Admission Control of VoIP calls in EDCA
WLANs: Analysis and Experimentation
Filippo Cacace, Giulio Iannello, Massimo Vellucci, and Luca Vollero
Università Campus Bio-Medico di Roma
Abstract— The Enhanced Distributed Channel Access (EDCA)
is a component of the new IEEE 802.11e standard [2], which
introduces Quality of Service (QoS) support in Wireless LANs
(WLANs). This paper proposes a model for the VoIP capacity of
an EDCA WLAN. The proposed solution decouples the problem
of estimating stations performance and that of evaluating channel
congestion. Moreover, it models the dynamics of the sending
queues. This translates in a model working under generic load
conditions (i.e. VoIP traffic at different bit rates) that captures the
impact of all the parameters introduced by EDCA. To validate
the model, we derived a simple Admission Control algorithm for
VoIP and generic real-time symmetric applications. Experimental
analysis and simulations have been used to validate the model
and to establish its limitations.
A comparison of our proposal with other approaches is
provided in Section III. To validate our model we used experimentation with a real testbed and simulations. Results are
reported in Section VI. Specifically, we verified our model first
with experiments and, when experiments were not possible for
the limited number of stations in the testbed, we used simulations. We also used simulations to explain differences between
experiments and analysis due to the physical characteristics of
the Wireless Medium that the analysis does not consider. The
results proves the ability of the model to capture the network
behavior and suggest how to increase the number of admissible
VoIP flows by adapting the packet size generated by VoIP
applications.
I. I NTRODUCTION
Real time and multimedia communications with Quality of
Service (QoS) support are increasingly important in wireless
networks of any nature, due to the growing demand of services like VoIP, streaming and videoconferencing and to the
network’s limited capacity. They are especially challenging for
WLANs based on the IEEE 802.11b [1] protocol due to the
distributed and contention-based nature of the channel access
mechanism.
The introduction in the IEEE 802.11e standard [2] of a new
contention access scheme called Enhanced Distributed Channel Access (EDCA) has provided mechanisms for QoS support
which were previously unavailable with the Distributed Coordination Function (DCF) used in the widely deployed 802.11
WLANs. The EDCA is however only a basic mechanism
which should be used in the context of a comprehensive
approach to implement QoS support. Such an approach should
also include a method for allocating resources among distinct
classes of traffic, like real time (RT), streaming, best effort,
(BE), as well as algorithms for the admission control of higher
priority flows and for EDCA parameters setting.
In this paper we propose a new model to evaluate the VoIP
capacity of an EDCA WLAN. We focus on this problem
because VoIP call admission control (CAC) is of relevant
practical interest in WLANs. Even if our model considers only
VoIP traffic, admitted VoIP flows can be easily protected from
interfering BE traffic by using the AIFS parameter [11], thus
yielding an effective CAC method. From a theoretical point
of view, our model provides the basis for an extension that
includes BE traffic, even though it is questionable that an
analytical approach is the best way to deal with the RT-BE
traffic interaction.
978-1-4244-1845-9/08/$25.00 © 2008 IEEE
60
II. BACKGROUND
Quality of Services support in WiFi network is provided
by the IEEE 802.11e standard [2]. IEEE 802.11e defines the
Hybrid Coordination Function (HCF), which includes the HCF
Controlled Channel Access (HCCA) and the HCF contentionbased channel access, also known as Enhanced Distributed
Channel Access (EDCA). HCCA and EDCA are interoperable
channel access mechanisms. HCCA is based on polling, while
EDCA is based on a slotted and highly parametric CSMA/CA
protocol. Both HCCA and EDCA distribute Transmission
Opportunities (TXOPs), in which stations (STAs) are allowed
to transmit one or more data frames.
The EDCA mechanism defines the concept of Access
Category (AC). Each STA may use up to four ACs, each
AC implementing a slotted CSMA/CA algorithm with its
own parameter. All the ACs compete independently to obtain
TXOPs. The four ACs within a station represent four priority
levels for data transmission. The standard names these levels
as background (BK), best effort (BE), video (VI) and voice
(VO). In infrastructure configurations, APs announce the configuration of ACs in selected beacon frames.
