Analysis of the 802.11e Enhanced Distributed
Channel Access Function †
Inanc Inan, Feyza Keceli, and Ender Ayanoglu
Center for Pervasive Communications and Computing
Department of Electrical Engineering and Computer Science
The Henry Samueli School of Engineering
University of California, Irvine, 92697-2625
Email: {iinan, fkeceli, ayanoglu}@uci.edu
Abstract
The IEEE 802.11e standard revises the Medium Access Control (MAC) layer of the former IEEE 802.11
standard for Quality-of-Service (QoS) provision in the Wireless Local Area Networks (WLANs). The Enhanced
Distributed Channel Access (EDCA) function of 802.11e defines multiple Access Categories (AC) with AC-specific
Contention Window (CW) sizes, Arbitration Interframe Space (AIFS) values, and Transmit Opportunity (TXOP)
limits to support MAC-level QoS and prioritization. We propose an analytical model for the EDCA function which
incorporates an accurate CW, AIFS, and TXOP differentiation at any traffic load. The proposed model is also
shown to capture the effect of MAC layer buffer size on the performance. Analytical and simulation results are
compared to demonstrate the accuracy of the proposed approach for varying traffic loads, EDCA parameters, and
MAC layer buffer space.
I. INTRODUCTION
The IEEE 802.11 standard [1] defines the Distributed Coordination Function (DCF) which provides
best-effort service at the Medium Access Control (MAC) layer of the Wireless Local Area Networks
(WLANs). The recently ratified IEEE 802.11e standard [2] specifies the Hybrid Coordination Function
(HCF) which enables prioritized and parameterized Quality-of-Service (QoS) services at the MAC layer,
on top of DCF. The HCF combines a distributed contention-based channel access mechanism, referred
to as Enhanced Distributed Channel Access (EDCA), and a centralized polling-based channel access
mechanism, referred to as HCF Controlled Channel Access (HCCA).
† This work is supported by the Center for Pervasive Communications and Computing, and by National Science Foundation under Grant
No. 0434928. Any opinions, findings, and conclusions or recommendations expressed in this material are those of authors and do not
necessarily reflect the view of the National Science Foundation.
http://arXiv.org/abs/0704.1833v3
We confine our analysis to the EDCA scheme, which uses Carrier Sense Multiple Access with Collision
Avoidance (CSMA/CA) and slotted Binary Exponential Backoff (BEB) mechanism as the basic access
method. The EDCA defines multiple Access Categories (AC) with AC-specific Contention Window (CW)
sizes, Arbitration Interframe Space (AIFS) values, and Transmit Opportunity (TXOP) limits to support
MAC-level QoS and prioritization [2].
In order to assess the performance of these functions, simulations or mathematical analysis can be used.
Although simulation models may capture system dynamics very closely, they lack explicit mathematical
relations between the network parameters and performance measures. A number of networking functions
would benefit from the insights provided by such mathematical relations. For example, analytical modeling
is a more convenient way to assist embedded QoS-aware MAC scheduling and Call Admission Control
(CAC) algorithms. Theoretical analysis can provide invaluable insights for QoS provisioning in the WLAN.
On the other hand, analytical modeling can potentially be complex, where the effect of multiple layer
network parameters makes the task of deriving a simple and accurate analytical model highly difficult.
However, a set of appropriate assumptions may lead to simple yet accurate analytical models.
The majority of analytical work on the performance of 802.11e EDCA (and of 802.11 DCF) assumes
that every station has always backlogged data ready to transmit in its buffer anytime (in saturation) as
will be discussed in Section III. Analysis of the system in this state (saturation analysis) provides accurate
and practical asymptotic figures. However, the saturation assumption is unlikely to be valid in practice
given the fact that the demanded bandwidth for most of the Internet traffic is variable with significant
idle periods. Our main contribution is an accurate EDCA analytical model which releases the saturation
assumption. The model is shown to predict EDCA performance accurately for the whole traffic load range
from a lightly loaded non-saturated channel to a heavily congested saturated medium for a range of traffic
models.
Similarly, the majority of analytical work on the performance of 802.11e EDCA (and of 802.11
DCF) assumes constant collision probability for any transmitted packet at an arbitrary backoff slot
independent of the number of retransmissions it has experienced. A complementary assumption is the
constant transmission probability for any AC at an arbitrary backoff slot independent of the number of
retransmissions it has experienced. As will be discussed in Section III, these approximations lead to
accurate analysis in saturation. Our analysis shows that the slot homogeneity assumption leads to accurate
performance prediction even when the saturation assumption is released.
Furthermore, the majority of analytical work on the performance of 802.11e EDCA (and of 802.11
DCF) in non-saturated conditions assumes either a very small or an infinitely large MAC layer buffer
space. Our analysis removes such assumptions by incorporating the finite size MAC layer queue (interface
queue between Link Layer (LL) and MAC layer) into the model. The finite size queue analysis shows
the effect of MAC layer buffer space on EDCA performance which we will show to be significant.
A key contribution of this work is that the proposed analytical model incorporates all EDCA QoS
parameters, CW, AIFS, and TXOP. The model also considers varying collision probabilities at different
AIFS slots which is a direct result of varying number of contending stations. Comparing with simulations,
we show that our model can provide accurate results for an arbitrary selection of AC-specific EDCA
parameters at any load.
We present a Markov model the states of which represent the state of the backoff process and MAC
buffer occupancy. To enable analysis in the Markov framework, we assume constant probability of packet
arrival per state (for the sake of simplicity, Poisson arrivals). On the other hand, we have also shown that
the results also hold for a range of traffic types.
II. EDCA OVERVIEW
The IEEE 802.11e EDCA is a QoS extension of IEEE 802.11 DCF. The major enhancement to support
QoS is that EDCA differentiates packets using different priorities and maps them to specific ACs that are
buffered in separate queues at a station. Each ACi within a station (0 ≤ i ≤ imax, imax = 3 in [2]) having
its own EDCA parameters contends for the channel independently of the others. Following the convention
of [2], the larger the index i is, the higher the priority of the AC is. Levels of services are provided
through different assignments of the AC specific EDCA parameters; AIFS, CW, and TXOP limits.
If there is a packet ready for transmission in the MAC queue of an AC, the EDCA function must sense
the channel to be idle for a complete AIFS before it can start the transmission. The AIFS of an AC is
determined by using the MAC Information Base (MIB) parameters as
AIFS = SIFS + AIFSN × Tslot, (1)
where AIFSN is the AC-specific AIFS number, SIFS is the length of the Short Interframe Space and
Tslot is the duration of a time slot.
If the channel is idle when the first packet arrives at the AC queue, the packet can be directly transmitted
as soon as the channel is sensed to be idle for AIFS. Otherwise, a backoff procedure is completed following
the completion of AIFS before the transmission of this packet. A uniformly distributed random integer,
namely a backoff value, is selected from the range [0, W ]. Should the channel be sensed busy at any time
slot during AIFS or backoff, the backoff procedure is suspended at the current backoff value. The backoff
resumes as soon as the channel is sensed to be idle for AIFS again. When the backoff counter reaches
zero, the packet is transmitted in the following slot.
The value of W depends on the number of retransmissions the current packet experienced. The initial
value of W is set to the AC-specific CWmin. If the transmitter cannot receive an Acknowledgment (ACK)
packet from the receiver in a timeout interval, the transmission is labeled as unsuccessful and the packet
is scheduled for retransmission. At each unsuccessful transmission, the value of W is doubled until the
maximum AC-specific CWmax limit is reached. The value of W is reset to the AC-specific CWmin if the
transmission is successful, or the retry limit is reached thus the packet is dropped.
