of a node is observed. The optimal CW valuesare presented using the analytic model when the number of nodes is given. Cali et al. in [3]propose a MAC protocol based on p-persistent CSMA after observing the system behavior.They resolve the transmission probability p to maximize the system throughput. In [6], theauthors propose the analytic model including AIFS, delay, and jitter. To model the impact ofAIFS, the authors exploit an inaccurate method multiplying the channel access probabilityby the probability that the transmission of higher priority ACs does not occur at subsequentidle slots before its AIFS expires. Engelstad et al. [4] enhance Xiao’s model to derive AIFSdifferentiation. However, their model is less accurate because busy probability for each ACis linearly proportional to AIFS difference, i.e., the number of additional slots. [8, 7] haveproposed more accurate model with only two AIFS values. Besides the accuracy problem,previous work is not able to capture the problem that although CAFs correspond to the sameAC, they are likely to belong to different classes.

6 Conclusion

In this paper, we introduce a new type of unfairness problem, which has not been addressedyet, induced by the multiple access category mechanism in EDCA and show that it is causedby the asymmetry in virtual or external collision probabilities among the contending nodes.Our proposed model is shown to explain accurately the unfairness problem. Based on theinsight obtained from the analytical results, we resolve the problem by adjusting the CWscaling factor instead of using the legacy uniform distribution. Optimizing the aggregatethroughput of proposed remedy will be a part of our future research.

Acknowledgments This research was supported by the MKE (The Ministry of Knowledge Economy),Korea, under the ITRC (Information Technology Research Center) support program supervised by the NIPA(National IT Industry Promotion Agency) (NIPA-2010-(C1090-1011-0004)).

References

1. Bianchi, G. (2000). Performance analysis of the IEEE 802.11 distributed coordination function. IEEEJournal on Selected Areas in Communications, 18(3).

2. Bianchi, G., & Tinnirello, I. (2005). Remarks on IEEE 802.11 DCF performance evaluation. IEEECommunication Letters, 9(8), 765–767.

3. Cali, F., Conti, M., & Gregori, E. (2000). Dynamic tuning of the IEEE 802.11 protocol to achieve atheoretical throughput limit. IEEE/ACM Transactions on Networking, 8(6).

4. Engelstad, P., et al. (2005). Non-saturation and saturation analysis of IEEE 802.11e EDCA withstarvation prediction. In Proceedings of ACM MSWiM’05, October 2005.

5. Han, S., Nandagopal, T., Bejerano, Y., & Choi, H. (2009). Analysis of spatial unfairness in wirelessLANs. In Proceedings of IEEE INFOCOM ‘09.

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6. Huang, C., & Liao, W. (2007). Throughput and delay performance of IEEE 802.11e enhanced distributedchannel access (EDCA) under saturation condition. IEEE Transactions on Wireless Communications,6(1).

7. Jeong, J., Choi, S., & Kim, C. (2005). Achieving weighted fairness between uplink and downlink inIEEE 802.11 DCF-based WLANs. In Proceedings of IEEE QShine’05, August 2005.

8. Kim, J., & Kim, C. (2004). Performance analysis and evaluation of IEEE 802.11e EDCF. WirelessCommunications and Mobile Computing, 4(1).

9. Kong, Z., Tsang, D., Bensaou, B., & Gao, D. (2004). Performance analysis of IEEE 802.11econtention-based channel access. IEEE Journal on Selected Areas in Communications, 22(10).

10. Std. 802.11-2007. (2007). Part 11: Wireless LAN medium access control (MAC) and physical layer(PHY) specifications, ANSI/IEEE Std. 802.11-2007.

11. Tinnirello, I., & Choi, S. (2005). Temporal fairness provisioning in multi-rate contention-based 802.11eWLANs. In Proceedings of IEEE WoWMoM’05, June 13–16, 2005, Taormina, Italy.

12. Xiao, Y. (2004). An analysis for differentiated services in IEEE 802.11 and IEEE 802.11e wirelessLANs. In Proceedings of IEEE ICDCS’04.

13. Xiao, Y., Li, H., & Choi, S. (2009). Two-level protection and guarantee for multimedia traffic inIEEE 802.11e distributed WLANs. ACM Wireless Networks, 15(2).

