Modeling the IEEE 802.11e EDCA for MAC parameter optimization
Boris Bellalta1 , Cristina Cano1 , Miquel Oliver1 , Michela Meo2
(1) Universitat Pompeu Fabra, Barcelona, Spain
{boris.bellalta,cristina.cano,miquel.oliver}.upf.edu
(2) Politecnico di Torino, Torino, Italy
michela.meo@polito.it
Abstract
A model of the IEEE 802.11e EDCA function is presented. The model is built to be simple, but
providing a clear description of the inter-relations between the EDCA MAC parameters (AIF S,
CWmin , CWmax and T XOP values) and the traffic flow characteristics (packet arrival rate and
packet length). The model is intended to be used as an abstraction of the IEEE 802.11e MAC
protocol in WLAN flow level simulations and/or analytical studies, such as Radio Resource Management and Call Admission Control. Moreover, as the model is able to capture the reciprocal
effects of tuning the MAC parameters, it can be used for optimization purposes as is presented in
this paper. Thus, using a multigoal optimization function, the optimal EDCA MAC parameters are
computed and evaluated in presence of elastic (BE) and rigid (V O) flows. The optimization process
has the goals of maximizing the elastic throughput while assuring the bandwidth-delay requirements
of the rigid flows. Finally, the outcomes allow us to define a procedure to tune iteratively the MAC
parameters, showing performances close to the optimal ones.
Keywords: IEEE 802.11e, EDCA, elastic and rigid flows
1
Introduction
IEEE 802.11 based WLANs [1] have become the most popular mobile/wireless broadband access to
Internet. Currently, the major part of the traffic carried by these networks is composed by traditional
internet services such as web browsing, e-mail or file transfer from P 2P applications [3]. However, voice
calls over Internet (VoIP) and other streaming or interactive services like real-time video or games will
play an important role in future heterogenous multiservice wireless networks.
In order to satisfy the higher traffic and the service requirements of novel applications, WLAN technology evolves in two clear directions: i) trying to maximize the utilization of the wireless channel with
novel PHY specifications, using OFDM and MIMO techniques and ii) improving traffic differentiation
mechanisms at MAC level, in order to manage properly the different traffic profiles.
First Access Points (AP) and wireless cards implementing the EDCA (Enhanced Distributed Channel Access) [2] are already commercially available under the WMM (Wireless MultiMedia) denomination. EDCA/WMM provides traffic differentiation by classifying the traffic flows in different Access
Categories (ACs) where each AC has its own MAC parameters.
About the parameters considered in EDCA, two basic questions arises: are optimal the standard
suggested parameters? Can other settings allow better performance results? In this paper we try to
answer these questions. To do this, a model of the IEEE 802.11e EDCA is developed. The model is
designed to be simple and computationally efficient but capable of capturing the main relationships
among MAC parameters in both saturated and non-saturated conditions. For more detailed and
complex (also in computational terms) models, refer to [4] for saturated traffic conditions or [5] and [7]
for unsaturated ones. Once the model is validated, it is used to find the optimal MAC parameters using
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a multi-objective optimization function. Finally, performance metrics obtained with the DCF , EDCA
and EDCA∗ (optimal parameters) are compared and used to present a simple EDCA parameter tuning
algorithm (EDCA − T A) which performs close to the optimal one.
2
A model of the IEEE 802.11e
The IEEE 802.11e EDCA classifies the traffic flows in Access Categories (ACs). Four ACs are defined,
each one associated to a single MAC transmission queue, where each AC has its own MAC parameters
and behaves independently of others. Letting ACj , with j = 1..4 the access category j, the basic
MAC parameters of each access category are labelled as: Arbitration Interframe Space (AIF Sj ), Minimum Contention Window (CWmin,j ), Maximum Contention Window (CWmax,j ) and Transmission
Opportunity (T XOPj ) 1 . The default values of each parameter are reported in Table 1.
