IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 57, NO. 2, MARCH 2008
1077
Enhanced Per-Flow Admission Control and QoS
Provisioning in IEEE 802.11e Wireless LANs
Chadi M. Assi, Member, IEEE, Anjali Agarwal, Senior Member, IEEE, and Yi Liu, Student Member, IEEE
Abstract—The emerging IEEE 802.11e standard is expected to
provide service differentiation and resource allocation for realtime traffic. To support the transmission of voice and multimedia
data with performance guarantees, it is crucial to design efficient
algorithms for admission control and resource allocation (particularly under heavy load), and several methods have been proposed
to date. However, most of these proposed methods may not be
efficient, because they assign channel access parameters (CAPs)
according to the access category into which a flow is mapped
rather than the absolute quality-of-service (QoS) requirements
of the flow. Using simulations, we highlight the shortcomings of
current admission control methods, and accordingly, we propose
a flow-based service differentiation mechanism. We introduce the
proper “per-flow” selection of CAPs based on the QoS requirements of traffic flows. Furthermore, the new flow-based admission
can control and adjust CAPs with the channel condition variations. We validate our reasoning through extensive simulations
and a thorough comparison with other schemes.
Index Terms—Admission control, IEEE 802.11e, wireless
networks.
I. I NTRODUCTION
T
HE INCREASING popularity of wireless local area networks (WLANs) based on the IEEE 802.11 [1] technology
is pushing operators to deploy WLANs as the access technology for their services, including those with various qualityof-service (QoS) requirements. To support QoS in the access
network, one needs, in addition to service differentiation, an
admission control algorithm, which makes the decision on
whether to admit a real-time stream based on its requirements
and the channel usage condition [7]. Moreover, an effective
resource allocation algorithm is also required to decide which
stream and what frame in that stream should be transmitted,
such that the required QoS can be guaranteed [7].
Two medium access control (MAC) functions have been
defined for the IEEE 802.11. The first is a contention-based
channel access function called distributed coordination function (DCF) that uses a carrier sense multiple access with
Manuscript received May 4, 2006; revised January 23, 2007, May 18, 2007,
and June 16, 2007. The review of this paper was coordinated by Prof. T. Hou.
C. M. Assi is with the Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC H3G 1M8, Canada (e-mail:
assi@ciise.concordia.ca).
A. Agarwal is with the Department of Electrical and Computer Engineering,
Concordia University, Montreal, QC H3G 1M8, Canada (e-mail: aagarwal@
ece.concordia.ca).
Y. Liu is with the Nortel, Ottawa, ON K2H 8E9, Canada (e-mail: y_liu49@
ece.concordia.ca).
Color versions of one or more of the figures in this paper are available online
at http://ieeexplore.ieee.org.
Digital Object Identifier 10.1109/TVT.2007.906368
collision avoidance and binary exponential backoff procedure
to decrease the probability of collisions. The second is a
controlled channel access function called point coordination
function (PCF) that uses a polling scheme to provide medium
access. PCF is an optional function due to its complexity and
inefficiency for normal data transmissions, even though it has
some limited QoS support [5].
DCF is currently unsuitable for multimedia applications
(e.g., voice over IP, video conferencing, etc.) [3], [4]. Under
DCF, a station might have to wait an arbitrarily long time to
transmit a frame, and accordingly, real-time applications may
suffer. That is, with DCF, all stations compete for the channel
with the same priority; there is no specified differentiation
mechanism to provide differential access [6] for different traffic
classes. Various priority schemes have been designed for DCF
[4], [6] to provide QoS support. To provide MAC-level QoS
support, the IEEE 802.11 Working Group has proposed a new
standard, i.e., the IEEE 802.11e [2]. A hybrid coordination
function (HCF) is proposed, in which two medium access
mechanisms are further developed: 1) a contention-based channel access, referred to as enhanced distributed channel access
(EDCA), and 2) centrally controlled channel access, referred
to as HCF controlled channel access (HCCA). The EDCA
works with four access categories (ACs), and each AC achieves
a differential channel access. This differentiation is achieved
through varying the amount of time that a station would sense
the channel to be idle and the length of the contention window
for a backoff. In particular, EDCA supports eight different
priorities, which are further mapped into the four ACs, and the
different ACs are assigned different channel access parameters
(CAPs) to provide service differentiation. It is assumed here
that a payload from a higher layer is labeled with a priority
value. The different CAPs include a different arbitration interframe space AIF S, a different initial contention window
size CWmin , and a different maximum contention window
size CWmax . For the ACi (i = 0, . . . , 3), the initial backoff
window size is CWmin [i], the maximum backoff window size is
CWmax [i], and the arbitration interframe space is AIF S[i]. For
0 ≤ i < j < 3, we have CWmin [i] ≥ CWmin [j], CWmax [i] ≥
CWmax [j], and AIF S[i] ≥ AIF S[j], and at least one of the
inequalities must be “not equal to” [8]. In other words, the
EDCA employs AIF S[i], CWmin [i], and CWmax [i], instead
of DIFS, CWmin , and CWmax , respectively. Accordingly, if
one AC has a smaller AIF S, CWmin , or CWmax , the traffic
of this particular stream will have a better chance to access
the wireless channel. Finally, these parameters are announced
by the access point (AP) via periodically transmitted beacon
frames. The AP can also adaptively adjust these parameters
0018-9545/$25.00 © 2008 IEEE
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based on the network condition. Resource reservation is another
major improvement for 802.11e. It is achieved by allocating a
transmission opportunity (TXOP) to different ACs, and each
TXOP is obtained in the beacon frames from the AP. A TXOP
is a time period wherein a station has the right to access the
channel and is denoted by a starting time and a maximum
duration of transmission. A station can get a TXOP either
through EDCA or HCCA. When a station has the right to access
the channel, it is allowed to transmit more than one packet
from a particular AC during its TXOP period. Additionally, the
concept of surplus bandwidth allowance (SBA) is introduced in
the new standard to compensate for the traffic loss caused by
packet error and collision and to meet the traffic throughput
requirements. SBA is a ratio representing the total allocated
time period over the time period stated by the application
and required for a successful transmission. However, how to
obtain the precise value of SBA has not been specified in
IEEE 802.11e yet.
