Fairness Provision in the IEEE 802.11e Infrastructure Basic Service Set
Fairness Provision in the IEEE 802.11e
Infrastructure Basic Service Set †
Feyza Keceli, Inanc Inan, and Ender Ayanoglu
Center for Pervasive Communications and Computing
Department of Electrical Engineering and Computer Science
The Henry Samueli School of Engineering
University of California, Irvine, 92697-2625
Email: {fkeceli, iinan, ayanoglu}@uci.edu
Abstract
Most of the deployed IEEE 802.11e Wireless Local Area Networks (WLANs) use infrastructure Basic Service
Set (BSS) in which an Access Point (AP) serves as a gateway between wired and wireless domains. We present
the unfairness problem between the uplink and the downlink flows of any Access Category (AC) in the 802.11e
Enhanced Distributed Channel Access (EDCA) when the default settings of the EDCA parameters are used. We
propose a simple analytical model to calculate the EDCA parameter settings that achieve weighted fair resource
allocation for all uplink and downlink flows. We also propose a simple model-assisted measurement-based dynamic
EDCA parameter adaptation algorithm. Moreover, our dynamic solution addresses the differences in the transport
layer and the Medium Access Control (MAC) layer interactions of User Datagram Protocol (UDP) and Transmission
Control Protocol (TCP). We show that proposed Contention Window (CW) and Transmit Opportunity (TXOP) limit
adaptation at the AP provides fair UDP and TCP access between uplink and downlink flows of the same AC while
preserving prioritization among ACs.
I. INTRODUCTION
IEEE 802.11 Wireless Local Area Network (WLAN) is built around a Basic Service Set (BSS) [1].
While a number of stations may gather to form an independent BSS with no connectivity to the wired
network, the common deployment is the infrastructure BSS which includes an Access Point (AP). The
AP provides the connection to the wired network.
The IEEE 802.11 standard [1] defines Distributed Coordination Function (DCF) as a contention based
Medium Access Control (MAC) mechanism. The 802.11e standard [2] updates the MAC layer of the
† This work is supported by the Center for Pervasive Communications and Computing, and by National Science Foundation under Grant
No. 0434928. Any opinions, findings, and conclusions or recommendations expressed in this material are those of authors and do not
necessarily reflect the view of the National Science Foundation.
http://arxiv.org/abs/0704.1842v2
former 802.11 standard for Quality-of-Service (QoS) provision. In particular, the Enhanced Distributed
Channel Access (EDCA) function of 802.11e is an enhancement of the DCF. The EDCA scheme (similarly
to DCF) uses Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) and slotted Binary
Exponential Backoff (BEB) mechanism as the basic access method. The major enhancement to support
QoS is that EDCA differentiates packets using different priorities and maps them to specific Access
Categories (ACs) that use separate queues at a station. Each ACi within a station (0 ≤ i ≤ 3) contends for
the channel independently of the others. Levels of services are provided through different assignments of
the AC-specific EDCA parameters; Contention Window (CW) sizes, Arbitration Interframe Space (AIFS)
values, and Transmit Opportunity (TXOP) limits.
The DCF and the EDCA are defined such that each station in a BSS uses the same contention parameter
set. Therefore, fair access can be achieved in the MAC layer for all the contending stations in terms of the
average number of granted transmissions, over a sufficiently long interval. However, this does not translate
into achieving fair share of bandwidth between uplink and downlink flows in the 802.11e infrastructure
BSS. An AC of the AP which serves all downlink flows has the same access priority with the same AC
of the stations that serve uplink flows. Therefore, an approximately equal number of accesses that an
uplink AC may get is shared among all downlink flows in the same AC of the AP. This leads to the
uplink/downlink unfairness problem in the WLAN where each individual downlink flow gets comparably
lower bandwidth than each individual uplink flow gets at high load. This phenomenon will be described
further in Section II-A.
We deal with weighted fair channel access between the uplink and the downlink flows of the same
AC in the IEEE 802.11e infrastructure BSS. Using a simple analytical approach, we calculate the EDCA
parameter settings that achieve a given utilization ratio between the uplink and the downlink transmissions.
Comparing with simulation results, we noticed that sticking only with analytical results that are based
on ideal condition assumptions may result in inaccuracies in a real WLAN scenario. Therefore, we also
propose a simple model-assisted measurement-based dynamic EDCA parameter adaptation algorithm that
provides weighted fair resource allocation in an arbitrary scenario.