AC
0 (Background: BK)
1 (Best effort: BE)
2 (Video: VI)
3 (Voice: VO)
Aj
7
3
2
2
T XOPj (ms)
0
0
6.016
3.264
Bj (packets)
0
0
4
2
CWmin,j
CWmin
CWmin
CWmin /2
CWmin /4
CWmax,j
CWmax
CWmax
CWmin
CWmin /2
Table 1: Default EDCA values
2.1
Modeling a node
A mobile node (including the AP) is modeled as a single queue of length K packets. Packets arrive
to the M Ni queue following a Poisson process with rate αi , where subscript i identifies the node i in
the network (i = 1 . . . n). The packet arrival rate is related to the bandwidth Br,i and the average
packet length E{Li } (assumed to be exponentially distributed) of the traffic flow active in M Ni ,
αi = Br,i /E{Li }. Packet service rate depends on the M Ni MAC parameters (BEBi,j , T XOPi,j and
AIF Si,j ) but also on the behavior of the others M N s (which also depends on their MAC parameters
and traffic profile). As we consider a single AC per node, the j subscript is avoided.
2.2
Channel probabilities
During the back-off process a M N senses continuously the channel, which can be in one of these three
states: empty, in a successful transmission or in a collision. Another possible state, not considered
here as the channel is assumed to be error-free, is the erroneous, meaning a successful transmission
but containing erroneous bits due to channel impairments.
Let pe,i , ps,i and pc,i be the probabilities with that M Ni sees each channel state. These probabilities
are related to the stationary transmission probability of the other n − 1 nodes, τj , j 6= i, and are given
by
pe,i =
Y
(1 − τj )
j6=i
2.3
ps,i =
X
τz
z6=i
Y
(1 − τj )
pc,i = 1 − pe,i − ps,i
(1)
j6=z6=i
Average slot duration
From the point of view of the M Ni , the back-off counter is only decreased after sensing the channel
free for σ seconds (one slot time). If during the back-off count-down the channel is sensed busy, the
back-off counter is frozen until the channel is detected free once again. Therefore, the time between
1
For simplicity, a fixed number of Bj packets, independent of their length, is considered.
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http://folk.uio.no/paalee/
two back-off decrements is a random variable which depends on previous channel probabilities and the
duration of the events related with them, which its expected value is
Bk ,•
•
αi = pe,i σ + ps,i (ETs,i
+ σ) + pc,i (ETc,i
+ σ)
(2)
• is the expected duration of a collision and ET Bk ,• is the expected duration of a
where ETc,i
s,i
successful transmission. The • denotes that this expected duration is from the point of view of the
M Ni which is in back-off.
To obtain the values for these channel durations, the multiple-packet transmission (EDCA’s T XOP )
for M Nk6=i has to be considered. Thus, the duration of a successful transmission depends on: i) the
packet length statistics of the transmitting node, ii) the ACK policy considered and iii) the number of
packets transmitted, which also depends on the queue occupation and the T XOP value (upper limit) of
the transmitting node (M Nk ). Notice that, higher values of T XOP will increase the time the channel
is occupied by M Nk . However, at the same time, the number of channel attempts by M Nk is expected
to be lower (which is only true if the M Nk is un-saturated) which results in higher pe,i values. In the
• only
other side, the duration of a collision comprises always a single MPDU transmission, so ETc,i
depends on the packet length statistics of the colliding nodes. To compute them, refer to [9].
At worst, from the point of view of the other nodes, we will assume that M Nk always transmits
Bk ,•
bulks of Bk packets (independently of its queue occupancy), resulting in a channel use equal to ETs,k
seconds at each successful transmission.
2.4
Transmission Probability
The probability that the M Ni transmits in a random chosen slot, τi , depends on i) M Ni has data to
be transmitted and ii) its AC parameters and iii) the behavior of other nodes. Taken into account all
contributions, the transmission probability is
τi =
ρi
EBia + 1
(3)
where the EBia includes the count-down slots from the BEBi instance (EBi ) and the extra blocked
slots due to the AIF Si behavior, with EBia = Ai + EBi + EBib . Note that we are approximating the
AIF S effect as extra slots (EBib ) that M Ni has to wait.
2.4.1
Binary Exponential Back-Off (BEB)
Prior to transmit, a MN starts a backoff instance. The same expression obtained by Bianchi [8] is used
to calculate the average number of slots, EBi , that M Ni has to wait before to transmit a packet.
2.4.2
Arbitration Interframe Space (AIFS)
An approach similar to [5] is used to compute the average number of blocked slots due to the AIF Si
duration, EBib . Thus, the number of transmissions observed by M Ni during its back-off countdown is
ptr,i · EBi , with
ptr,i = 1 −
Y
(1 − τj )
(4)
j6=i
which is the probability that M Nk6=i transmit while M Ni is in back-off. Thus, the average extra
number of blocked slots is
EBib = ptr,i · EBi · Ai
(5)
Notice how the impact of Ai is related to the transmission probability of the other nodes. For instance,
EBib is equal to 0 if there are no other M N transmitting.