Although both EDCA and HCCA can provide limited service
differentiation and certain resource reservation through the
new MAC functions, they both cannot provide any guaranteed
QoS. In this paper, we propose an admission control policy
wherein the control parameters [such as retry limit and maximum tolerable collision rate (CR)] are derived from the QoS
requirements specified by higher layers. Furthermore, the CAPs
for each stream are dynamically incremented or decremented
based on the measurement of the current channel conditions.
The rest of this paper is organized as follows: In Section II, we
present some background and the related work on admission
control and motivate our research. In Section III, we analyze
the system parameters and derive appropriate parameters. The
admission control and congestion control systems are presented
in Section IV. In Section V, we evaluate the performance of the
proposed schemes, and finally, we conclude in Section VI.
II. B ACKGROUND AND M OTIVATIONS
A. Background Information
Several admission schemes for WLANs have been proposed [7]–[12]. Although the methods of these mechanisms
are different, they all share the same fundamental principle.
Through providing more precise service differentiation and
more accurate resource reservation, these mechanisms intend
to achieve two goals: 1) Maximize the usage of the wireless
medium resources, and 2) efficiently admit new traffic while
not compromising the QoS level of admitted traffic. In general, admission control schemes can be classified into two
categories: 1) measurement-based [7]–[10] and 2) model-based
[11], [12] schemes. Measurement-based schemes make admission decisions based on measured network conditions such as
throughput and delay, whereas model-based schemes rely on
certain performance metrics to evaluate the network status.
For example, Xiao and Li [8] proposed a measurement-based
distributed admission control (DAC). In DAC, the AP measures
the amount of time occupied by the transmission of each AC
during each beacon interval. Based on this, the AP announces
a TXOP budget at the end of each beacon interval, which is
the partial available bandwidth to each AC. Each station further
determines an internal transmission limit per AC for each beacon interval based on the successfully used transmission time
during the previous beacon period and the transmission budget
announced from the AP. When the AP transmission budget is
lower than a certain level, new flows cannot be admitted. The
drawback of DAC [13] is its lack of direct relationships between
the TXOP parameters and the QoS requirements from the application. A threshold-based admission control is proposed in [11],
where each station measures either the relative occupied bandwidth or the channel average collision and compares them with
predefined thresholds to make admission decisions. Although
the system is easy to implement, it is difficult to select the
threshold values. Other measurement-based admission controls
such as HARMONICA [10] and virtual MAC and virtual source
algorithms are also proposed [13], [17]. Alternatively, Pong and
Moors [12] proposed a simple admission control wherein the
AP estimates the throughput that existing flows would achieve
if a new flow with certain parameters was admitted. The AP
has the responsibility of collision monitoring and throughput
estimation, and the algorithm indicates whether a new flow can
be admitted while preserving the QoS of existing flows.
Recently, a distributed airtime allocation and admission
control to support the transmission of multimedia data with
performance guarantees is presented in [7]. Here, admission
decision is made according to both the admission policies
and QoS requirements supplied by the application layer itself.
However, the authors noted that one of the biggest challenges
in providing parameterized QoS is that a quantitative control
over the stations’ medium occupancy (airtime usage) cannot
be achieved via the current EDCA. Furthermore, the airtime
control becomes more difficult due to the link adaptation in
IEEE 802.11, which allows stations to vary their transmission
rates based on the link condition. Their admission relies on an
effective airtime (EA) ratio parameter, which is the percentage
of airtime allocated to wireless stations. This EA parameter
is not easily specified and is only obtained from simulation
results. For example, if the EA parameter is selected to be too
small, then the channel utilization is degraded, and if it is too
large, then the QoS of existing and newly admitted flows cannot be guaranteed. In summary, measurement-based admission
control schemes rely mainly on measured channel conditions
to adjust the CAPs assigned for each AC. The admission
decision for a new stream is based on the measured available
system resources for the AC that this stream belongs to. Modelbased schemes mainly use either predefined criteria values to
compare against the measured values or an analytical model
to calculate achievable throughput. The admission decision for
a new stream is based on the comparison of those predefined
values with the achievable throughput and/or service rate.
B. Motivations
Although EDCA differentiates between traffic belonging to
different access categories by assigning them different access
parameters, EDCA and current admission control systems cannot differentiate between streams belonging to the same AC but
have different QoS requirements. Without such differentiation
ASSI et al.: PER-FLOW ADMISSION CONTROL AND QoS PROVISIONING IN IEEE 802.11e WIRELESS LANs
Fig. 1.
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Voice traffic CR. (a) Voice Traffic A (128 Kbps). (b) Voice Traffic B (64 Kbps). (c) Voice Traffic C (16 Kbps).
and a direct relation with the traffic QoS requirements, admission control schemes do not perform well. For example, in [8],
the system parameters are specified for each AC; an important
parameter, i.e., surplus factor, is used to compensate for the
traffic loss caused by packet collisions or channel error. The
value of the surplus factor is chosen for each AC and does not
depend on the QoS of individual streams inside the same AC.
To elaborate, consider that three voice traffic (A, B, and C) with
the same payload size of 80 B but with different data rates (128,
64, and 16 kb/s, respectively) are requesting admission. With
the DAC scheme, if these voice requests are admitted, they
will be mapped into the same AC and will therefore transmit
their packets using the same CAP set. Fig. 1 (the simulation
results presented in this figure correspond to the traffic pattern
presented in Section V-A) shows that, when all voice traffic are
using the same CAP set, the CR that these streams encounter
will be different, because their data rates are different. The
lower rate voice stream suffers a higher collision than the higher
rate stream, because collisions are mainly caused by the packets
from the same AC. With DAC, these three requests will be
assigned the same SBA value. To guarantee the throughput
requirement of each request, this surplus factor will be assigned
a relatively larger value for all the voice streams to compensate
for the throughput loss of the lower rate stream (since it
suffers more collisions). Hence, some of the medium resource
is wasted in the process. It is clear that, to improve the channel
utilization, these different requests should be assigned different
SBA values according to their specific CRs. Therefore, SBA
values should be chosen based on the traffic QoS requirements
instead of the AC QoS requirements. The same argument
applies to traffic delay and dropping requirements. The delay
requirement, for example, from different types of voice traffic
may be different. Under the currently proposed schemes, this
differential treatment of packets belonging to the same AC but
for different traffic streams does not exist. In WLANs, to design
a good admission control scheme, the following rules should
be followed: First, the thresholds used in the admission control
policy should be obtained based on flow QoS requirements.