Most of the data traffic in the Internet is carried by Transmission Control Protocol (TCP), while
most of the real-time applications use User Datagram Protocol (UDP). UDP employs one-way unreliable
communication. On the other hand, TCP defines reliable bi-directional communication where the forward
link data rate depends on the rate of received Acknowledgment (ACK) packets in the backward link.
Another key contribution of this study is that our solution considers the effects of this difference on the
design of the weighted fairness support algorithm.
II. BACKGROUND
In this section, we first present the uplink/downlink unfairness problem in the IEEE 802.l1(e) WLAN
at high traffic load. Next, we provide a brief review of the literature on this subject.
A. Problem Definition
In the 802.11e WLAN, at high load, a bandwidth asymmetry exists between contending upload and
download flows which use the same AC. This is due to the fact that the MAC layer contention parameters
are all equal for the AP and the stations. If n stations and an AP are always contending for the access to
the wireless channel using the same AC, each host ends up having approximately 1/(n+ 1) share of the
total transmissions over a long time interval. This results in n/(n + 1) of the transmissions to be in the
uplink, while only 1/(n + 1) of the transmissions belonging to the downlink flows. This is the WLAN
uplink/downlink unfairness problem stated previously. The uneven bandwidth share results in downlink
flows experiencing significantly lower throughput and larger delay. The congestion at the AP may result
in considerable packet loss depending on the size of interface buffers.
The results may even be more catastrophic in the case of TCP flows. The TCP receiver returns TCP
ACK packets to the TCP transmitter in order to confirm the successful reception of data packets. In the
case of multiple uplink and downlink flows in the WLAN, returning TCP ACKs of upstream TCP data
are queued at the AP together with the downstream TCP. When the bandwidth asymmetry in the forward
and reverse path builds up the queue in the AP, the dropped packets impair the TCP flow and congestion
control mechanisms which assume equal transmission rate both in the forward and reverse path [3].
TCP’s timeout mechanism initiates a retransmission of a data packet if it has not been acknowledged
during a timeout duration. However, any received TCP ACK can cumulatively acknowledge all the data
packets sent before the data packet for which the ACK is intended to. When the packet loss is severe in the
AP buffer, downstream flows will experience frequent timeouts resulting in significantly low throughput.
On the other hand, due to the cumulative property of TCP ACK mechanism, upstream flows with high
congestion windows will not experience such frequent timeouts. In this case, it is a low probability
that many consecutive TCP ACK losses occur for the same flow. Conversely, flows with low congestion
window (fewer packets currently on flight) may experience frequent timeouts and decrease their congestion
windows even more. Therefore, a number of upstream flows may starve in terms of throughput while
others enjoy a high throughput. This results in unfairness between the TCP upstream flows on top of the
unfairness between the uplink and the downlink.
Fig. 3 shows the average throughput of individual flows for a scenario of 10 uplink UDP, 10 downlink
UDP, 10 uplink TCP and 10 downlink TCP connections in an ns-2 simulation [4],[5]. Each connection
is initiated by a separate station. All stations employ 54 Mbps data rate at the physical layer. The packet
size is 1500 bytes for all flows. UDP flows are mapped to an AC with CWmin = 31 and CWmax = 511.
TCP flows use an AC with CWmin = 63 and CWmax = 1023. For both ACs, AIFSN values are set to 2
and TXOP limits are 0. Other simulation parameters are as stated in Section IV. The results illustrate the
throughput unfairness of the uplink and the downlink flows. The throughput unfairness between uplink
TCP connections is also significant. Moreover, data packet losses at the AP buffer have almost shut down
all downlink TCP connections.
B. Related Work
There are two groups of studies in the literature related to this work.
The first group works within the constraints of the default 802.11 contention parameters. In [6], the
effect of the AP buffer size in the wireless channel bandwidth allocation for TCP is studied. The proposed
solution of [6] is to manipulate advertised receiver windows of the TCP packets at the AP. Uplink/downlink
fairness problem is studied in [7] using per-flow queueing. A simplified approach is proposed in [8] where
two separate queues for TCP data and ACKs are used. In our previous work, we proposed using congestion
control and filtering techniques at the MAC layer to solve the TCP uplink unfairness problem [9]. Two
queue management strategies are proposed in [10] to improve TCP fairness. A rate-limiter approach is
used in [11] which requires available instantaneous WLAN bandwidth estimation in both directions.