The higher priority ACs are assigned smaller AIFSN. Therefore, the higher priority ACs can either
transmit or decrement their backoff counters while lower priority ACs are still waiting in AIFS. This
results in higher priority ACs enjoying a lower average probability of collision and relatively faster
progress through backoff slots. Moreover, in EDCA, the ACs with higher priority may select backoff
values from a comparably smaller CW range. This approach prioritizes the access since a smaller CW
value means a smaller backoff delay before the transmission.
Upon gaining the access to the medium, each AC may carry out multiple frame exchange sequences as
long as the total access duration does not go over a TXOP limit. Within a TXOP, the transmissions are
separated by SIFS. Multiple frame transmissions in a TXOP can reduce the overhead due to contention.
A TXOP limit of zero corresponds to only one frame exchange per access.
An internal (virtual) collision within a station is handled by granting the access to the AC with the
highest priority. The ACs with lower priority that suffer from a virtual collision run the collision procedure
as if an outside collision has occured [2].
III. RELATED WORK
In this section, we provide a brief summary of the theoretical DCF and EDCA function performance
analysis in the literature.
The majority of previous work carries out performance analysis for asymptotical conditions assuming
each station is in saturation. Three major saturation performance models have been proposed for DCF;
i) assuming constant collision probability for each station, Bianchi [3] developed a simple Discrete-Time
Markov Chain (DTMC) and the saturation throughput is obtained by applying regenerative analysis to
a generic slot time, ii) Cali et al. [4],[5] employed renewal theory to analyze a p-persistent variant of
DCF with persistence factor p derived from the CW, and iii) Tay et al. [6] instead used an average value
mathematical method to model DCF backoff procedure and to calculate the average number of interruptions
that the backoff timer experiences. Having the common assumption of slot homogeneity (for an arbitrary
station, constant collision or transmission probability at an arbitrary slot), these models define all different
renewal cycles all of which lead to accurate saturation performance analysis. Similarly, Medepalli et al.
[7] provided explicit expressions for average DCF cycle time and system throughput. Pointing out another
direction for future performance studies, Hui et al. [8] recently proposed the application of metamodeling
techniques in order to find approximate closed-form mathematical models.
These major methods are modified by several researchers to include the extra features of the EDCA
function in the saturation analysis. Xiao [9],[10] extended [3] to analyze only the CW differentiation.
Kong et al. [11] took AIFS differentiation into account via a 3-dimensional DTMC. On the other hand,
these EDCA extensions miss the treatment of varying collision probabilities at different AIFS slots due
to varying number of contending stations. Robinson et al. [12],[13] proposed an average analysis on
the collision probability for different contention zones during AIFS and employed calculated average
collision probability on a 2-dimensional DTMC. Hui et al. [14],[15] unified several major approaches
into one approximate average model taking into account varying collision probability in different backoff
subperiods (corresponds to contention zones in [12]). Zhu et al. [16] proposed another analytical EDCA
Markov model averaging the transition probabilities based on the number and the parameters of high
priority flows. Inan et al. [17] proposed a simple DTMC which provides accurate treatment of AIFS
and CW differentiation between the ACs for the constant transmission probability assumption. Another
3-dimensional DTMC is proposed by Tao et al. [18],[19] in which the third dimension models the state
of backoff slots between successive transmission periods. In [18],[19], the fact that the number of idle
slots between successive transmissions can be at most the minimum of AC-specific CWmax values is
considered. Independent from [18],[19], Zhao et al. [20] had previously proposed a similar model for
the heterogeneous case where each station has traffic of only one AC. Banchs et al. [21],[22] proposed
another model which considers varying collision probability among different AIFS slots due to a variable
number of stations. Chen et al. [23], Kuo et al. [24], and Lin et al. [25] extended [6] in order to include
mean value analysis for AIFS and CW differentiation.
Although it has not yet received much attraction, the research that releases the saturation assumption
basically follows two major methods; i) modeling the non-saturated behavior of DCF or EDCA function
via Markov analysis, ii) employing queueing theory [26] and calculating certain quantities through average
or Markov analysis. Our approach in this work falls into the first category.
Markov analysis for the non-saturated case still assumes slot homogeneity and extends [3] with necessary
extra Markov states and transitions. Duffy et al. [27] and Alizadeh-Shabdiz et al. [28],[29] proposed similar
extensions of [3] for non-saturated analysis of 802.11 DCF. Due to specific structure of the proposed
DTMCs, these extensions assume a MAC layer buffer size of one packet. We show that this assumption
may lead to significant performance prediction errors for EDCA in the case of larger buffers. Cantieni et
al. [30] extended the model of [28] assuming infinitely large station buffers and the MAC queue being
empty with constant probability regardless of the backoff stage the previous transmission took place. Li et
al. [31] proposed an approximate model for non-saturation where only CW differentiation is considered.
Engelstad et al. [32] used a DTMC model to perform delay analysis for both DCF and EDCA considering
queue utilization probability as in [30]. Zaki et al. [33] proposed yet another Markov model with states
that are of fixed real-time duration which cannot capture the pre-saturation DCF throughput peak.
A number of models employing queueing theory have also been developed for 802.11(e) performance
analysis in non-saturated conditions. These models are assisted by independent analysis for the calculation
of some quantities such as collision and transmission probabilities. Tickoo et al. [34],[35] modeled each
802.11 node as a discrete time G/G/1 queue to derive the service time distribution, but the models are
based on an assumption that the saturated setting provides good approximation for certain quantities in
non-saturated conditions. Chen et al. [36] employed both G/M/1 and G/G/1 queue models on top of [10]
which only considers CW differentiation. Lee et al. [37] analyzed the use of M/G/1 queueing model
while employing a simple non-saturated Markov model to calculate necessary quantities. Medepalli et al.
[38] built upon the average cycle time derivation [7] to obtain individual queue delays using both M/G/1
and G/G/1 queueing models. Foh et al. [39] proposed a Markov framework to analyze the performance
of DCF under statistical traffic. This framework models the number of contending nodes as an M/Ej/1/k
queue. Tantra et al. [40] extended [39] to include service differentiation in EDCA. However, such analysis
is only valid for a restricted scenario where all nodes have a MAC queue size of one packet.
There are also a few studies that investigated the effect of EDCA TXOPs on 802.11e performance
for a saturated scenario. Mangold et al. [41] and Suzuki et al. [42] carried out the performance analysis
through simulation. The efficiency of burst transmissions with block acknowledgements is studied in [43].
Tinnirello et al. [44] also proposed different TXOP managing policies for temporal fairness provisioning.
Peng et al. [45] proposed an analytical model to study the effect of burst transmissions and showed that
improved service differentiation can be achieved using a novel scheme based on TXOP thresholds.
A thorough and careful literature survey shows that an EDCA analytical model which incorporates all
EDCA QoS parameters, CW, AIFS, and TXOP, for any traffic load has not been designed yet.
IV. EDCA DISCRETE-TIME MARKOV CHAIN MODEL
Assuming slot homogeneity, we propose a novel DTMC to model the behavior of the EDCA function
of any AC at any load. The main contribution of this work is that the proposed model considers the
effect of all EDCA QoS parameters (CW, AIFS, and TXOP) on the performance for the whole traffic load
range from a lightly-loaded non-saturated channel to a heavily congested saturated medium. Although we
assume constant probability of packet arrival per state (for the sake of simplicity, Poisson arrivals), we
show that the model provides accurate performance analysis for a range of traffic types.
The state of the EDCA function of any AC at an arbitrary time t depends on several MAC layer events
that may have occured before t. We model the MAC layer state of an ACi, 0 ≤ i ≤ 3, with a 3-dimensional
Markov process, (si(t), bi(t), qi(t)). The stochastic process si(t) represents the value of the backoff stage
at time t, i.e., the number of retransmissions that the packet to be transmitted currently has experienced
until time t. The stochastic process bi(t) represents the state of the backoff counter at time t. Up to this
point, the definition of the first two dimensions follows [3] which is introduced for DCF. In order to
enable the accurate non-saturated analysis considering EDCA TXOPs, we introduce another dimension
which models the stochastic process qi(t) denoting the number of packets buffered for transmission at the
MAC layer. Moreover, as the details will be described in the sequel, in our model, bi(t) does not only
represent the value of the backoff counter, but also the number of transmissions carried out during the
current EDCA TXOP (when the value of backoff counter is actually zero).