Second, the CAPs should relate to the QoS requirements of a
particular stream and not to the particular AC where it belongs.
Third, the CAPs should be dynamically adjusted based on the
channel conditions.
III. A NALYSIS OF S YSTEM P ARAMETERS
The QoS requirements for real-time applications are mainly
throughput, delay, jitter, and dropping rate. In IEEE 802.11e
WLANs, these characteristics are either included in the Traffic
Specifications (TSPEC) table [4] or specified by the application. When the admission control unit (ACU) receives a traffic
request, followed by its QoS requirements, it should decide
whether this traffic should be admitted or not based on a
predefined admission control policy. By combining the TSPEC
table and traffic QoS requirements with the channel condition,
the ACU achieves two objectives: 1) Obtain variable threshold
values, which can be used in the admission control policy, and
2) obtain variable CAPs, which can be used to optimally utilize
the medium resources. Next, we explain how the ACU achieves
these two goals through a differential access for traffic with
different QoS requirements.
A. Per-Flow Delay Requirement versus Retry Limit
Real-time traffic such as voice and video streams have strict
delay requirements; if packets of real-time streams are not
delivered within their delay requirements, they are considered
to be lost. In WLANs, when a packet is sent from its corresponding application, the packet is first placed in a transmission
queue, and once it reaches the head of the queue, it starts contending for channel access until it is successfully transmitted
or ultimately dropped. The packet delay caused by waiting in
the transmission queue is referred to as the queuing delay, and
the delay associated with the medium access is referred to as the
medium access delay. Engelstad and Østerbø [18] and Vassis
and Kormentzas [19] analyzed the total delay for a system under
finite load conditions. The mean access delay, for both saturated
and nonsaturated networks, for priority class i is estimated
as [18]
dai
= 1−
piRi +1
pi
Ts + Tc
1 − pi
+
Ri
dsi pj (Wij − 1)
2 j=0 i
(1)
where Ri is the retry limit for priority class i, Wij denotes the
contention window size in the jth backoff stage for stream i
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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 57, NO. 2, MARCH 2008
(e.g., Wij = 2j CWmin,i , CWmin,i is the initial backoff window size of priority class i), and Ts and Tc are the duration
of a slot containing a successful transmission and a collision,
respectively. dsi is the countdown delay given by
ps
pi
ps
s
Ts + 1 −
(2)
Tc
di = σ +
pb
pb
1 − pi
where σ is the duration of an idle slot, and pi is the probability
of unsuccessful transmission when a station is transmitting traffic of more than one AC (i.e., virtual collision among queues),
which is given by
pi = 1 − i
1 − pb
c=0 (1
− τc )
(3)
and pb denotes the probability that the channel is sensed to be
busy, i.e.,
pb = 1 −
N
−1
(1 − τi )ni
(4)
a certain approximation may be needed; we first note that the
retry limit is bounded, i.e.,
0 ≤ Ri ≤ Rimax .
For instance, Ri = 0 corresponds to the case where the queuing
delay is very high and the system cannot guarantee the delay
requirement of the arriving stream. Here, a packet is not allowed
any retransmission, and it is dropped upon the first collision.
Alternatively, if the queuing delay is negligible (e.g., when the
load is light), then Ri converges toward its limit Rimax , for
which an exact expression may be derived. Note that, in the
latter case, the system is considered to be nonsaturated, and
the mean access delay is given by (1); Rimax can be derived
by setting dai = Di . Clearly, it is still not simple to obtain
an expression for Rimax . Therefore, we analyze the network
parameters under saturated network conditions. First, note that
a packet is dropped when it reaches the last backoff stage and
experiences another collision; therefore, the average time to
drop a packet is estimated [14] as
i=0
max
where N is the total number of priority classes, ni is the number
of stations in priority class i, and τi denotes the transmission
probability. ps is the probability that a time slot contains a
successful transmission, i.e.,
ps =
N
−1
ni τi (1 − pi ).
(5)
i=0
To derive the mean queuing delay dqi , Engelstad and Østerbø
[18] considered the medium access delay as the service time
for a packet in a single server queue (M/G/1), i.e.,
a(2)
λi di − dai
(6)
dqi =
2(1 − ρi )
where λi is the packet arrival rate for AC i; ρi is the utilization
a(2)
factor, i.e., ρi = min(1, λi dai ); and di
is the second-order
moment of the access delay. Then, the mean total delay that
a packet from AC i experiences is simply
dti
=
dai
+
dqi .
(7)
Clearly, in the expression of the total delay, the two parameters
CWmin,i and Ri can be used to control the delay. To satisfy
the traffic delay requirements (Di is the delay bound specified
in the TSPEC table, dti ≤ Di ), we can either choose a fixed Ri
and dynamically adjust the value of CWmin,i or select a fixed
CWmin,i to assign different retry limits to the traffic. In our
scheme, we choose a relatively fixed CWmin,i and calculate Ri
for a traffic stream to meet its delay requirements. We choose,
however, to vary CWmin,i in the congestion control policy
instead. To determine the retry limit that satisfies the delay
requirement Di for the arriving stream (whose arrival rate λi
is assumed to be given), one can solve for Ri when dti = Di .