The EDCA access protocol extends the access mechanism
of IEEE 802.11: the Distributed Coordination Function (DCF).
When an AC obtains a TXOP, it can transfer at least the first
frame waiting in its queue. Moreover the AC can transmit more
frames whether allowed by the AP. Specifically, each AC has
a maximum channel occupancy time, called TXOPlimit . This
limit is advertised by the AP with all the other QoS parameters.
If its TXOPlimit is equal to zero, the AC is allowed to transmit
only the first frame waiting in its queue for each TXOP it
gains. When the TXOPlimit is greater than zero, the AC is
IT-NEWS 2008
allowed to transmit as long as the total channel occupancy
time is less or equal than the TXOPlimit .
An idle AC starts competing for a TXOP upon the arrival
of a new frame in its queue. If the frame arrives and no more
ACs are active in the same STA, the AC senses the WM to
assess if it is idle or busy. If the channel is idle, the AC
ensures that it remains idle for a fixed interval of time: the
Arbitration Inter-Frame Space (AIFS). The AIFS is another
QoS parameter advertised by the AP and it can vary from AC
to AC.
After the AIFS has expired, the AC is allowed to transmit.
The transmission can be successful or unsuccessful. If the
transmission is successful, the receiving STA transmits back
to the transmitting STA a special frame, the ACK frame,
acknowledging for the success in the transmission. The AC
that has obtained the TXOP handles the transmission of all
the frames waiting in its queue until its TXOPlimit has been
consumed. If the transmission is unsuccessful, the AC enters
the Backoff process. The Backoff process is also used when the
channel is sensed busy during the first AIFS, when another AC
in the same STA is busy or the last TXOPlimit of the AC is too
close in time. As soon as the Backoff process is started, the AC
updates an internal variable, called Backoff Timer (BT). When
updating,
the value of the BT is extracted
randomly
in the
AC
AC
AC
− 1 . CWmin
, 2k CWmin
and
set 0, 1, . . . , min CWmax
AC
are called minimum and the maximum Contention
CWmax
Window and are advertised by the AP in the set of QoS
parameters. k is the number of collisions occurred to the
current frame. An AC with BT equal to zero is allowed to
attempt a transmission in the first slot time following an idle
AIFS or an empty slot time. The BT is decremented in each
slot time following an AIFS or an empty slot time.
III. R ELATED W ORK
EDCA provides less predictable performance than a
reservation-based method and it also suffers form network congestion. When the traffic load increases, EDCA cannot provide
any QoS guarantees. Low reliability of QoS guarantees, starvation of low priority traffic and unbalanced uplink/downlink
access opportunities are the most serious issues to be tackled
for the wide deployment of distributed access mechanisms
like EDCA in realistic scenarios. For these reasons, it can
be argued that support for service differentiation in WLANs
cannot be achieved without solving the relevant issue of
admission control [14]. The IEEE 802.11e standard itself
suggests a distributed admission control algorithm in which
the AP can control the traffic load from each AC as well as
any STA by periodically announcing the available bandwidth
for each AC. This algorithm, however, is rather complex,
difficult to implement and it has received scarce attention
from both the research and the industrial communities. An
alternative to a rigid mechanism of admission control and
bandwidth allocation among ACs is the adaptation at run-time
of 802.11e parameters, with the aim of optimizing the channel
performance depending on network load and applications. In
61
any case, admission control must be performed for traffic flows
explicitely requesting QoS guarantees.
Much work has already been devoted to characterize the
real time applications support of IEEE 802.11b/g networks.
The peculiarity of analytical models for the voice capacity
is that the stations competing for the wireless channel are
not necessarily saturated (with the possible exception of the
AP). This feature makes necessary to extend classical models
for saturated sources [6]. The voice quality and capacity of
WLANs in the presence of background data traffic has been
measured in [3] using a test bed consisting of commercially
available components. Because experimental results cannot
fully reveal the relationship between voice capacity and system
parameters when new wireless technologies or voice codecs
are employed, the voice capacity of IEEE 802.11b WLANs has
been theoretically estimated in [13], [17]. The voice capacity
estimated in these works may be overly optimistic due to
these simplified assumptions. A more precise analytical model
is presented in [7] for 802.11a/b WLANs. [18] presents one
of the first proposals of an admission control mechanism for
802.11e with RT and BE traffic. This proposal has limited applicability due to the use of saturated transmission probability
and some simplified assumptions on the use of the TXOPlimit
parameter.