Using the assumption of independent and constant collision probability at an arbitrary backoff slot, the
3-dimensional process (si(t), bi(t), qi(t)) is represented as a Discrete-Time Markov Chain (DTMC) with
states (j, k, l) and index i. We define the limits on state variables as 0 ≤ j ≤ ri − 1, −Ni ≤ k ≤ Wi,j
and 0 ≤ l ≤ QSi. In these inequalities, we let ri be the retransmission limit of a packet of ACi; Ni
be the maximum number of successful packet exchange sequences of ACi that can fit into one TXOPi;
Wi,j = 2
min(j,mi)(CWi,min + 1) − 1 be the CW size of ACi at the backoff stage j where CWi,max =
2mi(CWi,min +1)−1, 0 ≤ mi < ri; and QSi be the maximum number of packets that can buffered at the
MAC layer, i.e., MAC queue size. Moreover, it is important to note that a couple of restrictions apply to
the state indices.
• When there are not any buffered packets at the AC queue, the EDCA function of the corresponding
AC cannot be in a retransmitting state. Therefore, if l = 0, then j = 0 should hold. Such backoff
states represent the postbackoff process [1],[2], therefore called as postbackoff slots in the sequel.
The postbackoff procedure ensures that the transmitting station waits at least another backoff between
successive TXOPs. Note that, when l > 0 and k ≥ 0, these states are named backoff slots.
• The states with indices −Ni ≤ k ≤ −1 represent the negation of the number of packets that are
successfully transmitted at the current TXOP rather than the value of the backoff counter (which is
zero during a TXOP). For simplicity, in the design of the Markov chain, we introduced such states in
the second dimension. Therefore, if −Ni ≤ k ≤ −1, we set j = 0. As it will be clear in the sequel,
the addition of these states enables EDCA TXOP analysis.
Let pci denote the average conditional probability that a packet from ACi experiences either an external
or an internal collision after the EDCA function decides on the transmission. Let pnt(l
′, T |l) be the
probability that there are l′ packets in the MAC buffer at time t + T given that there were l packets
at t and no transmissions have been made during interval T . Similarly, let pst(l
′, T |l) be the probability
that there are l′ packets in the MAC buffer at time t + T given that there were l packets at time t and a
transmission has been made during interval T . Note that since we assume Poisson arrivals, the exponential
interarrival distributions are independent, and pnt and pst only depend on the interval length T and are
independent of time t. Then, the nonzero state transmission probabilities of the proposed Markov model
for ACi, denoted as Pi(j
′, k′, l′|j, k, l) adopting the same notation in [3], are calculated as follows.
1) The backoff counter is decremented by one at the slot boundary. Note that we define the postbackoff
or the backoff slot as Bianchi defines the slot time [3]. Then, for 0 ≤ j ≤ ri − 1, 1 ≤ k ≤ Wi,j, and
0 ≤ l ≤ l′ ≤ QSi,
Pi(j, k − 1, l
′|j, k, l) = pnt(l
′, Ti,bs|l). (2)
It is important to note that the proposed DTMC’s evolution is not real-time and the state duration
varies depending on the state. The average duration of a backoff slot Ti,bs is calculated by (29) which
will be derived. Note that, in (2), we consider the probability of packet arrivals during Ti,bs (buffer
size l′ after the state transition depends on this probability).
2) We assume the transmitted packet experiences a collision with constant probability pci (slot homo-
geneity). In the following, note that the cases when the retry limit is reached and when the MAC
buffer is full are treated separately, since the transition probabilities should follow different rules.
Let Ti,s and Ti,c be the time spent in a successful transmission and a collision by ACi respectively
which will be derived. Then, for 0 ≤ j ≤ ri − 1, 0 ≤ l ≤ QSi − 1, and max(0, l − 1) ≤ l
′ ≤ QSi,
Pi(0,−1, l
′|j, 0, l) = (1 − pci) · pst(l
′, Ti,s|l) (3)
Pi(0,−1, QSi − 1|j, 0, QSi) = 1 − pci. (4)
For 0 ≤ j ≤ ri − 2, 0 ≤ k ≤ Wi,j+1, and 0 ≤ l ≤ l
′ ≤ QSi,
Pi(j + 1, k, l
′|j, 0, l) =
pci · pnt(l
′, Ti,c|l)
Wi,j+1 + 1
. (5)
For 0 ≤ k ≤ Wi,0, 0 ≤ l ≤ QSi − 1, and max(0, l − 1) ≤ l
′ ≤ QSi,
Pi(0, k, l
′|ri − 1, 0, l) =
Wi,0 + 1
· pst(l
′, Ti,s|l) (6)
Pi(0, k, QSi − 1|ri − 1, 0, QSi) =
Wi,0 + 1
Note that we use pnt in (5) although a transmission has been made. On the other hand, the packet
has collided and is still at the MAC queue for retransmission as if no transmission has occured. This
is not the case in (3) and (6), since in these transitions a successful transmission or a drop occurs.
When the MAC buffer is full, any arriving packet is discarded as (4) and (7) imply.
3) Once the TXOP is started, the EDCA function may continue with as many packet SIFS-separated
exchange sequences as it can fit into the TXOP duration. Let Ti,exc be the average duration of a
successful packet exchange sequence for ACi which will be derived in (24). Then, for −Ni + 1 ≤
k ≤ −1, 1 ≤ l ≤ QSi, and max(0, l − 1) ≤ l
′ ≤ QSi,
Pi(0, k − 1, l
′|0, k, l) = pst(l
′, Ti,exc|l). (8)
When the next transmission cannot fit into the remaining TXOP, the current TXOP is immediately
concluded and the unused portion of the TXOP is returned. By design, our model includes maximum
number of packets that can fit into one TXOP. Then, for 0 ≤ k ≤ Wi,0 and 1 ≤ l ≤ QSi,
Pi(0, k, l|0,−Ni, l) =
Wi,0 + 1
. (9)
The TXOP ends when the MAC queue is empty. Then, for 0 ≤ k′ ≤ Wi,0 and −Ni ≤ k ≤ −1,
Pi(0, k
′, 0|0, k, 0) =
Wi,0 + 1
. (10)
Note that no time passes in (9) and (10), so the definition of these states and transitions is actually
not necessary for accuracy. On the other hand, they simplify the DTMC structure and symmetry.
4) If the queue is still empty when the postbackoff counter reaches zero, the EDCA function enters
the idle state until another packet arrival. Note (0,0,0) also represents the idle state. We make two
assumptions; i) At most one packet may arrive during Tslot with constant probability ρi (considering
the fact that Tslot is in the order of microseconds, the probability that multiple packets can arrive in
this interval is very small), ii) if the channel is idle at the slot the packet arrives at an empty queue,
the transmission will be successful at AIFS completion without any backoff. The latter assumption
is due to the following reason. While the probability of the channel becoming busy during AIFS
or a collision occuring for the transmission at AIFS is very small at a lightly loaded scenario,
the probability of a packet arrival to an empty queue is very small at a highly loaded scenario. As
observed via simulations, these assumptions do not lead to any noticeable changes in the results while
simplifying the Markov chain structure and symmetry. Then, for 0 ≤ k ≤ Wi,0 and 1 ≤ l ≤ QSi,
Pi(0, 0, 0|0, 0, 0) = (1 − pci) · (1 − ρi) + pci · pnt(0, Ti,b|0), (11)
Pi(0, k, l|0, 0, 0) =
Wi,0 + 1
· pnt(l, Ti,b|0), (12)
Pi(0,−1, l|0, 0, 0) = (1 − pci) · ρi · pnt(l, Ti,s|0). (13)
Let Ti,b in (11) and (12) be the length of a backoff slot given it is not idle. Note that actually a
successful transmission occurs in the state transition in (13). On the other hand, the transmitted packet
is not reflected in the initial queue size state which is 0. Therefore, pnt is used instead of pst.