Clearly, it is not easy to solve for an exact expression for Ri ,
and numerical analysis is helpful but not practical. Therefore,
(8)
E[Tdrop ] =
CWmin,i (2Ri
+1
− 1) + (Rimax + 1)
E[slot]
2
(9)
where E[slot] is the average slot time whose value can be
measured by a station. A node periodically keeps track of the
slots (whether an idle slot and a busy slot with a successful or
a failed transmission, i.e., collision), counts their number per
period of measurement (e.g., Ni idle slots, Ns successful slots,
and Nc failed slots), and measures the total idle period (e.g.,
Γi = Ni × σ), the total successful period Γs , and collision
period Γc . E[slot] is then estimated as (Γi + Γs + Γc )/(Ni +
Ns + Nc ). This measurement is adaptively corrected using a
simple moving average scheme. This estimation is performed
for every priority class [e.g., constant bit rate (CBR) or variable
bit rate (VBR)]. By setting E[Tdrop ] = Di , one can solve for
the maximum retry limit
2Di
max
Ri
+1 −1 .
(10)
= log2
CWmin,i E[slot]
It can be shown through simple numerical analysis that, for a
priority class i with delay bound Di , (10) yields a very good
approximation (slightly lower) to that obtained by solving (1).
It is to be noted that, when the load is light, the queuing
delay is relatively negligible (for example, it has been shown
in [18] that only when the traffic per AC [3] (and AC [2]) per
station exceeds 2 Mb/s does the queuing delays start to get
considerably large), and the retry limit is very close to Rimax ;
otherwise, the retry limit is upper bounded by Rimax .
B. Per-Flow Dropping Rate Requirement versus CR
For real-time transmissions, a certain fraction of dropped or
lost packets is tolerable and will appear as noise, but to maintain
an acceptable level of service, this fraction must be less than
a desired level within a certain period of time. Different realtime services can tolerate different packet dropping rates. In
ASSI et al.: PER-FLOW ADMISSION CONTROL AND QoS PROVISIONING IN IEEE 802.11e WIRELESS LANs
IEEE 802.11e, the dropping rate of a traffic flow is not specified
in the TSPEC table, but it may be obtained from the traffic
request and is related to the packet retransmission. For instance,
a packet retransmission is allowed if previous transmission of
the packet has collided or is received with error, and the delay
limit or allowed retry limit has not been reached. When a packet
retransmission limit is reached, retransmission is no longer
allowed, and consequently, the packet should be dropped. The
total dropping rate of a traffic should not exceed its desired
packet dropping rate to maintain the desired QoS requirement.
The probability that a given packet of traffic i is dropped after
Ri times of retransmissions is estimated as follows:
Ri +1
Pidrop = 1 − (1 − Pierror ) 1 − Picol_cur
(11)
where Pidrop is the dropping rate for stream i, and we get its
error
limit value Pidrop
_limit from the application if it is specified. Pi
is the packet error rate that depends on the channel condition
(signal-to-noise ratio, interference, etc.), and its value can be
estimated from the physical layer through acknowledgment
frames or measured. Picol_cur is the current channel CR for
traffic i, whose value can be measured by the station. Since the
value of Picol_cur is always less than or equal to 1, we can use
the value of the retry limit Ri (if known) and the limit value of
dropping rate Pidrop
_limit to obtain the maximum tolerable CR of
the incoming stream, i.e.,
Picol_ max
(Ri +1)
=
error
Pidrop
_limit − Pi
1 − Pierror
.
(12)
When the ACU receives an admission request, the ACU calculates Picol_ max for the arriving stream. If the calculated
Picol_ max of the traffic is less than Picol_cur , this traffic will
encounter a higher packet dropping rate than its desired level.
In other words, the required dropping rate limit cannot be
guaranteed for the stream under the current channel conditions.
Hence, the traffic request should be rejected since admitting it
may only lead to wasting system resources and causing more
collisions. Alternatively, if the maximum tolerable CR of the
stream is higher than that of the channel, then the network
conditions are sufficiently good to admit the flow and guarantee
its QoS requirements. As we mentioned earlier, the retry limit
is bounded; if Ri = 0, the maximum tolerable CR becomes
very small, and the network conditions should be near perfect
for the arriving stream to be admitted. Alternatively, when Ri
converges toward Rimax , there corresponds a larger maximum
CR that a stream can tolerate (Picol_ max is an increasing
function with Ri ); this means that the network is less restrictive in admitting the flow (as long as Picol_ max > Picol_cur ).
Since we do not have an exact expression for the actual retry
limit allowed for a stream to meet its delay requirement, one
can use the maximum retry limit instead to derive Picol_ max .
Accordingly, this yields a slightly aggressive admission control
method, which will provide an upper bound on the stream delay
performance. We therefore make this assumption in the rest of
this paper.
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C. Per-Flow Throughput Requirement versus SBA
To satisfy the throughput requirement of a stream, the ACU
needs to consider two aspects: 1) the traffic throughput requirement specified by the application and 2) the predicted extra
bandwidth needed to compensate for the traffic loss caused
by error or collisions on the channel. For CBR traffic, the
admission decision can be simply made when the system has
enough resources to support the mean data rate of the traffic
stream. For VBR traffic, the admission decision is relatively
difficult to be made. For example, a typical VBR video traffic
can have a peak data rate of 11.75 Mb/s and a mean data rate of
3.0 Mb/s. If the VBR traffic is admitted only when its peak rate
is guaranteed, the system resources cannot be fully utilized. On
the other hand, if we simply admit the VBR traffic as long as
its mean data rate is guaranteed, the throughput of the VBR
traffic can barely be supported. In [7], a dual-token bucket
filter for VBR traffic policing was presented. The traffic can
be shaped and rescheduled as traffic-shaped CBR traffic with
a guaranteed service rate. This guaranteed rate is a function of
the mean data rate, the peak data rate, the delay bound, and the
maximum burst size. We use this method to treat VBR traffic in
our scheme. The reasons of choosing a dual-token bucket filter
in our scheme are given as follows: 1) to increase the channel
utilization and 2) to decrease the oscillation of the CR. The
guaranteed rate for VBR can be derived as follows [7]:
i
Bi = (Pi − Gi ) × L
Pi
(13)
Bi
Di = G
i
where Bi , Pi , Gi , Li , and Di are the bucket size, peak data rate,
guaranteed service rate, maximum burst size, and delay bound
of VBR traffic i, respectively. Since the values of Pi , Li , and
Di can be obtained from the TSPEC table, we can obtain the
value of the guaranteed rate as follows:
Gi =
(Pi × Li )
.