Measurement-based approaches (see [15], [20], [21]) use
estimates of the available utilization time on the wireless
channel to take decisions about the admission of new flows.
A common limit of these approaches is that the increase in
the collision time due to the admission of a new flow is not
linear with the number of flows and it is difficult to estimate
without an analytical model.
Analyical models for EDCA in saturated conditions (see
[4], [16], [19]) are not useful for VoIP CAC. EDCA models
for non-saturated conditions have been recently proposed in
[5], [9], [12]. The last two approaches are computationally
heavy and do not model the sending queue dynamics, thus
they cannot take into account the impact of the TXOPlimit
parameter on the VoIP capacity of the EDCA network, that can
be relevant. [5] considers both BE traffic and queue dynamics,
thus it can be used for VoIP CAC; however, we present a
simpler approach. Indeed, the proposed approach separates the
problem of predicting the behavior of each station and the
problem of evaluating the congestion of the wireless channel.
This translates in a general model working under all load
conditions and whose complexity grows linearly with the
number of distinct real-time flows.
IV. A M ODEL FOR THE A DMISSION C ONTROL OF VO IP
CALLS
In this section we present a model to predict the behavior of
a WLAN and to establish whether or not a given configuration
of VoIP calls can be admitted without quality degradation. An
interesting aspect of the proposed model is that it decouples
the problems of estimating the channel and stations status. This
simplifies its applicability in realistic scenarios, where sources
may differ completely in terms of offered load, packet rates
and packet size characteristics.
λout = λs 1 − Pdrop − pR+1
(1)
λout = nt τ (1 − p)
(2)
and
A. Model Description
Consider a WLAN composed of Nsta VoIP stations, each
station requesting a symmetric VoIP communication with
nearly constant packet sizes at λt packets-per-second (pps).
Let assume to know Tslot , the average duration of a Time
Slot, and P e,the probability that a given Time Slot is empty.
Knowing those parameters we can determine independently
for each station its working point.
From an user point of view, each transmitting station can
be modeled using a time domain queue model, having its own
TXOPs’ service rate, µt . Since the slot progressing nature of
EDCA, we prefer to model the queue evolution in a different
time scale: the Time Slot domain. In that time domain, λs =
λt Tslot is the arrival rate expressed in packets per Time Slot,
while 1/µs is the average number of Time Slots spent for each
successful TXOP and for each frame discarded for too many
retransmission attempts.
The queue evolution can be easily determined using the
model of Fig. 1, where l is the maximum number of
frames sent in each successful TXOP. l can be computed as
TXOPlimit /Tps , where Tps is the average time spent to send
a single frame. Assuming to know the probability of collision
that the queue experiences when attempting a transmission,
the service delay can be approximated1 as
Eq.s 1 and 2 can be used to express τ , the probability of
attempting a transmission as a function of p:
λs (1 − Pdrop ) − pR+1
nt (1 − p)
Eventually, that value of τ can be used to compute back p
using P e:
Pe
P e = (1 − p) (1 − τ ) → p = 1 −
1−τ
Hence, fixed Tslot and P e we can solve for each station the
above implicit system, determining p, τ and Pdrop . Moreover,
from the values obtained for each station, we can compute
back P e and Tslot , using the following relations:
τ=
P enew =
and
Tslot, new = P enew σ +
N
sta
T ci P ci
i=2
N
sta
(1 − τj )
j=1, j=i
where w (k) is the average number of Time Slots spent in the
Backoff process for serving a TXOP experiencing k collisions,
while R is the maximum number of retransmission attempts.