Parts of the proposed DTMC model are illustrated in Fig. 1 for an arbitrary ACi with Ni = 2. Fig. 1(a)
shows the state transitions for l = 0. Note that in Fig. 1(a) the states with −Ni ≤ k ≤ −2 can only be
reached from the states with l = 1. Fig. 1(b) presents the state transitions for 0 < l < QSi and 0 ≤ j < ri.
Note that only the transition probabilities and the states marked with rectangles differ when j = ri − 1
(as in (6)). Therefore, we do not include an extra figure for this case. Fig. 1(c) shows the state transitions
when l = QSi. Note also that the states marked with rectangles differ when j = ri − 1 (as in (7)). The
combination of these small chains for all j, k, l constitutes our DTMC model.
A. Steady-State Solution
Let bi,j,k,l be the steady-state probability of the state (j, k, l) of the proposed DTMC with index i
which can be solved using (2)-(13) subject to
l bi,j,k,l = 1 (the proposed DTMC is ergodic and
irreducible). Let τi be the probability that an ACi transmits at an arbitrary backoff or postbackoff slot
∑ri−1
l=1 bi,j,0,l
+ bi,0,0,0 · ρi · (1 − pci)
∑ri−1
∑Wi,j
l=0 bi,j,k,l
. (14)
Note that −Ni ≤ k ≤ −1 is not included in the normalization in (14), since these states represent a
continuation in the EDCA TXOP rather than a contention for the access. The value of τi depends on the
values of the average conditional collision probability pci , the various state durations Ti,bs, Ti,b, Ti,s and
Ti,c, and the conditional queue state transition probabilities pnt and pst.
1) Average conditional collision probability pci: The difference in AIFS of each AC in EDCA creates
the so-called contention zones as shown in Fig. 2 [12]. In each contention zone, the number of contending
stations may vary. The collision probability cannot simply be assumed to be constant among all ACs.
We can define pci,x as the conditional probability that ACi experiences either an external or an internal
collision given that it has observed the medium idle for AIFSx and transmits in the current slot (note
AIFSx ≥ AIFSi should hold). For the following, in order to be consistent with the notation of [2], we
assume AIFS0 ≥ AIFS1 ≥ AIFS2 ≥ AIFS3. Let di = AIFSi − AIFS3. Also, let the total number
ACi flows be fi. Then, for the heterogeneous scenario in which each station has only one AC
pci,x = 1 −
i′:di′≤dx
(1 − τi′)
(1 − τi)
. (15)
When each station has multiple ACs that are active, internal collisions may occur. Then, for the scenario
in which each station has all 4 ACs active
pci,x = 1 −
i′:di′≤dx
(1 − τi′)
fi′−1
i′′>i
(1 − τi′′). (16)
Similar extensions when the number of active ACs are 2 or 3 are straightforward.
We use the Markov chain shown in Fig. 3 to find the long term occupancy of contention zones. Each
state represents the nth backoff slot after completion of the AIFS3 idle interval following a transmission
period. The Markov chain model uses the fact that a backoff slot is reached if and only if no transmission
occurs in the previous slot. Moreover, the number of states is limited by the maximum idle time between
two successive transmissions which is Wmin = min(CWi,max) for a saturated scenario. Although this is
not the case for a non-saturated scenario, we do not change this limit. As the comparison with simulation
results show, this approximation does not result in significant prediction errors. The probability that at
least one transmission occurs in a backoff slot in contention zone x is
ptrx = 1 −
i′:di′≤dx
(1 − τi′)
fi′ . (17)
Note that the contention zones are labeled with x regarding the indices of d. In the case of equal AIFS
values, the contention zone is labeled with the index of the AC with higher priority.
Given the state transition probabilities as in Fig. 3, the long term occupancy of the backoff slots b′n can
be obtained from the steady-state solution of the Markov chain. Then, the AC-specific average collision
probability pci is found by weighing zone specific collision probabilities pci,x according to the long term
occupancy of contention zones (thus backoff slots)
pci =
∑Wmin
n=di+1
pci,x · b
∑Wmin
n=di+1
where x = max
y | dy = max
(dz | dz ≤ n)
which shows x is assigned the highest index value within
a set of ACs that have AIFS smaller than or equal to n+AIFS3. This ensures that at backoff slot n, ACi
has sensed the medium idle for AIFSx. Therefore, the calculation in (18) fits into the definition of pci,x .
Note that the average collision probability calculation in [12, Section IV-D] is a special case of our
calculation for two ACs.
2) The state duration Ti,s and Ti,c: Let Ti,p be the average payload transmission time for ACi (Ti,p
includes the transmission time of MAC and PHY headers), δ be the propagation delay, Tack be the time
required for acknowledgment packet (ACK) transmission. Then, for the basic access scheme, we define
the time spent in a successful transmission Ti,s and a collision Ti,c for any ACi as
Ti,s =Ti,p + δ + SIFS + Tack + δ + AIFSi (19)
Ti,c =Ti,p∗ + ACK Timeout + AIFSi (20)
where Ti,p∗ is the average transmission time of the longest packet payload involved in a collision [3].
For simplicity, we assume the packet size to be equal for any AC, then Ti,p∗ = Ti,p. Being not explicitly
specified in the standards, we set ACK Timeout, using Extended Inter Frame Space (EIFS) as EIFSi−
AIFSi.
The extensions of (19) and (20) for the Request-to-Send/Clear-to-Send (RTS/CTS) scheme are
Ti,s =Trts + δ + SIFS + Tcts + δ + SIFS + Ti,p + δ + SIFS + Tack + δ + AIFSi (21)
Ti,c =Trts + CTS Timeout + AIFSi (22)
where Trts and Tcts are the time required for RTS and CTS packet transmissions respectively. Being not
explicitly specified in the standards, we set CTS Timeout as we set ACK Timeout.
3) The state duration Ti,bs and Ti,b: The average time between successive backoff counter decrements
is denoted by Ti,bs. The backoff counter decrement may be at the slot boundary of an idle backoff slot
or the last slot of AIFS following an EDCA TXOP or a collision period. We start with calculating the
average duration of an EDCA TXOP for ACi Ti,txop as
Ti,txop =
l=0 bi,0,−Ni,l · ((Ni − 1) · Ti,exc + Ti,s) +
k=−Ni+1
bi,0,k,0 · ((−k − 1) · Ti,exc + Ti,s)
k=−Ni+1
bi,0,k,0 +
l=0 bi,0,−Ni,l
where Ti,exc is defined as the duration of a successful packet exchange sequence within a TXOP. Since
the packet exchanges within a TXOP are separated by SIFS rather than AIFS,
Ti,exc = Ti,s − AIFSi + SIFS, (24)
Ni = max(1, ⌊(TXOPi + SIFS)/Ti,exc⌋). (25)
Given τi and fi, simple probability theory can be used to calculate the conditional probability of no
transmission (pidlex,i ), only one transmission from ACi′ (p
suci′
x,i ), or at least two transmissions (p
x,i) at the
contention zone x given one ACi is in backoff.
pidlex,i =
i′:di′≤dx
(1 − τi′)
fi′ , if di > dx
i′:di′≤dx
(1 − τi′)
1 − τi
, if di ≤ dx.
suci′
x,i =
0, if dx < di′
fi′τi′(1 − τi′)
fi′−1
i′′:di′′≤dx
(1 − τi′′)
fi′′ , if di > dx and di′ ≤ dx
fi′τi′(1 − τi′)
fi′−1
1 − τi
i′′:di′′≤dx
(1 − τi′′)
fi′′ , if di ≤ dx and di′ ≤ dx.