The second group proposes changes at the MAC layer access parameters to achieve improved fairness.
Our work also falls into this category. AIFS and CW differentiation is proposed for improved fairness and
channel utilization in [12]. A simulation-based analysis is carried out for a specific scenario consisting
of TCP and audio flows both in the uplink and the downlink. An experimental study is carried out in
[13] to decide on CW and TXOP values of the AP and the stations for a scenario with TCP uplink and
downlink flows. Both solutions propose that individual uplink and downlink streams use separate ACs. No
guidelines are provided on how to decide on the EDCA parameters that achieve fair resource allocation
for an arbitrary scenario. Also, the interaction of TCP flow and congestion control mechanisms with
the MAC is not addressed. In [14], it is proposed that the AP accesses the channel in Point Interframe
Space (PIFS) completion without any backoff when the interface queue size goes over a threshold. The
use of TXOP is evaluated in [15] for temporal fairness provisioning among stations employing different
data rates. Achieving weighted fairness between uplink and downlink in DCF is studied through mean
backoff distribution adjustment in [16]. A mechanism that dynamically tunes CW and TXOP values in
order to prevent delay asymmetry of realtime UDP flows is proposed in [17]. An adaptive priority control
mechanism is employed in [18] to balance the uplink and downlink delay of VoIP traffic.
III. WEIGHTED FAIR ACCESS BETWEEN UPLINK AND DOWNLINK FLOWS
In this section, we first describe the simple analytical model we propose in order to find the AIFS,
CWmin, and TXOP settings of the ACs that provide weighted fairness between uplink and downlink
flows. Next, we propose a parameter adaptation algorithm which dynamically updates the analytically
calculated CW and TXOP values of the AP regarding simple network measurements. As we will describe
in Section III-D, our dynamic solution also addresses the effects of the slow-start phase of TCP.
Every beacon interval, the AP announces the values of the AC-specific EDCA parameters to the stations.
The stations overwrite their EDCA parameter settings with the new values if any change is detected. Due
to the specific design of the EDCA Parameter Set element in the beacon packet, the stations can only
employ CW values that are integer powers of 2, i.e., the AP encodes the corresponding 4-bit fields of
CWmin and CWmax in an exponent form. A key point which the studies in the literature have missed is
that the CW settings of the ACs at the AP are not restricted to the powers of 2. The ACs at the AP may
use any value and this value does not have to be equal to what is announced via beacons.
A. Analytical Model
Fair access between uplink and downlink flows using the same AC can be provided by assigning
different EDCA parameters for the AP and the stations. This results in two Traffic Classes (TCs) using
the same AC. While uplink flows constitute the first TC, downlink flows constitute the second TC. In the
analysis, we will treat the case with one AC (thus 2 TCs), since we address the weighted fairness problem
between the uplink and downlink flows that are mapped to the same AC. Moreover, we only formulate
the situation when there is only one TC per station, therefore no internal collisions can occur. Note that,
this does not cause any loss of generality, since the analysis can be extended for larger number of ACs
or TCs as in [19], and larger number of ACs per station as in [20],[21].
Our analysis considers the fact that the difference in AIFS creates the so-called contention zones
as shown in Fig. 1 [19],[22],[23],[21]. First, we calculate the average collision probability of each TC
according to the long term occupancy of AIFS and backoff slots in saturation. The average collision
probability of a TC is a function of transmission probabilities of all TCs. Next, we formulate the average
transmission probability for each TC, which is a function of average collision probability of the same TC.
This results in a set of nonlinear equations which can be solved numerically.