(Di × Pi + Li )
(14)
Now, when the ACU receives a real-time traffic request with
its throughput requirements, the ACU first classifies the traffic
into CBR or VBR. The ACU then calculates a TXOP period,
which will be assigned to the traffic later if the traffic is
admitted, based on either the traffic mean data rate (CBR)
or its guaranteed rate (VBR). This TXOP period includes not
only the required period but also a surplus time period for
retransmissions caused either by packet errors or collisions on
the channel. The procedure of the calculation for this TXOP
period is illustrated next. First, the ACU calculates the traffic
throughput requirement parameter Tirequired given by

ρi
p =
i
(Si ×8) × SI
 T required = g × d + (2 × g − 1) × T + g × T
i
i
i
i
sifs
i
ack
(15)
where SI is the length of the service interval, which meets the
traffic end-to-end delay requirement, and its value is the minimum value of end-to-end delay requirements for all admitted
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streams [2]; ρi is the mean data rate for CBR traffic or the
guaranteed rate for VBR traffic; Si is the nominal packet size
of traffic i; di is the duration of the packet transmission time
based on Si and the PHY data rate used; Tsifs is the a short
interframe space period; gi is the number of packets per SI for
traffic i; and Tack is the duration to transmit an acknowledgment
frame. To include the surplus time period to compensate for the
error or collision, the total assigned TXOP period for traffic i is
computed as follows:
TXOPi = Tirequired × SBAi .
(16)
Now, the ACU decides whether to admit the stream or not
according to the TXOPi period and whether it can be supported
by the system. If the system has enough available resources
to assign TXOPi , the traffic request will be admitted. Note
that this assigned period will also be periodically updated at
the beginning of each beacon interval according to the channel
conditions. Dynamically adjusting this assigned TXOPi period
can maximally utilize the system resources. In (16), since the
value of Tirequired is a statistical constant coming from the
traffic throughput requirement, SBAi becomes the only variable
to influence the value of TXOPi . Therefore, to admit more
flows, we need to keep the total assigned TXOPi for each flow
as small as possible, and hence, it is essential to find the optimal
value of SBAi . Although finding a precise value for SBA has
not been specified by the IEEE 802.11e, it is still a subject of
research. In [8], the SBA is measured by the AP for each AC.
However, in WLANs, the AP does not know how many packets
have collided and from which stations they have originated. In
contrast, the station knows exactly how many packets have been
sent out from its interface and how many packets have been
received from its upper layer, and thus, the difference is the
extra bandwidth that should be compensated for by the SBA.
In our scheme, we use the following procedure to calculate
the optimal SBA value. First, all stations calculate the SBA
value at the end of each beacon interval as follows: SBAji =
j
j
(Ni,transmitted
(n)/Ni,received
(n)), where i is the ID of the flow,
j
j is the ID of the station, and Ni,transmitted
(n) is the total
number of packets transmitted from station j during the nth
j
(n) is the total number of packets
beacon interval. Ni,received
of flow i received from the corresponding application. Note
that packets dropped due to buffer overflow are neglected in
our scheme. Every station reports its SBAji to the AP. When
the AP receives SBAji at the end of the nth beacon interval, it
j
records the cumulative value SBAi = N
j=1 SBAi /N (where
N is the total number of stations with the same flow i) and
broadcasts it with TSPEC table to all stations at the beginning
of the next beacon interval. In our scheme, SBAi is also used
as an indicator for the current channel conditions as well as a
parameter to determine the current CR as follows:
Picol_cur = 1 −
1
.
SBAi
(17)
Note that SBAi actually accounts for packets that are dropped
either due to collision or due to other sources of errors; therefore, (17) assumes that the channel error rate is negligible.
Alternatively, if the frame error rate is known (e.g., using
measurement and estimation methods), then the right-hand side
of (17) would be equal to 1 − (1 − Pierror )(1 − Picol_cur ), and
the collision probability can be determined.
IV. A DMISSION AND C ONGESTION C ONTROL S CHEME
A. Admission Control Policy
The station first classifies the arriving request and then calculates the admission control parameters for the traffic stream
based on its QoS requirements. These parameters include
Picol_ max , Ri , and TXOPi . The station then makes an admission decision whether to reject this new traffic by comparing its
maximum tolerable CR Picol_ max with the current channel CR.
When Picol_ max is less than the current channel CR, the trafficrequired delay bound and dropping rate cannot be satisfied under the current channel conditions, and accordingly, the traffic
request should be rejected. If the request can be admitted at the
station level, the station will further forward this request to the
AP. When the AP receives the request from the station, it further
uses another procedure to decide whether it should accept this
request based on the current available medium resources. The
current available medium resource is the difference between
the SI and the period that is currently utilized or occupied
by currently existing streams. The current medium resources
used by real-time streams can be calculated by the product of
the TXOP requirement of each admitted flow and its current
corresponding SBA. If the available medium resource is larger
than TXOPi (the requirement of the new request computed by
the AP), the traffic will be finally admitted. Otherwise, the
traffic is rejected. To restate, a new flow is admitted if the
following inequality holds:
required
× SBAk+1 +
Tk+1
k Tirequired × SBAi ≤ µ × SI
i=1
(18)
where k is the total number of existing flows, i is the index for
existing flows, and µ is a user-defined utilization target for realtime traffic. The remaining parts of SI (i.e., (1 − µ) × SI) are
used for best effort (BE) traffic. Note that, when the AP receives
a new request and it cannot find its corresponding SBA and
Picol_cur , the AP always chooses the current maximum value
to calculate the admission control parameters for the new flow
to guarantee its QoS requirements. When the flow is finally
admitted by the AP, the AP will assign a set of particular CAPs
and a TXOP period.
Note that BE traffic is always admitted. However, when the
system is overloaded, too many data transmission inside the
channel can eventually degrade the performance of the whole
system. As a result, the QoS of existing real-time flows will
be affected. In our scheme, we use the parameter µ in (18) to
guarantee a minimal portion of SI for BE. When the system
is overloaded, real-time traffic will be rejected due to shortage
of resources. In such a case, the BE traffic will continue to be
admitted and will share the same resources with other existing
BE traffic. As a result, the BE throughput per station will
ASSI et al.: PER-FLOW ADMISSION CONTROL AND QoS PROVISIONING IN IEEE 802.11e WIRELESS LANs
be reduced since the total bandwidth occupied by BE traffic
before and after the system goes into the overload state remains
the same, and accordingly, the QoS level of existing real-time
traffic will not be sacrificed.