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2.4.3
Queue utilization
Under the assumption that each packet is released from the transmission queue each time its corresponding ACK is received (so, no Block ACK is considered), the M Ni is modeled as a simple
M/M/1/Ki queue with departure rate depending on the T XOPi limit (Bi , measured in packets).
Thus, for the M Ni , the departure rates are modeled as
µi = Bi /XiBi
(6)
where XiBi is the TXOP service time for M Ni due to a transmission of Bi packets. The queue
utilization from M Ni , ρi is computed as the probability that the queue is not empty, so ρi = 1 − π0s ,
from the queue steady state probability distribution.
2.5
Packet Service Time
The packet service time is the effective delay since a packet is ready to be transmitted (head of the
queue) until it is released from the queue, as it has been correctly delivered or discarded due to a
maximum retry limit Ri .
Considering ideal channel conditions, a packet is not correctly received only if it suffers a collision.
The conditional probability of packet collision for M Ni is
ptr,i = 1 −
Y
(1 − τk )
(7)
k6=i
which is equivalent to the probability that at least another M Nk6=i transmits in the same slot
than M Ni . The average number of transmissions that a packet undergoes is computed under the
independence assumption as,
Mi =
i +1
1 − pR
i
1 − pi
(8)
Finally, the service time is given by
Bi
XiBi = (Mi − 1) (EBia αi + ETc,i ) + EBia αi + Ts,i
(9)
Bi
where Mi is the average number of required transmissions, αi is the average slot duration, Ts,i
is the
duration of a Bi packets burst transmission and ETc,i is the average duration of a collision involving
node i.
Traffic Flow
elastic (E)
rigid (R)
Bandwidth
max.available
100 Kbps
Frame Length
1500 Bytes
200 Bytes
Table 2: Traffic Profiles
3
Model and Scenario Validation
A single-hop ad-hoc scenario with ne = 4 nodes carrying an elastic (saturated) flow and ns (variable)
nodes carrying a rigid one (un-saturated) is built. The traffic profile for each node class is shown in
Table 2 and results are plotted in Fig. 1. Analitical results are compared with the ones obtained from a
detailed simulator of the IEEE 802.11e EDCA MAC protocol, developed using the COST (Component
Oriented Simulation Toolkit) simulation package [10]. The parameters used to validate the model are
shown in Table 3.
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Parameter
Rdata
AIF Sj ,
SIFS
TXOP
SLOT (σ)
RTS
MAC header
PLCP preamble
Retry Limit (R)
Value
2 M bps
see Table 1
10 µs
see Table 1
20 µs
160 bits @ Rbasic
240 bits @ Rdata
144 bits @ Rbasic
7
Parameter
Rbasic
RP HY
CWmin = 32
CWmax
ACK
CTS
MAC FCS
PLCP header
K (Queue length)
Value
1 M bps
1 M bps
see Table 1
see Table 1
112 bits @ Rbasic
112 bits @ Rbasic
32 bits @ Rdata
48 bits @ Rbasic
20 packets
Table 3: System parameters of the IEEE 802.11b specification [1]
5
14
5
x 10
18
B=1 sim
B=4 sim
B=8 sim
B=1 model
B=4 model
B=8 model
12
x 10
B=1 sim
B=4 sim
B=8 sim
B=1 model
B=4 model
B=8 model
16
14
10
12
8
bps
bps
10
8
6
6
4
4
2
2
0
0
2
4
6
8
10
12
14
R flows
0
0
5
10
15
R flows
(a)
(b)
Figure 1: Aggregate Throughput for (a) Elastic flows and (b) Rigid flows
Three values of Bi for rigid flows are considered: 1, 4 and 8. The other parameters are equal to the
defined in [2] for the AC V O. As can be seen, both simulation and model match very well. Moreover,
from Fig. 1 the benefits of using higher Bi limits can be explained. For example, considering the
no utilization of the T XOP mechanism (single packet transmission, Bi = 1) only 4 rigid flow behave
correctly. However, increasing the T XOP value, the system reaches up to 8/9 rigid flows respectively.
The same model using an M/M/1[1,Bi ] /Ki bulk queue had been validated in [9].