We define pci,x as the probability that TCi experiences a collision given that it has observed the medium
idle for AIFSx and transmits in the current slot (note AIFSx ≥ AIFSi should hold). For notational sim-
plicity, let uplink flows belong to TC0 and downlink flows belong to TC1. Let di = AIFSNi−AIFSNmin
where AIFSNmin = min(AIFSN0, AIFSN1) and AIFSi = SIFS + AIFSNi · Tslot. Following the
slot homogeneity assumption of [24], assume that each TCi transmits with constant probability, τi. Also,
let the total number of TCi in the BSS be Ni (note that N1 = 1). Then,
pci,x = 1−
i′:di′≤dx
(1− τi′)
(1− τi)
. (1)
We use the Markov chain shown in Fig. 2 to find the long term occupancy of contention zones. Each
state represents the nth backoff slot after completion of the AIFSmin idle interval following a transmission
period. The Markov analysis uses the fact that a backoff slot is reached if no transmission occurs in the
previous slot. Moreover, the number of states is limited by the maximum idle time between two successive
transmissions which is Wmin = min(CWi,max) for a saturated scenario. The probability that at least one
transmission occurs in a backoff slot in contention zone x is
ptrx = 1−
i′:di′≤dx
(1− τi′)
Ni′ . (2)
The long term occupancy of the backoff slots b′n in Fig. 2 can be obtained from the steady-state solution.
Then, the average collision probability pci is found by weighing zone specific collision probabilities pci,x
according to the long term occupancy of contention zones (thus backoff slots)
pci =
∑Wmin
n=di+1
pci,xb
∑Wmin
n=di+1
where x = max
y | dy = max
(dz | dz ≤ n)
which shows x is assigned the highest index value within
a set of TCs that have AIFSN smaller than equal to n+ AIFSNmin.
Given pci , we can calculate the expected number of backoff slots Ei[tbo] that TCi waits before attempting
a transmission. Let Wi,k = 2
min(k,mi)(CWi,min + 1)− 1 be the CW size of TCi at backoff stage k where
CWi,max = 2
mi(CWi,min + 1)− 1, 0 ≤ mi < ri. Note that, when the retry limit ri is reached, any packet
is discarded.
Ei[tbo] =
(prici)
pk−1ci (1− pci)
1− prici
pk−1ci (1− pci)
. (4)
Then as also shown in [23], the transmission probability of TCi can be calculated as
Ei[tbo] + 1
. (5)
The nonlinear system of equations (1)-(5) can be solved numerically to calculate average collision
and transmission probabilities of each TCi for an arbitrary setting of EDCA parameters. We provide the
validation of the proposed analytical model in [19].
B. Weighted Fairness between Uplink and Downlink Flows
Let γi be the probability that the transmitted packet belongs to an arbitrary user from TCi given that
the transmission is successful. Also, let psi,n be the probability that a successfully transmitted packet at
backoff slot n belongs to ACi. Then,
n=di+1
psi,n
psj,n
, (6)
psi,n =
(1− τi)
i′:di′≤n−1
(1− τi′)
Ni′ , if n ≥ di + 1
0, if n < di + 1.
Let U denote the utilization ratio between the downlink and the uplink transmissions of an AC. Let
NTXOP,i denote the maximum number of packets that can fit in one TXOP of TCi. Then, for our running
example with one AC,
γ1 ·NTXOP,1
γ0 ·NTXOP,0
. (8)
1) Implementation of the Numerical Solution: Without loss of generality, the EDCA parameters of the
stations, AIFS0, CWmin,0, and NTXOP,0, are fixed at predetermined values . Then, the EDCA parameters
of the TC at the AP, AIFS1, CWmin,1, and NTXOP,1, that achieve a required utilization ratio Ur can be
calculated numerically as follows.
1) We assume AIFS differentiation is only used for the prioritization between the ACs not the TCs (thus
AIFS0 = AIFS1).
2) When AIFS0 = AIFS1, after some algebra on (6)-(8),
τ1 · (1− τ0) ·NTXOP,1
τ0 · (1− τ1) ·NTXOP,0
. (9)
Therefore, τ1 can be written in terms of τ0, NTXOP,0, NTXOP,1, and Ur. A numerical solution for τ0
and τ1 for given Ur and a fixed value of NTXOP,1 (initially, NTXOP,1 = 1) is obtained using (1)-(5).
3) CWmin,1 can be calculated as follows (the formula below is obtained using (8) and (9) in [25, Section
IV-A]),
CWmin,i =
2− τi
(1− prici)(1− 2pci)(1− pci)
(1− pci)
2(1− (2pci)
mi+1) + 2mip
ci (1− 2pci)(1− pci)(1− p
ri−mi−1
4) A simple controller block checks whether the prioritization among ACs are maintained or not for the
new configuration. This block ensures that CWmin of a low priority AC (at the AP or a station) is
not smaller than CWmin of a higher priority AC. Therefore, if analytically calculated CWmin,1 value
does not satisfy the controller block requirements, NTXOP,1 is doubled and the algorithm returns to
step 3. The larger NTXOP,1 is, the larger CWmin,1 will be.