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TABLE I
APPLICATION QoS PERFORMANCE TARGETS
B. Congestion Control Policy
In our model, the values of the CAPs are dynamically
assigned to newly admitted streams based on their QoS requirements; these values are periodically updated according to
the current channel condition at the beginning of each beacon
interval. Accordingly, the channel collision probability can
be dramatically decreased, and the channel utilization can be
noticeably improved. This has been previously demonstrated
in many papers [8], [15]. Nevertheless, how to find a proper
index to dynamically adjust these parameters has been a popular research topic. In our scheme, we use SBAi to be such
an indicator, because its value represents the current channel
condition, as we discussed earlier. The AP uses its SBAi value
to assign CAP values to a newly admitted traffic stream of
priority class i:
CWmin ji = CWmin ji (0) × rand(1, SBAi )
(19)
AIF Sij = β × AIF Sij (0) × rand(1, SBAi )
where CWminj (0) and AIF Sij (0) are the default values of
i
traffic class i, and β is a constant used to make AIF Sij fall
within the range specified by the standard. Note that, when
the newly admitted flow cannot find its corresponding SBA,
the AP uses the default values of this priority class to assign
for the stream, which means that the value of SBAi is equal
to one in this case. Now, each station will use the SBAi value
to periodically update CAP values at the beginning of the nth
beacon interval as follows:
CWmin ji (n) = CWmin ji (n−1)×rand (1, 1+∆SBAi (n))
AIF Sij (n) = β ×AIF Sij (n−1)×rand (1, 1+∆SBAi (n))
(20)
where ∆SBAi (n) is the difference between SBAi (n) and
SBAi (n − 1). As more streams are admitted, the network may
get congested, and higher CRs may be observed (e.g., SBAji
constantly higher than 1). Accordingly, the system responds to
this congestion by deploying a multiplicative increase of the
access parameters to reduce the transmission (and, hence, the
contention) on the wireless channel. The selection of the access
parameters is further randomized to ensure that various streams
select different parameters, which will, in turn, yield lower
collisions. On the other hand, when the system becomes less
congested (e.g., SBAi (n) < 0), each station will respond by
reducing the values of its access parameters. We have adopted
the following conservative linear decrease approach (with
δ = 0.5):

 CWmin ji (n) = max CWmin ji (0), δ × CWmin ji (n − 1)
 AIF S j (n) = max AIF S j (0), δ × AIF S j (n − 1) .
i
i
i
(21)
V. P ERFORMANCE E VALUATION
We study the performance of the proposed admission and
congestion control schemes using network simulator NS-2 [16].
We present the simulation performance of the following three
systems: 1) no admission control (N-AC), which follows the
default parameter sets given in IEEE 802.11e; 2) DAC [8],
wherein all the system parameters used in the admission control
policy are class based and congestion control is mainly used for
controlling the BE traffic; and 3) flow-based DAC (F-DAC), in
which the ACU assigns different access parameters based on the
QoS requirements and the congestion control policy is applied
to all traffic streams.
A. Simulation Scenario
We set up our model with fifteen stations and one AP. We
assume that all stations are within transmission range with
a clear channel (no channel error). All traffic streams flow
from the stations to the AP. Each station carries three flows
from three ACs (voice, video, and data). For voice traffic, we
consider three types (A, B, and C), all with 80-B payload size.
For video traffic, three types are considered (A, B, and C)
with the same mean payload size of 500 B. Data traffic are
generated with an exponential on/off distribution, with a mean
data rate of 1024 kb/s, and with a fixed payload size of
1024 B. The QoS performance targets of each traffic types
are shown in Table I. We use the following as initial CAPs
for each AC: AIF S[voice] = 25 µs, CW min[voice] = 15,
AIF S[video] = 25 µs, CW min[video] = 31, AIF S[data] =
34 µs, and CW min[data] = 63. The other system parameters
used in our simulation are given as follows: channel rate of
36 Mb/s, the physical layer convergence procedure header
length of 40 bits, preamble length of 96 bits, SIF S = 16 µs,
slot time = 9 µs, SI = 120 ms, and beacon interval = 2 s.
The traffic profile is given as follows: At the beginning of the
simulation, voice traffic starts, and a new voice traffic arrives
for each 30 s with the sequence of A, B, and C. Video traffic
starts arriving at 10 s, and for each 30 s thereafter, a new video
traffic arrives at the system with the sequence of A, B, and C,
respectively. Data traffic starts at 20 s and arrives at the system
every 30 s until the end of the simulation. The total simulation
time is 450 s. To quantitatively evaluate the performance of
our scheme, the following metrics are used: overall system
throughput, per-flow throughput, packet mean delay, jitter,
and CR.
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Fig. 2. System throughput comparison.
B. Analysis of Simulation Results
1) System Performance: Fig. 2 shows the system throughput comparison for the three schemes. The results show that,
without admission control, the overall system throughput can
only reach 10.783 Mb/s, whereas, with admission control, the
overall system throughput presents noticeable improvement.
For instance, with DAC, the overall system throughput reaches
12.189 Mb/s (i.e., an improvement of 13%), and with F-DAC,
the throughput can be further increased to 13.205 Mb/s (i.e.,
an improvement of 22.46%). We can also see that all schemes
achieve similar throughput performance before 170 s. After
170 s, the system begins to enter the heavy-load state, and
the total throughput begins to vary with different schemes.
The reason for this is that, without any admission control, all
arriving flows are admitted, and therefore, the collision among
packets of different flows will be high. When collision on the
channel is high, the transmission efficiency is reduced due to
multiple failed transmissions and retransmissions, which ultimately impacts the system throughput. With admission control,
the number of admitted flows and, consequently, the collision
are controlled; therefore, we can clearly see the improvement
in the system throughput between DAC, F-DAC, and N-AC.