w (k) can be computed as
k
min 2j CWmin , CWmax − 1
w (k) =
2
j=0
Knowing λs and µs , the model of Fig. 1 can be easily
solved assuming an exponential behavior of service times and
assuming Poisson sources. From this model we can obtain,
hence, the average number of frame sent in each successful
TXOP as
Pk
T si P si +
where
P si = τi
k=0
k=1
N
sta
i=1
R
Nq
(1 − τi )
i=1
1
=
(1 − p)pk (1 + k + w (k)) + pR+1 (1 + k + w (k))
µs
1
nt = Nq
N
sta
min {k, l} Pk
and

P ci = τi 1 −
j<i

(1 − τj ) 
(1 − τj )
j>i
In the expression of Tslot we assume that the stations are
ordered such that i > j if Tps, i ≥ Tps, j . The values of Ts, i ,
the average duration of a successful Time Slot of station i, and
Tc, i , the average duration of a collision Time Slot involving
station i and not involving station j with j < i, can be
computed using the expressions defined in Table I. Tdata is the
time needed to transmit the MAC payload over the wireless
channel.
Hence, starting from the parameters [Tslot , P e] we can
compute back the same parameters, defining the function:
[T snew , P enew ] = F ([Tslot , P e])
k=1
where, Pi is the probability of having i packets waiting for
transmission in the queue. Moreover the model allows us to
determine the probability that a packet is dropped for queue
overflow as Pdrop = PN , where N is the maximum queue
depth.
Knowing Pdrop and nt we can write the following expressions for λout , the rate of successfully served frames:
1 We assume that the station always uses the post-Backoff. This is obviously
a worst case approximation.
62
The working points of the system are all the pairs [Tslot , P e]
that are solutions of the following equation:
[Tslot , P e] − F ([Tslot , P e]) = 0
This equation may have one or multiple stable solutions.
We have only one solution when each station has only a
physically valid working point, i.e. each station is saturated or
non-saturated. When the channel starts becoming congested
for a subset or for all the stations, we have multi-stability,
which translates into multiple solutions to the problem above.
λs
λs
0
1
λs
2
µs
µs
λs
λs
3
l
µs
µs
Fig. 1.
λs
l+1
µs
Nq
µs
VO AC Queue Model.
TABLE I
C HANNEL PARAMETERS
Parameter
σ
Tsif s
Tc
T sap
Value
20µs
10µs
Taif s + Tsif s + Tdata + Tsif s + TackT imeout
Taif s + nt,ap Tsif s + Tdata + Tsif s + Tack
T ssta
Taif s
Tdata
Tack
Taif s + nt,sta Tsif s + Tdata + Tsif s + Tack
AIF S · σ
Tplcp + ThdrM AC + TpayloadM AC
Tplcp + TackM AC
λs
B. Admission Control
The model defined above has been used to derive a simple
admission control rule for deciding whether to admit or not
a given number of VoIP sources in a network. Alg. 1 bases
its choice on the status of the AP, which is the bottleneck
of the system. If the AP is saturated or is not saturated but
experiences high packet losses, the configuration is assumed
overloading the network and the particular number of stations
can not be admitted.
Algorithm 1 Admission Control Check
ap
1: decision AcceptNewConfiguration(λt , λsta
t )
2: compute [Tslot , P e] = F ([Tslot , P e])
3: if AP([Tslot , P e]) is congested then
4:
RETURN do not accept
5: else
6:
RETURN accept
7: end if
as a router between the wireless and wired portion of the testbed, to keep separate the two networks at the IP layer. Real
time traffic that we consider is symmetric, i.e. is composed
by two identical flows, one from the mobile station to a host
on the wired portion of the network and the other one in the
opposite direction. All the flows are generated through a traffic
generator. We used mgen for higher bit rates. Since mgen has
a packet size limit of 24 bytes at the application level, we
wrote a simple RTP traffic generator for codec with smaller
payload sizes.
The admission limit for VoIP sessions is obtained by comparing the loss ratio and total end-to-end delay with some
thresholds. The loss ratio can be easily deduced from the
sequence number contained in the RTP header. A precise
evaluation of the total delay is more difficult, as it requires a
synchronization of the clocks between the two endpoints. To
overcome this difficulty, we have devised a schema, where the
endpoints of each real-time flow coincide. In the experimental
set-up, each STA has two interfaces, one connected to the
wired network and the other to the wireless network through
the AP. A VoIP session is composed by two flows, one
outgoing from the wireless interface to the AP, then on the
wired link end eventually to the Ethernet card of the same STA.