5) If the calculated CWmin,1 is not an integer, it is rounded to the closest integer value.
A few remarks on the implementation are as follows.
• A numerical solution also exists when AIFS0 and AIFS1 are not equal, but the implementation
differs since (9) does not hold. In such a case, AIFS1 is also assigned an initial value as NTXOP,1
and the nonlinear system of equations (1)-(8) is solved numerically. According to the controller block
requirements on CWmin,1, the procedure may be repeated for updated values of AIFS1 and NTXOP,1.
• As previously mentioned, our formulation is valid for the situation when there is only one TC per
station (including the AP). As an approximation, we assume that (1)-(8) still holds when there are
multiple TCs at the AP. Indeed as only a few collisions are avoided when the internal collision
procedure is run at the AP [26], the solution of (1)-(8) will be very close to an extension that exactly
formulates the virtual collisions at the AP. In this case, if the AIFS values of TCs within an AC
remains equal, it can be shown that (9) still holds for the TCs of the same AC. Therefore, we use
the implementation procedure previously stated for scenarios when larger number of ACs exist as
long as there is one AC (or TC) per station and multiple TCs at the AP.
2) Proposed BEB Algorithm for non-integer CW values: As specified in [2], the initial value of CW
is set to the AC-specific CWmin. At each unsuccessful transmission, the value of CW is doubled until
the maximum AC-specific CWmax limit is reached. The value of CW is reset to the AC-specific CWmin
if the transmission is successful, or the retry limit is reached thus the packet is dropped.
The proposed analytical calculation for weighted fairness may decide a non-integer value of CWmin,1
thus W1,k, k < r1. The simplest approach is rounding to the closest integer and employing the rounded
value in the BEB.
Instead, we also propose the AP to choose integer W ′1,k values from a probability distribution that satis-
fies E[W ′1,k] = W1,k. For example, it is straightforward to show a simple discrete probability distribution
such as Pr(W ′1,k = ⌊W1,k⌋) = ⌈W1,k⌉ −W1,k and Pr(W
1,k = ⌈W1,k⌉) = W1,k − ⌊W1,k⌋ holds. According
to the proposed algorithm, the EDCA function at the AP decides on the interval (0,W ′1,k) to select the
backoff value regarding the given simple discrete probability distribution.
Fig. 4 shows the downlink/uplink access ratio for increasing number of uplink and downlink flows.
We assume equal AIFSN = 2 for all the stations and the AP, and analytically calculate CWmin,1 that
achieves downlink/uplink access ratio of Ur = 1 when CWmin,0 = 127, NTXOP,0 = 1, and NTXOP,1
is varied from 1 to 4. The performance of rounding the analytically calculated CW values is compared
with the performance of the proposed BEB algorithm that uses the stated discrete probability distribution
function. As the results imply, the proposed BEB algorithm maintains perfect weighted fairness while
rounding the analytically calculated value may result in slight inaccuracies in terms of utilization ratio.
As the number of uplink stations increase, CWmin,1 that achieves Ur = 1 decreases. As Fig. 4 shows
the effect of rounding is much more noticeable when CWmin,1 is small. The effect of rounding becomes
negligible as NTXOP,1 (thus CWmin,1) is increased.
C. Dynamic Parameter Adaptation
The IEEE 802.11 infrastructure BSS exhibits some non-ideal conditions which most of the analytical
models ignore to maintain simplicity. For example,
• Accurate information on the instantaneous number of active flows may not always be available to
the AP [27].
• If a station and the AP collide, the station’s transmission results in failure since the destination (the
AP) is not in listen mode. However, there is some probability that the transmission of the AP results
in success as a consequence of the capture effect depending on the spatial distribution and the power
levels of the stations [28].
Such non-ideal conditions make finding the optimum EDCA setting analytically hard for any scenario.
This also limits the use of proposed BEB algorithm for non-integer CW values. We propose a simple
model-assisted measurement-based dynamic algorithm to adapt the analytically calculated CWmin values
for such scenarios.