Moreover, F-DAC outperforms DAC in terms of total system
throughput. This is because F-DAC calculates admission control parameters based on the flow QoS requirements and not
based on requirements of the AC of the flow. For example, SBA
is used to compensate for the packet loss caused by collisions.
In DAC, all flows belonging to the same AC will be assigned the
same SBA, regardless of their QoS requirements, whereas, in
F-DAC, the AP calculates the SBA for each flow; two flows
within the same AC but with different data rates will be
assigned different SBAs, because they experience different
collisions. This will result in better utilization of the bandwidth
resources. Another metric that we used to compare DAC and
F-DAC is the total number of flows admitted. Without admission control, all streams are allowed in the system; with
DAC, ten voice and nine video flows can be admitted, and
with F-DAC, 13 voice and 13 video flows are admitted. This
increase is mainly due to the fact that SBA and other CAPs are
computed/selected according to each flow. In summary, when
admission control is applied, the system throughput can be
improved. However, with per-flow admission control, not only
can the overall system throughput be improved but also the total
number of admitted flows is increased.
2) Per-Flow Throughput: Fig. 3 shows that the per-voicestream throughput can be satisfied before 180 s under the
different models. When the simulation time reaches 180 s
and without admission control, the throughput of voice traffic
begins to degrade as the system load continues to increase and
become unacceptable at the end of the simulation. For example,
for voice A, the throughput degrades to 121 kb/s at the end
of the simulation, which is below the acceptable level (see
Table I). Alternatively, with DAC and F-DAC, the throughput
of voice traffic slightly decreases but remains guaranteed within
its requirement range. However, we can clearly see that DAC
performs slightly better in terms of per-flow throughput than
F-DAC. This is because DAC always selects a larger SBA
value (assigned to the lower rate streams) to compensate for
the loss that all voice traffic experience. Clearly, this will waste
system resources. Alternatively, F-DAC chooses the SBA value
corresponding to each flow type, which means that the channel
bandwidth is more effectively utilized by competing flows, and
therefore, more flows will be admitted. Similar results are also
observed for video traffic throughput.
3) Mean Delay: Fig. 4 shows that, without admission control, when the traffic load gets higher, the average packet delay
for voice traffic (A, B, and C) quickly increases and cannot be
bounded (i.e., in the 5-ms delay requirement). This is because
all arriving streams are admitted, regardless of the channel conditions and the resource availability. On the other hand, when
admission schemes are implemented (DAC or F-DAC), the
average packet delay is controlled according to the admission
policy. We can see that both schemes are capable of bounding
the delays of voice traffic within their delay requirements. Fig. 5
shows the mean delay performance of video traffic under the
three models; the figure shows a similar behavior as seen for
voice traffic. Before 180 s, all the video streams under three different models have almost the same average delay. After 180 s,
although the mean delay of video streams begins to diverge,
their delay requirements can still be satisfied (refer to Table I)
until 400 s. After 400 s and without any admission control,
the mean delay of video traffic is not controlled and does not
satisfy the stream delay requirement. When admission control
is implemented, the mean delays of video traffic can be bounded
inside their desired range. From Fig. 4, we can see that all
voice streams exhibit almost the same mean delay with both
DAC and F-DAC. This is because all voice streams have the
same delay requirement. However, from Fig. 5, the results show
that the different video classes (A, B, and C) exhibit almost
the same mean delay under the DAC scheme while showing
different mean delays with F-DAC. This is mainly because
each traffic class under F-DAC, and unlike the DAC scheme, is
assigned its own retry limit according to its delay requirement.
In summary, our results show that, without admission control,
the delay requirements of real-time traffic cannot be guaranteed
and bounded. On the other hand, when admission control is
applied, the mean delay of all traffic can be controlled. We have
ASSI et al.: PER-FLOW ADMISSION CONTROL AND QoS PROVISIONING IN IEEE 802.11e WIRELESS LANs
Fig. 3.
Voice traffic throughput comparison. (a) Voice Traffic A. (b) Voice Traffic B. (c) Voice Traffic C.
Fig. 4.
Voice traffic delay comparison. (a) Voice Traffic A. (b) Voice Traffic B. (c) Voice Traffic C.
Fig. 5.
Video traffic delay comparison. (a) Video Traffic A. (b) Video Traffic B. (c) Video Traffic C.
Fig. 6.
Voice traffic latency standard deviation with N-AC. (a) Voice Traffic A. (b) Voice Traffic B. (c) Voice Traffic C.
also shown that, by providing a better service differentiation
(i.e., the assignment of different CAPs) among traffic streams
of the same class, we can still guarantee the delay requirements
while admitting more flows in the network and, hence, achieve
better channel utilization.
4) Latency Standard Deviation: We only include the jitter
requirement for the voice traffic (Table I). The jitter performance (under N-AC, DAC, and F-DAC) as the system load
changes is shown in Figs. 6–8, respectively. As can be seen, the
jitter of N-AC is very close to DAC and F-DAC when the traffic
load is light. However, when the traffic load becomes high, the
jitter of N-AC rapidly increases (Fig. 6) and becomes close to
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3 ms, whereas the jitter bound is 1 ms (Table I). This is mainly
because of the unpredictable collisions and congestion on the
wireless channel. On the other hand, with DAC and F-DAC,
the jitter has a much smaller value (with F-DAC slightly better)
and can be controlled. For example, when the system load gets
higher, the jitter under DAC can almost be kept at less than
1 ms, and toward the end of the simulation, the value of the
jitter fluctuates around 1 ms.
5) Throughput of BE Traffic: We choose µ = 0.75, which
means that 25% of the service interval SI is used for sending
BE traffic. Fig. 9 shows that, without admission control, as
the load increases, the throughput for BE traffic increases, but
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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 57, NO. 2, MARCH 2008
Fig. 7. Voice traffic latency standard deviation with DAC. (a) Voice Traffic A. (b) Voice Traffic B. (c) Voice Traffic C.
Fig. 8. Voice traffic latency standard deviation with F-DAC. (a) Voice Traffic A. (b) Voice Traffic B. (c) Voice Traffic C.