The second flow follows the reverse path. This setup eliminates
the need for cumbersome synchronization procedures and
makes it possible a very precise measurement of the total endto-end delay. The wired network is composed by 100 Mbit
Ethernet links connected through switches. Its contribution to
the total delay is negligible, thus we obtain a good estimate
of the delay generated by the access network, both in the
downlink direction (from the AP to the STA) and in uplink
(from the STA to the AP).
VI. M ODEL VALIDATION
V. E XPERIMENTAL S ETUP
To validate our model we implemented an integrated wireless/wireline IP network, where the access network is a
IEEE 802.11e infrastructure WLAN that serves wireless hosts
(STAs). IEEE 802.11b is used at the physical layer. Both
AP and STAs have been realized using laptops. The AP was
a Compaq EvoN800v and the STAs were HP tc4200, both
running Linux Ubuntu 2.6.15-26-386, equipped with NetGear
WG511T wireless NICs and using the MadWifi v.0.9.2.1
driver. In the test-bed, each active STA sends its traffic through
the IEEE 802.11e VO access category. The AP is configured
63
In this section we compare the admission boundaries generated by the analytical model described previously with simulation and experimental results. This comparison is not straightforward, for reasons that we explain below. Interestingly, there
are discrepancies between simulation and experimental results,
whose cause can be traced back to physical diversity issues.
We have considered a few codecs and we report results
about four, namely two VoIP codecs (ITU G.711 with a fixed
bit rate of 64 kbps, and G.729 with a bit rate of 8 kbps), and
two videoconferencing codecs at higher bit rates (200 and 300
kbps). For VoIP codecs we consider packetization intervals in
TABLE II
MAC PARAMETERS
PHYSICAL AND
Parameter
CWmin
CWmax
AIF S
TXOPlimit
Value
8
16
2
3264µs
Parameter
R
Tplcp
ThdrM AC
TackM AC
Value
7
120µs
58.18µs
56µs
the range of 10-50 ms (corresponding to 100-20 packets per
second, pps). For videoconference the packetization range is
restricted to 10-25 ms (100-40 pps) so that the MTU is not
exceeded.
Simulation has been restricted to the behavior of the wireless part of the access network. We used ns2 , with standard
extensions for 802.11e 2 , and IEEE 802.11b link parameters
set as in Table II. In ns2 we used sources with constant
deterministic packet inter-departure intervals. Notice that the
analytical model uses instead an exponential process for the
arrival of the packets at the queues.
The duration of each flow was set at 180 s, both in the
simulations and in the experiments. The admission boundary
was determined (again for both simulations and experiments)
by requiring a loss rate less than 1% and an end-to-end delay of
less than 50 ms. Notice that in our setting losses and delays are
essentially due to the queue on the AP. The trade-off between
losses and delay is controlled by the AP queue size. We used
a queue size of 50 and with this value the admissibility is
always decided by the loss rate, i.e. by packets discarded at
the AP queue.
The admission results shown in Fig. 2 were obtained by increasing the number of STAs for a given codec and pps value.
Due to the limitation in the number of available machines
we were not able to perform experiments with more than 16
STAs. Experiments were repeated 3 times when the number
of STAs was non critical, and 5 times at the boundary of the
admission region. There were four cases (one for G.729 and
three for G.711) in which the admission conditions were met
in about half of the experiments. In these cases we decided to
classify these configurations as non admissible, thus the data
in the plots met the admission criteria along all the repetitions.
1) Impact of the TXOPlimit parameter: The first remark is
that 802.11e outperforms 802.11b with respect to the number
of admissible STAs. A comparison with the admission limits
of 802.11 VoIP traffic reported in [7] (obtained through simulations and analytical model), shows for G.711 an improvement
of 4 and 10 STAs at, respectively, 100 and 20 pps. This marked
improvement is essentially due to the TXOPlimit parameter,
whose impact has been often underestimated in previous works
about 802.11e performance. In the case of symmetric traffic,
TXOPlimit has an intrinsic prioritization effect since a station
with more packets in its sending queue (the AP in our case)
can transmit for period longer than stations that have only one
2 [Online].