The AP carries out the dynamic adaptation for each AC every β beacon intervals which is called an
adaptation interval in the sequel. If it is detected as a new flow starting transmission or as an old flow
becoming inactive at the last adaptation interval, the algorithm decides on new good EDCA parameters
using the proposed analytical model which results in weighted fair resource allocation for the estimated
number of uplink and downlink flows in ideal conditions. Otherwise, fine tuning on the CW and the
TXOP values of the AC at the AP is carried out to make measured U as close as to Ur.
We use a simple algorithm to estimate the number of active flows. More advanced approaches [27]
can also be used. The AP counts the number of unique source and destination MAC addresses observed
from incoming frames to estimate the number of uplink and downlink flows respectively. Let nu and nd
denote the number of uplink and downlink flows labeled as active. If the AP receives a packet with the
corresponding MAC address not on its list, it adds the new MAC address to the list and increments nu or
nd. If the AP does not receive any packet with the corresponding MAC address during the last adaptation
interval, it deletes the MAC address from the list and decrements nu or nd. Then, we define the required
utilization ratio as
. (11)
If Ur has been changed during the last adaptation interval, EDCA parameters are analytically calculated
for U = Ur and the fine tuning phase is skipped. Otherwise, solely fine tuning on CWmin is performed
as follows. Every adaptation interval, the AP measures the number of successful uplink and downlink
transmissions, ntu and ntd respectively where ntd/ntu is the measured U of the last adaptation interval.
< (1 − α) · Ur, then CWmin,1 is decremented (where 0 ≤ α ≤ 1). Similarly, if
> (1 + α) · Ur,
then CWmin,1 is incremented. Otherwise, no action is taken. Note that using steps equal in value to 1 in
the CWmin adaptation is sufficient since the analytical calculation will provide a good initial guess.
D. TCP-MAC Interactions
TCP defines a reliable bi-directional communication where the forward link data rate depends on the rate
of the received ACK packets in the backward link. This behavior of TCP constitutes the main difference
between TCP and UDP access in the WLAN. The key observation is that, if we assume there are no
packet losses in TCP connections (infinitely large interface buffers at the AP and the stations), the TCP
access is fair irrespective of the EDCA parameter selection (which is not the case for UDP). This is due
to the fact that the slow link limits the throughput for all TCP flows. However, when the buffer size at the
AP (bottleneck) is limited, significant unfairness and low channel utilization is experienced as previously
shown in Fig. 3. Therefore, for fair resource allocation and high channel utilization, packet losses at the
AP buffer should be minimized. We configure our adaptation algorithm considering the TCP dynamics to
achieve this objective.
None of the work in the literature on IEEE 802.11 MAC upload/download fairness considered the
asymmetry in the forward and backward link packet rate during the slow-start phase of the TCP con-
nections. During the slow-start phase, the packet rate in the forward link is twice the packet rate in the
backward link. When the congestion avoidance phase is entered, the forward and the backward link packet
rates become equal. When this asymmetry during slow-start is neglected, the download traffic is penalized
with longer queueing delays. Depending on the buffer availability, significant packet loss may even occur
during the slow-start. These may considerably affect the short-term fairness and the channel utilization.
Our solution is simple yet effective. Considering each TCP data and ACK streams of each connection
as individual active flows, the parameter adaptation algorithm of Section III-C is used. Since TCP is
fair irrespective of the EDCA parameter selection as long as there are no packet losses, fine tuning on
CWmin is always skipped. Therefore, the AP does not have to measure ntu and ntd . On the other hand,
fine tuning is carried out on TXOP assignments to overcome increased rate of downlink TCP data flows
during slow-start. Since the forward to backward link packet rate ratio is 2 during the slow-start, the
analytically calculated TXOP duration is multiplied by 2. Our approach is adapting the duration of the
TXOP depending on the number of packets buffered at the interface queue. If the number of packets goes
over a threshold value th, doubled TXOPs are enabled until the number goes below the threshold again.
Assigning best-effort data flows a non-zero TXOP or a small CWmin may not be a favorable approach
when multimedia flows coexist in the WLAN. The controller block located at the AP should check whether
the QoS for admitted realtime flows is preserved or not in the WLAN with the CWmin and TXOP values
calculated for uplink/downlink fairness.
IV. NUMERICAL AND SIMULATION RESULTS
We carried out simulations in ns-2 [4] in order to evaluate the performance of the proposed weighted
fairness adaptation algorithm. For the simulations, we employ the IEEE 802.11e EDCA MAC simulation
module for ns-2.28 [5].