pcolx,i =1 − p
x,i −
suci′
x,i (28)
Let xi be the first contention zone in which ACi can transmit. Then,
Ti,bs =
xi<x′≤3
(pidlex′,i · Tslot + p
x′,i · Ti,c +
suci′
x′,i · Ti′,txop) · pzx′ (29)
where pzx denotes the stationary distribution for a random backoff slot being in zone x. Note that, in
(29), the fractional term before summation accounts for the busy periods experienced before AIFSi is
completed. Therefore, if we let d−1 = Wmin,
pzx =
min(dx′ |dx′>dx)
n=dx+1
b′n. (30)
The expected duration of a backoff slot given it is busy and one ACi is in idle state is calculated as
Ti,b =
pcolx′,i
1 − pidlex′,i
· Ti,c +
suci′
1 − pidlex′,i
· Ti′,txop
· pzx′ . (31)
4) The conditional queue state transition probabilities pnt and pst: We assume the packets arrive at
the AC queue with size QSi according to a Poisson process with rate λi packets per second. Using the
probability distribution function of the Poisson process, the probability of k arrivals occuring in time
interval t can be calculated as
Pr(Nt,i = k) =
exp−λit(λit)
. (32)
Then, pnt(l
′, T |l) and pst(l
′, T |l) can be calculated as follows. Note that the finite buffer space is
considered throughout calculations since the number of packets that may arrive during T can be more
than the available queue space.
pnt(l
′, T |l) =
Pr(NT,i = l
′ − l), if l′ < QSi
QSi−1
Pr(NT,i = l
′ − l), if l′ = QSi.
pst(l
′, T |l) =
Pr(NT,i = l
′ − l + 1), if l′ < QSi
QSi−1
l′=l−1
Pr(NT,i = l
′ − l + 1), if l′ = QSi.
Note that in (11)-(14), ρi = 1−Pr(NTslot,i = 0). Together with the steady-state transition probabilities,
(14)-(34) represent a nonlinear system which can be solved using numerical methods.
B. Normalized Throughput Analysis
The normalized throughput of a given ACi, Si, is defined as the fraction of the time occupied by the
successfully transmitted information. Then,
psiNi,txopTi,p
pITslot +
i′ psi′Ti′,txop + (1 − pI −
i′ psi′ )Tc
pI is the probability of the channel being idle at a backoff slot, psi is the conditional successful transmission
probability of ACi at a backoff slot, and Ni,txop = (Ti,txop−AIFSi+SIFS)/Ti,exc. Note that, we consider
Ni,txop and Ti,txop in (35) to define the generic slot time and the time occupied by the successfully
transmitted information in the case of EDCA TXOPs.
The probability of a slot being idle, pI , depends on the state of previous slots. For example, conditioned
on the previous slot to be busy (pB = 1−pI ), pI only depends on the transmission probability of the ACs
with the smallest AIFS, since others have to wait extra AIFS slots. Generalizing this to all AIFS slots, pI
can be calculated as
γnpB(pI)
γnpBp
I + γd0p
I (36)
where γn denotes the probability of no transmission occuring at the (n + 1)
th AIFS slot after AIFS3.
Substituting γn = γd0 for n ≥ d0, and releasing the condition on the upper limit of summation, Wmin,
to ∞, pI can be approximated as in (36). According to the simulation results, this approximation works
well. Note that γn = 1 − p
x where x = max
y | dy = max
(dz | dz ≤ n)
The probability of successful transmission psi is conditioned on the states of the previous slots as well.
This is again because the number of stations that can contend at an arbitrary backoff slot differs depending
on the number of previous consecutive idle backoff slots. Therefore, for the heterogeneous case, in which
each station only has one AC, psi can be calculated as
psi =
(1 − τi)
n=di+1
(n−1)
i′:0≤di′≤(n−1)
(1 − τi′)
+ (pI)
(1 − τi′)
 . (37)
Similarly, for the scenario, in which each station has four active ACs,
psi =
(1 − τi)
n=di+1
(n−1)
i′:0≤di′≤(n−1)
(1 − τi′)
fi′−1
i′′>i
(1 − τi′′)
+ (pI)
(1 − τi′)
fi′−1
i′′>i
(1 − τi′′)
. (38)
C. Average Delay Analysis
Our goal is to find total average delay E[Di] which is defined as the average time from when a packet
enters the MAC layer queue of ACi until it is successfully transmitted. Di has two components; i) queueing
time Qi and ii) access time Ai. Qi is the period that a packet waits in the queue for other packets in
front to be transmitted. Ai is the period a packet waits at the head of the queue until it is transmitted
successfully (backoff and transmission period). We carry out a recursive calculation as in [11] to find
E[Ai] for ACi. Then, using E[Ai] and bi,j,k,l, we calculate E[Di]=E[Qi]+E[Ai]. Note that, E[Ai] differs
depending on whether the EDCA function is idle or not when the packet arrives. We will treat these cases
separately. In the sequel, Ai,idle denotes the access delay when the EDCA function is idle at the time a
packet arrives.
The recursive calculation is carried out in a bottom-to-top and left-to-right manner on the AC-specific
DTMC. For the analysis, let Ai(j, k) denote the time delay from the current state (j, k, l) until the packet
at the head of the ACi queue is transmitted successfully (l ≥ 1). The initial condition on the recursive
calculation is
Ai(ri − 1, 0) = Ti,s. (39)
Recursive delay calculations for 0 ≤ j ≤ ri − 1 are
Ai(j, k) =
Ai(j, k − 1) + Ti,bs, if 1 ≤ k ≤ Wi,j
(1 − pci)Ti,s + pci
PWi,j+1
Ai(j+1,k
Wi,j+1+1
+ Ti,c
, if k = 0 and j 6= ri − 1.
Then,
E[Ai] =
∑Wi,0
k=0 Ai(0, k)
Wi,0 + 1
Following the assumptions made in (11)-(13) and considering the packet loss probability due to the
retry limit as pl,r = (pci)
ri (note that the delay a dropped packet experiences cannot be considered in a
total delay calculation), E[Ai,idle] can be calculated as
E[Ai,idle] = Ti,s · (1 − pci) + (E[Ai] + Ti,b) · pci · (1 − pl,r). (42)
In this case, the average access delay is equal to the total average delay, i.e., Di(0, 0, 0) = E[Ai,idle].
We perform another recursive calculation to calculate the total delay a packet experiences Di(j, k, l)
(given that the packet arrives while the EDCA function is at state (j, k, l)). In the calculations, we account
for the remaining access delay for the packet at the head of the MAC queue and the probability that this
packet may be dropped due to the retry limit.
Let Ai,d(j, k) be the access delay conditioned that the packet drops. Ai,d(j, k) can easily be calculated by
modifying the recursive method of calculating Ai(j, k). The initial condition on this recursive calculation
Ai,d(ri − 1, 0) = Ti,c. (43)
Recursive delay calculations for 0 ≤ j ≤ ri − 1 are
Ai,d(j, k) =
Ai(j, k − 1) + Ti,bs, if 1 ≤ k ≤ Wi,j
Wi,j+1
Ai,d(j + 1, k) + Ti,c, if k = 0 and j 6= ri − 1.