Available:
ieee80211e-ns2/
http://sourceforge.net/projects/
64
packet waiting. Therefore, although the number of accesses
to the wireless channel is evenly distributed, the AP gains a
larger goodput. Notice that this happens when the TXOPlimit
is the same on the AP and the STAs. The first paper to
propose the use of the TXOPlimit parameter to mitigate the AP
bottleneck problem is, to our knowledge, [10]. Unfortunately,
their analytical model does not capture the dynamic of the
sending queue, which ultimately decides how many packets
can be actually sent during the TXOPlimit . More work is
however needed to determine whether setting a TXOPlimit
on the AP larger than that of STAs can improve the voice
capacity of a 802.11e cell.
2) Impact of physical diversity: Experimental results reported in Fig. 2 show that actual voice capacity of the
802.11e wireless cell is sometimes higher than predicted by
both simulation and model. Predictions based on theory and
simulation understimate by one or even 2 STAs the actual
admission limit. Our conclusion is that this effect is due to
physical layer diversity, that, as highlighted in [8], plays a
significant role in mitigating EDCA thhroughput degradation
under heavy contention. EDCA exploits this physical layer
channel diversity when the collision between two frames
of different strength results in a successful decoding of the
dominant frame due to signal strength differential. This has
two effects, namely: (i) the actual collision probability is lower
than expected, and (ii) stations with the weaker signal attemp
to transmit less frequently due to exponential backoff. When
the wireless channel is in the critical boundary zone, even a
slight improvement in the collision probability can affect the
performance of the cell.
3) Validity of the model: Since it is difficult to eliminate
physical diversity, experimental results must be interpreted
with caution. Fig. 2 shows that the simulation limit on STAs
does not exceed the boundary determined experimentally. The
model slightly understimates the admission limit at higher
pps, whereas for lower pps and low bit rates the model
admission limit is one or two STAs more than simulations.
In only one case the model exceeds the experimental limit
(300 kbps, 40 pps). This allows us to conclude that the model
capture the essence of the cell behavior for non saturated
symmetric traffic, and it is therefore useful for practical
purposes. However, the model accuracy can be improved in
two respects: (i) introducing a post-Backoff probability for
packets that arrive when the sending queue is empty (the
current model approximation is that this probability is 1); (ii)
using non Poisson sources. For example, deterministic sources
are more appropriate in the test bed setting, due to the low jitter
on the wired part of the path.
4) Increasing the number of VoIP flows: The admission
region for VoIP flows shows a strong dependence on the
number of the packets per second generated by the source. For
codecs with low bit rate the number of VoIP flows that can be
admitted is more than doubled when the packetization interval
is doubled (halving the pps number). Since the additional
VoIP capacity is relevant, this property can be exploited in
practice by using adaptive approaches on the packetization
Fig. 2.
Admission boundaries for VoIP and videconferencing
interval or on the packet size that can be implemented both
at the application or at the network layer. Additional studies
are required to characterize the increase in the end-to-end
delay generated in this case, but the reduction in the channel
contention would probably have a positive impact on the
overall performance of the WLAN.
VII. C ONCLUSION
In this paper we propose an analytical model for EDCA
WLANs. The proposed analysis models the main characteristics of VoIP scenarios, including both parameters configuration
and unbalanced uplink/downlink traffic flows. Moreover, while
other models make simplistic assumptions on the use of the
TXOPlimit parameter, our analysis models its real dynamic
impact. Another advantage of our analysis is the decoupling
of channel and stations analysis, leading to a more modular
and tractable analysis in the case of complex configurations.
Using the model we defined a simple Admission Control
algorithm that has been used to validate our model contrasting
the results it provides with those obtained using simulations
and experiments. In this comparison, the model shows its
ability to provide meaningful results, defining a lower bound
for the Admission Control.
ACKNOWLEDGMENT
This work is partly funded by the 6th Framework Program,
Information Society Technologies, under Contracts no. FP6IST-038423 (CONTENT), no. FP6-IST-034819 (ONELAB)
and no. FP6-IST-033516 (NETQOS).
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