Searching the optimal EDCA parameters (EDCA∗ )
4
In order to compute the optimal working parameters, a multi-goal optimization function is used. Four
objectives are defined: i) to guarantee the rigid throughput, ii) a queue utilization of rigid flows near
ρV O = 0.8, iii) an average queuing delay (including service time) lower than 150 ms and iv) the
maximum allowable elastic throughput. The set of weights are: i) the rigid throughput is considered
a hard constraint (1 %), ii) a low level of flexibility for ρV O (10 %), iii) a soft requirement for
the average queueing delay (10%) 2 and iv) a very soft requirement for the best-effort throughput
2
as the queue length of MN is limited to 20 packets, the average delay had a very lower influence during the optimization
process
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(75 %)). The optimization problem is solved by using the MATLAB 3 package. The allowed range
for MAC parameters is: CWmin,BE = [32, 1024], CWmin,V O = [8, 32], ABE = [3, 7], AV O = [2],
T XOPBE = [1, 7] and T XOPV O = [1, 7].
To obtain the optimal parameters a fixed number of elastic flows equal to ne = 3 and a variable
number of rigid flows ns are considered. Packet lengths are the same shown in Table 2 but three
different bandwidth BV O values are considered for rigid flows: 100, 150 and 200 Kbps. The optimal
parameters are shown in Table 4. From these results, next tendencies are observed:
1. The CWmin,BE and CWmin,V O are used only for a ”fine-tuning” optimization. These parameters
are only significatively modified when all other parameters are at their maximum/minimum
values.
2. As we are trying to maximize the BE throughput, the T XOPBE starts with higher values. It is
decreased as the contention increases (ns flows increases). However, which is remarkable, prior
to decrease this value, the system tries to check whether higher values of T XOPV O are feasible.
For example with BV O = 150 Kbps and ns = 4, a single increment of T XOPV O allows also to
increase T XOPBE , regarding the optimal parameters for ns = 3.
3. The T XOPV O starts with lower values and it is increased as contention increases.
4. AIF SBE only is increased when both T XOPBE and T XOPV O are near its minimum/maximum
value. Is interesting to see how its influence is related with the traffic load. Thus, increasing the
number of V O flows could not require increase the AIF SBE value.
nr
1
2
3
4
5
6
7
8
9
10
11
Br = 100 Kbps
BE
VO
{32, 7, 3}
{9, 4, 2}
{32, 7, 3}
{9, 4, 2}
{32, 7, 3}
{9, 3, 2}
{32, 7, 3}
{9, 6, 2}
{32, 7, 4} {10, 8, 2}
{32, 7, 3} {10, 7, 2}
{32, 7, 3}
{9, 7, 2}
{32, 7, 3} {10, 7, 2}
{33, 6, 3}
{9, 5, 2}
{35, 2, 4} {10, 8, 2}
{377, 1, 7} {15, 8, 2}
Br = 150 Kbps
BE
VO
{32, 7, 3} {9, 3, 2}
{32, 7, 3} {9, 3, 2}
{32, 6, 3} {9, 4, 2}
{32, 7, 3} {9, 5, 2}
{32, 7, 3} {11, 8, 2}
{32, 7, 4} {9, 8, 2}
{45, 1, 7} {16, 8, 2}
···
···
···
···
···
···
···
···
Br = 200 Kbps
BE
VO
{32, 7, 3} {8, 4, 2}
{32, 7, 3} {9, 5, 2}
{32, 7, 4} {9, 6, 2}
{32, 2, 3} {8, 4, 2}
{32, 1, 5} {8, 7, 2}
···
···
···
···
···
···
···
···
···
···
···
···
Table 4: Optimization suggested MAC parameters, with {CWmin,i , Ai , Bi }
Using the optimal settings, called EDCA∗ , the system performance is plotted in Fig. 2. Notice
that using the DCF any rigid flow is able to achieve its throughput requirements as the elastic flows
starve the rigid ones. This is solved by using the EDCA (fixed parameters) which show a clear and
strict traffic differentiation between the V O and BE ACs. However, allowing higher ρV O values than
the ones provided by EDCA, the overall performance is improved using the EDCA∗ parameters. For
all three BV O values, using EDCA∗ there are an increment of one rigid flow over the maximum number
of supported rigid flows from EDCA at the cost of reducing the BE throughput only in this point.