Then,
E[Ai,d] =
∑Wi,0
k=0 Ai,d(0, k)
Wi,0 + 1
If a packet arrives during the backoff of another packet, it is delayed at least for the remaining access
time. Depending on the queue size, it may be transmitted at the current TXOP, or may be delayed till
further accesses are gained. Then, for 0 ≤ j ≤ ri − 1, 0 ≤ k ≤ Wi,j, and 1 ≤ l ≤ QSi,
Di(j, k, l) =(1 − pl,r) · (Ai(j, k) + min(Ni − 1, l − 1) · Ti,exc + Di(−1,−1, l − Ni))
+ pl,r · (Ai,d(j, k) + Di(−1,−1, l − 1)) . (46)
When the packet arrives during postbackoff, the total delay is equal to the access delay. Then, for 0 ≤
k ≤ Wi,j and l = 0,
Di(j, k, l) = Ai(j, k). (47)
When the packet arrives during a TXOP, it may be transmitted at the current TXOP, or it may wait for
further accesses. Then, for −Ni + 1 ≤ k ≤ −1 and 1 ≤ l ≤ QSi,
Di(j, k, l) = min(k − 1, l) · Ti,exc + Di(−1,−1, l − k + 1). (48)
Di(−1,−1, l) is calculated recursively according to the value of l
Di(−1,−1, l) =
0, if l ≤ 0
E[Ai] · (1 − pl,r), if l = 1
χ, if l > 1
where
χ =(1 − pl,r) · (E[Ai] + min(Ni − 1, l − 1) · Ti,exc
+Di(−1,−1, l − Ni)) + pl,r · (E[Ai,d] + Di(−1,−1, l − 1)) . (50)
Let the probability of any arriving packet seeing the EDCA function at state (j, k, l) be b̄i,j,k,l. Since
we assume independent and exponentially distributed packet interarrivals, b̄i,j,k,l can simply be calculated
by normalizing bi,j,k,l excluding the states in which no time passes, i.e., ∀(j, k, l) such that (0,−Ni, 1 ≤
l ≤ QSi) or (0,−Ni ≤ k ≤ −1, 0). Note that b̄i,j,k,l is zero for these states
b̄i,j,k,l =
bi,j,k,l
l=1 bi,0,−Ni,l −
k=−Ni
bi,0,k,0
. (51)
Then, the total average delay a successful packet experiences E[Di] can be calculated averaging Di(j, k, l)
over all possible states
E[Di] = E[Ai,idle] · b̄i,0,0,0 +
∀(j,k,l)/(0,0,0)
Di(j, k, l) · b̄i,j,k,l. (52)
D. Average Packet Loss Ratio
We consider two types of packet losses; i) the packet is dropped when the MAC layer retry limit is
reached, ii) the packet is dropped if the MAC queue is full at the time of packet arrival. Let plri denote
the average packet loss ratio for ACi. We use the steady-state probability bi,j,k,l to find the probability
whether the MAC queue is full or not at the time of packet arrival. If the queue is full, the arriving packet
is dropped (second term in (53)). Otherwise, the packet is dropped with probability prici , i.e. only if the
retry limit is reached (first term in (53)). Note that we consider packet retransmissions only due to packet
collisions. Then,
plri =
QSi−1
bi,j,k,l · p
bi,j,k,QSi. (53)
E. Queue Size Distribution
Due to the specific structure of the proposed model, it is straightforward to calculate the MAC queue
size distribution for ACi. Note that we use queue size distribution in the calculation of average packet
loss ratio.
Pr(l = l′) =
bi,j,k,l′. (54)
V. NUMERICAL AND SIMULATION RESULTS
We validate the accuracy of the numerical results calculated via the proposed EDCA model by comparing
them with the simulations results obtained from ns-2 [46]. For the simulations, we employ the IEEE
802.11e HCF MAC simulation model for ns-2.28 that we developed [47]. This module implements all
the EDCA and HCCA functionalities stated in [2].
As in all work on the subject in the literature, we consider ACs that transmit fixed-size User Datagram
Protocol (UDP) packets. In simulations, we consider two ACs, one high priority and one low priority.
Each station runs only one traffic class. Unless otherwise stated, the packets are generated according to
a Poisson process with equal rate for both ACs. We set AIFSN1 = 3, AIFSN3 = 2, CW1,min = 15,
CW3,min = 7, m1 = m3 = 3, r1 = r3 = 7. For both ACs, the payload size is 1034 bytes. Again, as in
most of the work on the subject, the simulation results are reported for the wireless channel which is
assumed to be not prone to any errors during transmission. The errored channel case is left for future
study. All the stations have 802.11g Physical Layer (PHY) using 54 Mbps and 6 Mbps as the data and
basic rate respectively (Tslot = 9 µs, SIFS = 10 µs) [48]. The simulation runtime is 100 seconds.
Fig. 4 shows the differentiation of throughput for two ACs when EDCA TXOP limits of both are set
to 0 (1 packet exchange per EDCA TXOP). In this scenario, there are 5 stations for both ACs and they
are transmitting to an AP. The normalized throughput per AC as well as the total system throughput
is plotted for increasing offered load per AC. We have carried out the analysis for maximum MAC
buffer sizes of 2 packets and 10 packets. The comparison between analytical and simulation results
shows that our model can accurately capture the linear relationship between throughput and offered load
under low loads, the complex transition in throughput between under-loaded and saturation regimes, and
the saturation throughput. Although we do not present here, considerable inaccuracy is observed if the
postbackoff procedure, varying collision probability among different AIFS zones, and varying service time
among different backoff stages are not modeled correctly as proposed. The results also present that the
slot homogeneity assumption works accurately in a non-saturated model for throughput estimation.
The proposed model can also capture the throughput variation with respect to the size of the MAC buffer.
The results reveal how significantly the size of the MAC buffer affects the throughput in the transition
period from underloaded to highly loaded channel. This also shows small interface buffer assumptions of
previous models [27],[28],[29],[40] can lead to considerable analytical inaccuracies. Although the total
throughput for the small buffer size case has higher throughput in the transition region for the specific
example, this cannot be generalized. The reason for this is that AC1 suffers from low throughput for
QS1 = 10 due to the selection of EDCA parameters, which affects the total throughput.
It is also important to note that the throughput performance does not differ significantly (around %1-
%2) for buffer sizes larger than 10 packets for the given scenarios. Therefore, we do not include such
cases in order not to complicate the figures. Since the complexity of the mathematical solution increases
with the increasing size of the third dimension of DTMC, it may be preferable to implement the model
for smaller queue sizes when the throughput performance is not expected to be affected by the selection.
Fig. 5 depicts the differentiation of throughput for two ACs when EDCA TXOP limits are set to 1.504
ms and 3.008 ms for high and low priority ACs respectively. For TXOP limits, we use the suggested values
for voice and video ACs in [2]. It is important to note that the model works for an arbitrary selection of the
TXOP limit. According to the selected TXOP limits, N1 = 5 and N2 = 11. The normalized throughput per
AC as well as the total system throughput is plotted while increasing offered load per AC. We have done
the analysis for maximum MAC buffer sizes of 2 packets and 10 packets. The model accurately captures
the throughput for any traffic load. As expected, increasing maximum buffer size to 10 packets increases
the throughput both in the transition and the saturation region. Note that when more than a packet fits
into EDCA TXOPs, this decreases contention overhead which in turn increases channel utilization and
throughput (comparison of Fig. 5 with Fig. 4). Although corresponding results are not presented here, the
model works accurately for higher queue sizes in the case of EDCA TXOPs as well.
Fig. 6 displays the differentiation of throughput for two ACs when packet arrival rate is fixed to 2
Mbps and the station number per AC is increased. We have done the analysis for the MAC buffer size
of 10 packets with EDCA TXOPs enabled. The analytical and simulation results are well in accordance.
As the traffic load increases, the differentiation in throughput between the ACs is observed.
Fig. 7 shows the normalized throughput for two ACs when offered load per AC is not equal. In this
scenario, we set the packet arrival rate per AC1 to 2 Mbps and the packet arrival rate per AC3 to 0.5
Mbps. The analytical and simulation results are well in accordance. As the traffic load increases, AC3
maintains linear increase with respect to offered load, while AC1 experiences decrease in throughput due
to larger settings of AIFS and CW if the total number of stations exceeds 22.