We consider a network topology where each wireless station initiates a connection with a wired station
where the WLAN traffic is relayed to the wired network through the AP. The stations are uniformly
distributed on a circle and the AP is located at the center. The power thresholds are set so that every
station can hear the other’s transmission. The data connections use either UDP or TCP NewReno. The
UDP traffic uses a Constant Bit Rate (CBR) application. The TCP traffic uses a File Transfer Protocol
(FTP) agent which models bulk data transfer. The default TCP NewReno parameters in ns-2 are used.
The UDP traffic is mapped to a higher priority AC than the TCP traffic. All the stations are assumed to
have 802.11g PHY using 54 Mbps and 6 Mbps as the data and basic rate respectively [29]. The packet
size is 1500 bytes for all flows. The buffer size at the stations and the AP is set to 200 packets. We found
β = 5, α = 0.5, and th = 50 packets to be appropriate through extensive simulations.
Fig. 5 shows the average throughput of individual flows for a scenario of 10 uplink UDP, 10 downlink
UDP, 10 uplink TCP and 10 downlink TCP connections (same scenario as in Fig. 3). At the stations,
UDP flows are mapped to an AC with CWmin = 31 and CWmax = 511. TCP flows use an AC with
CWmin = 63 and CWmax = 1023. For both ACs, AIFSN values are set to 2 and TXOP limits are 0.
Unless otherwise stated, all data connections of the stations in other experiments use these ACs (thus these
EDCA parameters). At the AP, we run the proposed algorithm designed for weighted fairness support
in the downlink and uplink. Since the number of downlink and uplink flows are equal for both ACs,
we define the downlink/uplink utilization requirement as Ur = 1. The analytical model decides on the
CW and the TXOP that achieves Ur = 1. Fine tuning on CW is carried out for the fairness of UDP
flows. The TXOP is adaptively doubled according to the proposed algorithm for TCP flows. The results
illustrate that U = 1 is perfectly achieved in terms of throughput for both UDP and TCP flows.
We have tested the proposed algorithm for a range of network conditions.
a) Experiment 1: In the first set of experiments, we generate an equal number of TCP and UDP
flows both in the uplink and downlink. Each flow starts at the same time and the simulation duration is
100 seconds. The wired link delay (denoted as Round Trip Time (RTT) in the titles of the figures) is
equal for all flows (30 ms). Fig. 6 shows the total throughput of TCP and UDP flows in each direction for
the proposed algorithm. The results for the default 802.11e EDCA are also included for comparison. As
the results depict, U = 1 is perfectly achieved in terms of average throughput for the proposed algorithm,
while the default scheduler cannot maintain fair access. Fig. 7 shows the total throughput of TCP and
UDP flows as well as the total system throughput for the proposed algorithm and the default case. The
proposed algorithm can maintain more efficient channel utilization than the default EDCA while providing
fair access. In Fig. 8, we present the performance in terms of fairness between individual TCP or UDP
flows in the same direction for the proposed algorithm and the default EDCA. The performance metric
we use is the widely used fairness index [30]. The fairness index, f , is defined as follows: if there are n
concurrent connections and the throughput achieved by connection i is equal to xi, 1 ≤ i ≤ n, then
i=1 xi)
i=1 x
. (12)
As the results imply, the proposed algorithm also provides fair access between UDP and TCP flows of
the same direction. However, the default EDCA results in unfair resource allocation even between the
TCP flows of the same direction. As we have described in Section II-A, the unfairness is more significant
between TCP uplink flows. Although no unfair behavior is expected between UDP flows in the same
direction, we have included these results in Fig. 8 for the sake of completeness.
b) Experiment 2: We have repeated the simulation set of experiment 1 when the wired link delay is
varied for TCP flows. The wired link delay of the first TCP connection is set to 24 ms and each newly
generated TCP connection is assigned 4 ms larger wired link delay than the previous one. Therefore,
the second TCP connection has 28 ms wired link delay, the third one has 32 ms wired link delay and
so on. This holds for both uplink and downlink connections. UDP wired link delay is constant for each
connection. Fig. 9 shows the average throughput of each TCP and UDP flow in each direction. Fig. 10
shows the total throughput of TCP and UDP flows as well as the total system throughput. Fig. 11 shows
the performance in terms of fairness between individual TCP or UDP flows in the same direction. As the
results show, the performance of the proposed algorithm in terms of fair resource allocation is independent
of the duration of the wired link delay. High channel utilization and perfect fairness is maintained. On
the other hand, as the comparison of Fig. 8 and Fig. 11 imply, the performance of default EDCA depends
on the duration of the wired link delay. In the case of varying wired link delays, the unfairness between
individual TCP flows both in the downlink and uplink is even worse.