For any other number of ns a higher BE throughput is obtained.
3
c http://www.mathworks.com
°
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6
2
6
x 10
2
DCF − R
DCF − E
EDCA − R
EDCA − E
EDCA* − R
EDCA* − E
1.8
1.6
6
x 10
2
DCF − R
DCF − E
EDCA − R
EDCA − E
EDCA* − R
EDCA* − E
1.8
1.6
1.6
bps
1.2
bps
1.4
1.2
bps
1.4
1.2
1
1
0.8
0.8
0.8
0.6
0.6
0.6
0.4
0.4
0.4
0.2
0.2
0.2
0
0
2
4
6
R flows
8
10
0
12
0
1
2
3
(a)
4
5
R flows
6
7
8
9
0
10
12
7
ρVO
VO
10
6
1
0.5
8
5
DCF
EDCA
EDCA*
ρ
ρVO
0.5
4
1.5
1
6
R flows
3
DCF
EDCA
EDCA*
1
4
2
(c)
1.5
2
1
R flows
DCF
EDCA
EDCA*
0
0
(b)
1.5
0
DCF − R
DCF − E
EDCA − R
EDCA − E
EDCA* − R
EDCA* − E
1.8
1.4
1
x 10
0
0.5
0
1
2
3
(d)
4
5
R flows
6
7
8
9
10
0
0
(e)
1
2
3
4
R flows
5
6
7
8
(f)
Figure 2: Throughput (a) 100 Kbps, (b) 150 Kbps, (c) 200 Kbps and Queue Utilization (d) 100 Kbps
(e) 150 Kbps (f) 200 Kbps
4.1
Setting different ρ levels
The goal over the V O queue utilization provides a threshold between queuing delay and losses for
V O flows and the flexibility to allocate extra resources to the BE AC flows. This means that lower
objective values for ρV O will give higher protection to V O flows (less losses and delay) but also lower
throughput for BE flows. Setting a bandwidth of BV O = 150 Kbps, with ns = 5 and ne = 3, the
parameter evolution for different ρV O values is described in Table 5. Notice that we have increased
the range of possible AV O values up to 7 to provide more freedom to the optimization function.
The BE throughput resulting of setting different ρV O objective values is shown in Fig. 3. Note
how lower ρV O are conservative and are obtained by decreasing the BE performance. Although, higher
ρV O allow the system to allocate more resources for the BE flows.
5
A parameter tuning algorithm (EDCA − T A)
As the optimization process is computationally costly, a simple algorithm capable of capture the
tendencies observed is required. Then, we have proposed the following tuning algorithm.
1. Initialize the parameter vector to the EDCA defined parameters, so ξ = [BE : (32, 7, 3), V O :
(8, 1, 2)]. Set the goals vector G = (SV O , D) to the desired values of throughput SV O and delay
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Objective ρV O
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
B = 150
BE
{177, 1, 4}
{81, 1, 5}
{32, 2, 3}
{32, 1, 1}
{32, 4, 1}
{32, 3, 1}
{32, 2, 1}
{39, 7, 5}
{32, 2, 1}
Kbps
VO
{8, 7, 2}
{8, 8, 2}
{8, 6, 2}
{8, 5, 2}
{8, 8, 2}
{8, 6, 2}
{9, 4, 2}
{11, 4, 2}
{8, 4, 3}
Table 5: Optimization suggested MAC parameters, with {CWmin,i , Ai , Bi }
5
1.5
11
EDCA
EDCA*
ρVO objective
x 10
EDCA−R
*
EDCA −R
EDCA−E
EDCA*−E
10
9
8
1
7
bps
ρVO
6
5
4
0.5
3
2
1
0
0
0.1
0.2
0.3
0.4
ρ
VO
0.5
0.6
objective
0.7
0.8
0.9
1
0
0
0.1
0.2
0.3
0.4
ρ
VO
(a)
0.5
0.6
objective
0.7
0.8
0.9
1
(b)
Figure 3: (a) Goodness of fitting (ρV O ) (b) Throughput for different ρV O values
D (mean and jitter). Go to 2.
2. Using the current parameters ξ. Check whether the goals (allowing a 1 % of packet losses) are
achieved or the maximum number of iterations have been reached. If so, go to Step 4. Else, go
to Step 3.
3. Increase/Decrease sequentially (only one change each iteration):
(a) T XOPV O → T XOPV O + 1. Update ξ. Go to Step 2.