In the design of the model, we assume constant packet arrival probability per state. The Poisson arrival
process fits this definition because of the independent exponentially distributed interarrival times. We have
also compared the throughput estimates obtained from the analytical model with the simulation results
obtained using an On/Off traffic model in Fig. 8. A similar study has first been made for DCF in [27].
We modeled the high priority with On/Off traffic model with exponentially distributed idle and active
intervals of mean length 1.5 s. In the active interval, packets are generated with Constant Bit Rate (CBR).
The low priority traffic uses Poisson distributed arrivals. Note that we leave the packet size unchanged,
but normalize the packet arrival rate according to the on/off pattern so that total offered load remains
constant to have a fair comparison. The analytical predictions closely follow the simulation results for the
given scenario. We have observed that the predictions are more sensitive if the transition region is entered
with a few number of stations (5 stations per AC).
Our model also provides a very good match in terms of the throughput for CBR traffic. In Fig. 9, we
compare the throughput prediction of the proposed model with simulations using CBR traffic. The packet
arrival rate is fixed to 2 Mbps for both ACs and the station number per AC is increased. MAC buffer size
is 10 packets and EDCA TXOPs are enabled.
Fig. 10 depicts the total average packet delay with respect to increasing traffic load per AC. We present
the results for two different scenarios. In the first scenario, TXOP limits are set to 0 ms for both ACs.
In the second scenario, TXOP limits are set to 1.504 ms and 3.008 ms for high and low priority ACs
respectively. The analysis is carried out for a buffer size of 10 packets. As the results imply, the analytical
results closely follow the simulation results for both scenarios. In the lightly loaded region, the delays
are considerably small. The increase in the transition region is steeper when TXOP limits are 0. In the
specific example, enabling TXOPs decreases the total delay where the decrease is more considerable for
the low priority AC (due to selection of parameters). Since the buffer size is limited, the total average
delay converges to a specific value as the load increases. Still this limit is not of interest, since the packet
loss rate at this region is unpractically large. Note that this limit will be higher for larger buffers. The
region of interest is the start of the transition region (between 2 Mbps and 3 Mbps for the example in
Fig. 10). On the other hand, we also display other data points to show the performance of the model for
the whole load span.
Fig. 11 depicts the average packet loss ratio with respect to increasing traffic load per AC. We present
the results for two different scenarios. In the first scenario, TXOP limits are set to 0 ms for both ACs.
In the second scenario, TXOP limits are set to 1.504 ms and 3.008 ms for high and low priority ACs
respectively. The analysis is carried out for a buffer size of 10 packets. As the results imply, the analytical
results closely follow the simulation results for both scenarios. Although it is not presented in Fig. 11,
the packet loss ratio drops exponentially to 0 when the offered load per AC is lower than 2.5 Mbps.
The results presented in this paper fixes the AIFS and CW parameters for each AC. The results are
compared for different TXOP values at varying traffic load. Therefore, the presented results can mainly
indicate the effects of TXOP on the maximum throughput. The model can also be used in order to
investigate the effects of AIFS and CW on the maximum throughput.
As the comparison of Fig. 4 and Fig. 5 reveals, the total throughput can be maximized with the
introduction of EDCA TXOPs which enable multiple frame transmissions in one channel access (note
that MAC buffer sizes for each AC should be equal to or larger than the number of packets that can fit
to the AC-specific TXOP in order to efficiently utilize each TXOP gained). EDCA TXOPs decrease the
channel contention overhead and the ACs can efficiently utilize the resources. Note also that the effects of
EDCA TXOPs in the lightly loaded region is marginal compared to highly loaded region. This is expected
since the MAC queues do not build up in the lightly loaded scenario where stations usually have just one
packet to send at their access to the channel.
As Fig. 4 shows the saturation throughput is usually less than the maximum throughput that can be
obtained. This is also observed for DCF in [3]. Similarly, in Fig. 6-Fig. 9, the total throughput slightly
decreases as the total load increases. As the load in the system increases the collision overhead becomes
significant which decreases the total channel utilization. On the other hand, as also discussed in [3], the
point where the maximum throughput is observed is unstable in a random access system. Therefore, a
good admission control algorithm should be defined to operate the system at the point right before the
lightly loaded to highly loaded transition region starts.
VI. CONCLUSION
We have presented an accurate Markov model for analytically calculating the EDCA throughput and
delay for the whole traffic load range from a lightly loaded non-saturated channel to a heavily congested
saturated medium. The presented model shows the accuracy of the homogeneous slot assumption (constant
collision and transmission probability at an arbitrary backoff slot) that is extensively studied in saturation
scenarios for the whole traffic range. The presented model accurately captures the linear relationship
between throughput and offered load under low loads and the limiting behavior of throughput at saturation.
The key contribution of this paper is that the model accounts for all of the differentiation mechanisms
EDCA proposes. The analytical model can incorporate any selection of AC-specific AIFS, CW, and TXOP
values for any number of ACs. The model also considers varying collision probabilities at different con-
tention zones which provides accurate AIFS differentiation analysis. Although not presented explicitly in
this paper, it is straightforward to extend the presented model for scenarios where the stations run multiple
ACs (virtual collisions may take place) or RTS/CTS protection mechanism is used. The approximations
made for the sake of DTMC simplicity and symmetry may also be removed easily for increased accuracy,
although they are shown to be highly accurate.
We also show that the MAC buffer size affects the EDCA performance significantly between underloaded
and saturation regimes (including saturation) especially when EDCA TXOPs are enabled. The presented
model captures this complex transition accurately. This analysis also points out the fact that including an
accurate queue treatment is vital. Incorporating MAC queue states also enables EDCA TXOP analysis so
that the EDCA TXOP continuation process is modeled in considerable detail. To the authors’ knowledge
this is the first demonstration of an analytic model including EDCA TXOP procedure for finite load.
It is also worth noting that our model can easily be simplified to model DCF behavior. Moreover, after
modifying our model accordingly, the throughput analysis for the infrastructure WLAN where there are
transmissions both in the uplink and downlink can be performed (note that in a WLAN downlink traffic
load may significantly differ from uplink traffic load).
Although the Markov analysis assumes the packets are generated according to Poisson process, the
comparison with simulation results shows that the throughput analysis is valid for a range of traffic types
such as CBR and On/Off traffic (On/Off traffic model is a widely used model for voice and telnet traffic).
The non-existence of a closed-form solution for the Markov model limits its practical use. On the other
hand, the accurate saturation throughput analysis can highlight the strengths and the shortcomings of
EDCA for varying scenarios and can provide invaluable insights. The model can effectively assist EDCA
parameter adaptation or a call admission control algorithm for improved QoS support in the WLAN.
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http://newport.eecs.uci.edu/$\sim $fkeceli/ns.htm
0,-1,0 0,0,0 0,1,0 0,2,0 0,Wi,0-1,0 0,Wi,0,0
0,1,l’ 0,Wi,0-1,l’0,0,l’ 0,Wi,0-2,l’
pnt(0,Ti,bs|0)pnt(0,Ti,bs|0)pnt(0,Ti,bs|0)
0,-1,l’
.pnt(0,Ti,s|0)
.pnt(l’,Ti,s|0)
pnt(l’,Ti,bs|0)pnt(l’,Ti,bs|0)pnt(l’,Ti,bs|0)
)(1-ρ
0,0BO0,l’
pnt(l’,Ti,b|0)
0,-2,0
0,-1,l j,0,l j,1,l j,2,l j,Wi,j-1,l j,Wi,j,l
j,1,l’ j,Wi,j-1,l’j,0,l’ j,Wi,j-2,l’
pnt(l,Ti,bs|l)pnt(l,Ti,bs|l)
0,-1,l’
pnt(l’,Ti,bs|l)pnt(l’,Ti,bs|l)pnt(l’,Ti,bs|l)
pnt(l,Ti,bs|l)
0,-2,l
0,-2,l’
pst(l’,Ti,exc|l)
pst(l,Ti,exc|l)
j+1,l’
(1-pc
)pst(l,Ti,s|l)
(1-pc
)pst(l’,Ti,s|l)
pnt(l’,Ti,c|l)
0,-1,l-1 j,0,l j,1,l j,2,l j,Wi,j-1,l j,Wi,j,l
j,lBOj+1,l
1111-pc
Fig. 1. Parts of the proposed DTMC model for Ni=2. The combination of these small chains for all j, k, l constitutes the proposed DTMC
model. (a) l = 0. (b) 0 < l < QSi. (c) l = QSi. Remarks: i) the transition probabilities and the states marked with rectangles differ when
j = ri − 1 (as in (6) and (7)), ii) the limits for l
′ follow the rules in (2)-(13).