Author Biographies

Jiwoong Jeong is currently pursuing his Ph.D. in the School of Com-puter Science and Electrical Engineering from Seoul National Univer-sity. He received the B.S. degree from Dongguk University in 2002,and the M.S. degree in the School of Computer Science and Electri-cal Engineering from Seoul National University, Seoul, Korea in 2004,respectively. His current research interests include MAC and network-ing layer protocol design for wireless networks.

Jaehyuk Choi received his Ph.D. degree in Electrical Engineering andComputer Science from Seoul National University, Seoul, Korea, in2008. He is currently a post-doctoral research fellow at the Departmentof Electrical Engineering and Computer Science, The University ofMichigan, Ann Arbor. He was a postdoctoral researcher in Brain Korea21 at Seoul National University, 2008. His current research interests arein the area of mobile computing and wireless networks including wire-less LAN, mesh/relay networks, cognitive radio, and sensor networks.He received a Silver Award at the Samsung HumanTech Paper Contestin 2007.

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Sunghyun Choi is currently an associate professor at the Schoolof Electrical Engineering, Seoul National University (SNU), Seoul,Korea. Before joining SNU in September 2002, he was with PhilipsResearch USA, Briarcliff Manor, New York, USA as a Senior Mem-ber Research Staff and a project leader for 3 years. He received hisB.S. (summa cum laude) and M.S. degrees in electrical engineeringfrom Korea Advanced Institute of Science and Technology (KAIST)in 1992 and 1994, respectively, and received Ph.D. at the Departmentof Electrical Engineering and Computer Science, The University ofMichigan, Ann Arbor in September, 1999. His current research inter-ests are in the area of wireless/mobile networks with emphasis onwireless LAN/MAN/PAN, next-generation mobile networks, mesh net-works, cognitive radios, resource management, data link layer pro-tocols, and cross-layer approaches. He authored/coauthored over 120technical papers and book chapters in the areas of wireless/mobile net-works and communications. He has coauthored (with B. G. Lee) a book

“Broadband Wireless Access and Local Networks: Mobile WiMAX and WiFi,” Artech House, 2008. Heholds over 30 patents, and has tens of patents pending. He has served as a General Co-Chair of COMS-WARE 2008, and a Technical Program Committee Co-Chair of ACM Multimedia 2007, IEEE WoWMoM2007 and IEEE/Create-Net COMSWARE 2007. He has also served on program and organization commit-tees of numerous leading wireless and networking conferences including ACM MobiCom, IEEE INFOCOM,IEEE SECON, IEEE MASS, and IEEE WoWMoM. He is also serving on the editorial boards of IEEETransactions on Mobile Computing, ACM SIGMOBILE Mobile Computing and Communications Review(MC2R), Computer Communications, and Journal of Communications and Networks (JCN). He has servedas a guest editor for IEEE Journal on Selected Areas in Communications (JSAC), IEEE Wireless Commu-nications, Pervasive and Mobile Computing (PMC), ACM Wireless Networks (WINET), Wireless PersonalCommunications (WPC), and Wireless Communications and Mobile Computing (WCMC). From 2000 to2007, he was a voting member of IEEE 802.11 WLAN Working Group. He has received a number of awardsincluding the Young Scientist Award awarded by the President of Korea (2008); IEEK/IEEE Joint Award forYoung IT Engineer (2007); the Outstanding Research Award (2008) and the Best Teaching Award (2006)both from the College of Engineering, Seoul National University; the Best Paper Award from IEEE WoW-MoM 2008; and Recognition of Service Award (2005, 2007) from ACM. He is a senior member of IEEE,and a member of ACM, KICS, IEEK, KIISE.

Chong-kwon Kim received the B.S. degree in Industrial Engineer-ing from Seoul National University, the M.S. degree in OperationsResearch from Georgia Institute of Technology, and the Ph.D. degreein Computer Science from University of Illinois at Urbana-Champaignin 1981, 1982, and 1987, respectively. In 1987, he joined Bellcore as aMember of Technical Staff and worked on Broadband ISDN and ATM.Since 1991, he has been with Seoul National University as a Profes-sor in the School of Computer Science and Engineering. His researchinterests include wireless and mobile networking, high speed networkcontrol, distributed processing, and performance evaluation.

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Resolving Intra-Class Unfairness in 802.11 EDCA

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