(b) ABE → ABE − 1. Update ξ. Go to Step 2.
(c) T XOPBE → T XOPBE − 1. Update ξ. Go to Step 2.
If the maximum values of all three parameters have been reached, double at each iteration the
value of CWmin,BE until its maximum value is reached. Update ξ. Go to Step 2.
4. Set the working parameters to ξ.
Running the algorithm in the same conditions in what the EDCA∗ parameters have been obtained,
the MAC parameters resulting are shown in Table 6. Notice the similar behavior with the optimal
parameters of previous section. Moreover, in Fig 4 there are plotted the aggregate throughput and
the queue utilization for the EDCA∗ and the EDCA − T A parameters. Rigid throughput is perfectly
matched by the proposed algorithm as it is the main objective of the EDCA−T A algorithm. However,
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nr
1
2
3
4
5
6
7
8
9
10
11
Br = 100
BE
{32, 7, 3}
{32, 7, 3}
{32, 7, 3}
{32, 7, 3}
{32, 7, 4}
{32, 7, 4}
{32, 6, 4}
{32, 6, 5}
{32, 5, 6}
{32, 2, 7}
{1024, 1, 7}
Kbps
VO
{8, 2, 2}
{8, 2, 2}
{8, 2, 2}
{8, 2, 2}
{8, 2, 2}
{8, 2, 2}
{8, 3, 2}
{8, 3, 2}
{8, 4, 2}
{8, 6, 2}
{8, 8, 2}
Br = 150
BE
{32, 7, 4}
{32, 7, 4}
{32, 7, 4}
{32, 6, 4}
{32, 6, 5}
{32, 4, 6}
{256, 1, 7}
···
···
···
···
Kbps
VO
{8, 2, 2}
{8, 2, 2}
{8, 2, 2}
{8, 3, 2}
{8, 3, 2}
{8, 4, 2}
{8, 8, 2}
···
···
···
···
Br = 200 Kbps
BE
VO
{32, 7, 4} {8, 2, 2}
{32, 6, 4} {9, 2, 2}
{32, 6, 4} {9, 3, 2}
{32, 5, 5} {8, 4, 2}
{32, 2, 7} {8, 6, 2}
···
···
···
···
···
···
···
···
···
···
···
···
Table 6: Parameter selected from algorithm, with {CWmin,i , Ai , Bi }
not significant lower values than the optimal are obtained for the BE throughput. Notice also, the
similar evolution of the ρV O with both parameter sets.
6
2
6
x 10
2
EDCA−TA − E
EDCA* − E
EDCA−TA − R
EDCA* − R
1.8
6
x 10
2
EDCA−TA − E
EDCA* − E
EDCA−TA − R
EDCA* − R
1.8
1.2
1.2
bps
1.4
1.2
bps
1.6
1.4
bps
1.6
1.4
1
1
0.8
0.8
0.8
0.6
0.6
0.6
0.4
0.4
0.4
0.2
0.2
0.2
0
2
4
6
R flows
8
10
12
0
1
2
3
4
5
6
7
8
9
0
ρr
1
0.5
12
7
EDCA−TA
EDCA*
ρr
ρr
0.5
10
6
1.5
1
8
5
EDCA−TA
EDCA*
1
6
R flows
4
(c)
1.5
4
3
R flows
EDCA−TA
EDCA*
2
2
(b)
1.5
0
1
R flows
(a)
0
EDCA−TA − E
EDCA* − E
EDCA−TA − R
EDCA* − R
1.8
1.6
1
x 10
0
0.5
0
1
2
3
4
5
R flows
(d)
(e)
6
7
8
9
0
0
1
2
3
4
R flows
5
(f)
Figure 4: Performance comparison between EDCA∗ and EDCA − T A
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6
7
8
6
Conclusions
The parameters suggested by EDCA are designed to provide a clear traffic differentiation, which could
reduce unnecessarily the performance of BE traffic. To solve this situation, we have proposed a simple
tuning algorithm which, compared with EDCA: i) increases the maximum number of feasible V O flows
and ii) improves significantly the throughput for the BE active flows. These gains are expected to be
greater as the data rate of the system increases.
7
Acknowledgements
This work was partially supported by Spanish Government under project TIC2003-09279-C02-01, and
by the European Commission under NEWCOM network of excellence.
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