Transmission/
Collision period
AIFSN
AIFSN
AIFSN
AIFSN
No Tx Zone 3 Zone 2 Zone 1
AC3 in Backoff
AC2 in Backoff
AC1 in Backoff
AC0 in Backoff
Fig. 2. EDCA backoff after busy medium.
1 d2 d1 Wmin
tr 1-p
tr 1-p
Fig. 3. Transition through backoff slots in different contention zones for the example given in Fig.2.
1 2 3 4 5 6 7 8 9 10
Offered traffic rate per AC (Mbps)
QS=2, AC
 − analysis
QS=2, AC
 − analysis
QS=2, total − analysis
QS=2, AC
 − sim
QS=2, AC
 − sim
QS=2, total −sim
QS=10, AC
 − analysis
QS=10, AC
 − analysis
QS=10, total − analysis
QS=10, AC
 − sim
QS=10, AC
 − sim
QS=10, total − sim
Fig. 4. Normalized throughput prediction of the proposed model for 2 AC heterogeneous scenario with respect to increasing load per AC
at each station and varying MAC buffer size in basic access mode (TXOP3 = 0, TXOP1 = 0). Simulation results are also added for
comparison.
1 2 3 4 5 6 7 8 9 10
Offered traffic rate per AC (Mbps)
QS=2, AC
 − analysis
QS=2, AC
 − analysis
QS=2, total − analysis
QS=2, AC
 − sim
QS=2, AC
 − sim
QS=2, total − sim
QS=10, AC
 − analysis
QS=10, AC
 − analysis
QS=10, total − analysis
QS=10, AC
 − sim
QS=10, AC
 − sim
QS=10, total − sim
Fig. 5. Normalized throughput prediction of the proposed model for 2 AC heterogeneous scenario with respect to increasing load per AC
at each station and varying MAC buffer size in basic access mode (TXOP3 = 1504ms, TXOP1 = 3008ms). Simulation results are also
added for comparison.
2 3 4 5 6 7 8 9 10 11 12
Number of stations per AC
 − analysis
 − analysis
total − analysis
 − sim
 − sim
total −sim
Fig. 6. Normalized throughput prediction of the proposed model for 2 AC heterogeneous scenario with respect to increasing number
of stations when MAC buffer size is 10 packets and total offered load per AC is 2 Mbps (TXOP3 = 1504ms, TXOP1 = 3008ms).
Simulation results are also added for comparison.
8 9 10 11 12 13 14 15 16
Number of stations per AC
 − analysis
 − analysis
total − analysis
 − sim
 − sim
total − sim
Fig. 7. Normalized throughput prediction of the proposed model for 2 AC heterogeneous scenario with respect to increasing number of
stations when MAC buffer size is 10 packets (TXOP3 = 1504ms, TXOP1 = 3008ms). Total offered load per AC3 is 0.5 Mbps while
total offered load per AC3 is 2 Mbps. Simulation results are also added for comparison.
18 19 20 21 22 23 24 25 26 27
Number of stations per AC
t AC1 − analysis
 − analysis
total − analysis
, Poisson − sim
, On/Off − sim 
total − sim
Fig. 8. Normalized throughput prediction of the proposed model for 2 AC heterogeneous scenario with respect to increasing number of
stations when total offered load per AC is 0.5 Mbps (TXOP3 = 1504ms, TXOP1 = 3008ms). Simulation results are also added for
the scenario when AC3 uses On/Off traffic with exponentially distributed idle and active times both with mean 1.5s. AC1 uses Poisson
distribution for packet arrivals.
2 3 4 5 6 7 8 9 10 11 12
Number of stations per AC
 − analysis
 − analysis
total − analysis
, CBR − sim
, CBR − sim
total, CBR − sim
Fig. 9. Normalized throughput prediction of the proposed model for 2 AC heterogeneous scenario with respect to increasing number
of stations when MAC buffer size is 10 packets and total offered load per AC is 2 Mbps (TXOP3 = 1504ms, TXOP1 = 3008ms).
Simulation results are also added for the scenario when both AC1 and AC3 uses CBR traffic.
1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6
Offered traffic rate per AC (Mbps)
 − analysis
 − analysis
 − sim
 − sim
 + TXOP − analysis
 + TXOP − analysis
 + TXOP − sim
 + TXOP − sim
Fig. 10. Total average delay prediction of the proposed model for 2 AC heterogeneous scenario with respect to increasing load per AC at
each station. In the first scenario, TXOP limits are set to 0 ms for both ACs. In the second scenario, TXOP limits are set to 1.504 ms and
3.008 ms for high and low priority ACs respectively. Simulation results are also added for comparison.
2.5 3 3.5 4 4.5 5
Offered traffic rate per AC (Mbps)
 − analysis
 − analysis
 − sim
 − sim
 + TXOP − analysis
 + TXOP − analysis
 + TXOP − sim
 + TXOP − sim
Fig. 11. Average packet loss ratio prediction of the proposed model for 2 AC heterogeneous scenario with respect to increasing load per
AC at each station. In the first scenario, TXOP limits are set to 0 ms for both ACs. In the second scenario, TXOP limits are set to 1.504
ms and 3.008 ms for high and low priority ACs respectively. Simulation results are also added for comparison.
	Introduction
	EDCA Overview
	Related Work
	EDCA Discrete-Time Markov Chain Model
	Steady-State Solution
	Average conditional collision probability pci
	The state duration Ti,s and Ti,c
	The state duration Ti,bs and Ti,b
	The conditional queue state transition probabilities pnt and pst
	Normalized Throughput Analysis
	Average Delay Analysis
	Average Packet Loss Ratio
	Queue Size Distribution
	Numerical and Simulation Results
	Conclusion
	References

  The IEEE 802.11e standard revises the Medium Access Control (MAC) layer of
the former IEEE 802.11 standard for Quality-of-Service (QoS) provision in the
Wireless Local Area Networks (WLANs). The Enhanced Distributed Channel Access
(EDCA) function of 802.11e defines multiple Access Categories (AC) with
AC-specific Contention Window (CW) sizes, Arbitration Interframe Space (AIFS)
values, and Transmit Opportunity (TXOP) limits to support MAC-level QoS and
prioritization. We propose an analytical model for the EDCA function which
incorporates an accurate CW, AIFS, and TXOP differentiation at any traffic
load. The proposed model is also shown to capture the effect of MAC layer
buffer size on the performance. Analytical and simulation results are compared
to demonstrate the accuracy of the proposed approach for varying traffic loads,
EDCA parameters, and MAC layer buffer space.

Introduction
	EDCA Overview
	Related Work
	EDCA Discrete-Time Markov Chain Model
	Steady-State Solution
	Average conditional collision probability pci
	The state duration Ti,s and Ti,c
	The state duration Ti,bs and Ti,b
	The conditional queue state transition probabilities pnt and pst
	Normalized Throughput Analysis
	Average Delay Analysis
	Average Packet Loss Ratio
	Queue Size Distribution
	Numerical and Simulation Results
	Conclusion
	References