Fig. 9. BE traffic throughput comparison.
as the system load further increases (e.g., at 180 s), the BE
throughput starts to decrease and ultimately reaches a very
small value. It is because, without admission control, all traffic
is admitted, and voice and video streams always have a better
chance of accessing the channel. At this heavy load, only traffic
from those voice and video streams can be transmitted, and
hence, the overall throughput for BE reaches an unacceptable
low value (200 kb/s). This clearly shows the unfairness that
BE traffic experiences. When the admission control scheme
is applied and at higher loads, the throughput of BE slightly
decreases but still maintains a much better overall throughput
performance. That is due to the fact that many arriving real-time
streams (voice or video) will be rejected, because their QoS
requirements cannot be guaranteed; therefore, BE traffic will
have some available resources to transmit their packets. More-
over, with admission control, the system through its resource
reservation maintains a guaranteed minimal bandwidth for BE
traffic, as shown in Fig. 9. We can see that the DAC can achieve
better throughput for BE traffic than the F-DAC at higher
loads. This is mainly because, in DAC, all real-time traffic will
be assigned a relatively larger TXOP value than desired. BE
traffic will contend to utilize the unused period of TXOP value
from real-time traffic for its transmission. However, this higher
throughput for BE is obtained by sacrificing the total number of
admitted real-time streams. The purpose of admission control
is to provide a guaranteed minimal throughput for BE traffic
while increasing the total number of admitted real-time streams.
We can see that, in F-DAC, the throughput of the BE traffic is
guaranteed around its desired minimal bandwidth, and the total
number of admitted real-time flows is also improved.
6) CR: In this section, we examine how the CR will affect
the performance of different traffic streams. Figs. 10 and 11
compare the average CR of voice and video streams. The CR is
measured as the ratio between the number of packets collided
and the number of packets transmitted per flow. First, without
admission control, the CR cannot be controlled since all streams
are admitted. Fig. 10 shows that, as the load increases, the
CR also increases. At some point, however, the CR exhibits
no further increase as the load continues to increase, and
this is attributed to the fact that many flows are contending
for channel access; hence, a station will have less chance of
accessing the channel and transmitting packets of a particular
stream. Alternatively, Fig. 11 shows a similar trend for the CR
when the system does not implement any admission control.
However, interestingly, as the load increases (e.g., after 315 s
for video traffic A), the CR starts decreasing. This is due to
the fact that the more flows we have in the system, the less
chance there will be for a station to capture the channel and
transmit its packets and, hence, the less chance there is for a
ASSI et al.: PER-FLOW ADMISSION CONTROL AND QoS PROVISIONING IN IEEE 802.11e WIRELESS LANs
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Fig. 10. Voice traffic CR comparison. (a) Voice Traffic A. (b) Voice Traffic B. (c) Voice Traffic C.
Fig. 11. Video traffic CR comparison. (a) Video Traffic A. (b) Video Traffic B. (c) Video Traffic C.
collision. With admission control, fewer flows are admitted,
and hence, the CR is better controlled, particularly under heavy
load. Fig. 10 shows that the CR under DAC at heavy load
becomes constant. However, the CR is only slightly better than
the N-AC case, because DAC does only congestion control
(i.e., the selection of AIF S and CWmin values) for BE traffic.
A similar performance is exhibited for video traffic. Under
F-DAC, however, the CR is limited to a much smaller value
(both for video and voice). This is because fewer flows are
allowed in the system, and better congestion control is applied
for all types of traffic. In summary, our results show that, to
decrease the CR, we need to apply congestion control. With
congestion control, the CR of real-time traffic can be significantly decreased, and system performance can be dramatically
improved.
VI. C ONCLUSION
We demonstrated that, although the IEEE 802.11e standard
provides differential access for traffic with various QoSs, it
does not provide any form of protection for existing traffic.
This is because every arriving flow is admitted, regardless of
the channel conditions. This lack of admission control will
degrade the services offered by the network. Although admission control schemes have been recently proposed to provide
such protection, they have their limitations. We proposed an
F-DAC in which the admission parameters are derived according to the QoS requirements of the flow and not to that of
the AC, where the flow belongs. Furthermore, these admission
parameters consider both the QoS requirements of the arriving
stream and the current channel conditions. We compared the
proposed admission control scheme with another DAC. The
results have shown that, although both schemes could provide
the desired QoS for admitted streams, the flow-based scheme
achieved better channel utilization in terms of the total number
of admitted flows and the overall system throughput.
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Chadi M. Assi (M’03) received the B.S. degree
in engineering from Lebanese University, Beirut,
Lebanon, in 1997 and the Ph.D. degree from the
Graduate Center, City University of New York, in
April 2003.
In August 2003, he joined the Concordia Institute
for Information Systems Engineering, Concordia
University, Montreal, QC, Canada, where he is currently an Associate Professor. His current research
interests are wireless networks, optical networks, and
network security.
Dr. Assi was the recipient of the prestigious Mina Rees Dissertation Award
from the City University of New York for his research on wavelength-divisionmultiplexing optical networks in August 2002.
Anjali Agarwal (SM’03) received the B.E. degree in electronics and communication engineering
from Delhi College of Engineering, Delhi, India,
in 1983, the M.Sc. degree in electrical engineering
from University of Calgary, Calgary, AB, Canada, in
1986, and the Ph.D. degree in electrical engineering
from Concordia University, Montreal, QC, Canada,
in 1996.
She is currently an Associate Professor with the
Department of Electrical and Computer Engineering,
Concordia University. Prior to joining the faculty at
Concordia University, she was a Protocol Design Engineer and a Software
Engineer in industry, where she was involved in providing specifications
and design issues for TCP/IP and Voice-over-IP support and in the software
development life cycle of real-time embedded software. Her current research
interests are the various aspects of real-time and multimedia communication
over the Internet and over access networks.
Yi Liu (S’05) received the B.Eng. degree from Nanjing University of Posts and
Technology, Nanjing, China, and the M.E. degree from Concordia University,
Montreal, QC, Canada.
He is currently a System Engineer with Nortel, Ottawa, ON, Canada. His
research interests are wireless networks and WiMax.