c) Experiment 3: In the third set of experiments, we also generate an equal number of TCP and UDP
flows both in the uplink and downlink. In this scenario, each uplink or downlink flow starts at different
times and the simulation duration is 300 seconds. The wired link delay is equal for all flows. The first
downlink UDP connection starts at t = 5 s. The first uplink UDP connection starts at t = 10 s. The first
uplink TCP connection starts at t = 7 s. The first downlink TCP connection starts at t = 12 s. Then,
a new flow of the same type arrives every 10 s. No other flow arrives after 200 s. Fig. 12 and Fig. 13
show the instantaneous UDP and TCP throughput of individual uplink and downlink flows respectively for
default EDCA. The unfairness between uplink and downlink for both UDP and TCP and the unfairness
between individual TCP flows both in the uplink and downlink are evident. Fig. 14 and Fig. 15 show the
instantaneous UDP and TCP throughput of individual uplink and downlink flows respectively when the
proposed algorithm is enabled. As the results imply, the proposed algorithm adaptively updates EDCA
parameters and always maintains instantaneous Ur (as calculated in (11).
d) Experiment 4: We have repeated the simulation set of experiment 3 when the wired link delay is
varied for TCP flows. We set different wired link delays using the way as previously stated. Fig. 16 and
Fig. 17 show the instantaneous UDP and TCP throughput of individual uplink and downlink flows respec-
tively for default EDCA. The unfairness between individual TCP flows both in the uplink and downlink
are more pronounced when compared with the equal wired link delay scenario. Fig. 18 and Fig. 19 show
the instantaneous UDP and TCP throughput of individual uplink and downlink flows respectively for the
proposed algorithm. Since the proposed algorithm adaptively updates EDCA parameters, it maintains fair
resource allocation. The downlink flows does not starve in terms of throughput.
e) Experiment 5: We have repeated the simulation set of experiment 3 when half of the TCP flows
model short flows. The flow generation times follow the rules of experiment 3. The simulation duration
is 450 s. No other flow arrives after 300 s. The short and long TCP flows are alternatively initiated both
in the downlink and uplink. The short TCP flows consist of 31 packets and leave the system after all the
data is transferred. Fig. 20 shows the total transmission duration for individual short TCP flows for the
proposed algorithm and the default EDCA. Note that flow indices from 1 to 15 represent uplink TCP flows
while flow indices from 16 to 30 represent downlink TCP flows. The file transfers with short durations
can be completed in a considerably shorter time when the proposed algorithm is used. At high load,
short flows experience significantly long delays and connection timeouts when default constant EDCA
parameter selection is used.
f) Experiment 6: We have repeated the simulation set of experiment 5 when the wired link delay
is varied for TCP flows. We set different wired link delays using the way as previously stated. Fig. 21
shows the total transmission duration for individual short TCP flows for the proposed algorithm and the
default EDCA. The comparison of Fig. 20 and Fig. 21 reveals that the proposed algorithm performance
in terms of short TCP flow completion time is independent of varying wired link delays among the flows.
g) Experiment 7: In another set of experiments, we consider three types of traffic sources; audio,
video, and data. The audio traffic model implements a Voice-over-IP (VoIP) application as a Constant
Bit Rate (CBR) traffic profile at 24 kbps. The constant audio packet size is 60 bytes. Although not
presented here, similar results and discussion hold when the silence suppression scheme is used and the
audio traffic exhibits on-off traffic characteristics. For the video source models, we have used traces of
real H.263 video streams [31]. The mean and maximum video payload size is 2419 bytes and 3112
bytes respectively. The mean video data rate is 255 kbps. The audio flows are mapped to an AC with
CWmin = 7 and CWmax = 15. The video flows use an AC with CWmin = 15 and CWmax = 31. For both
ACs, AIFSN values are set to 2 and TXOP limits are 0. Fig. 22 shows the average throughput of uplink
and downlink data flows when there are 5 voice and 5 video flows both in the uplink and downlink (a
total of 20 flows with QoS requirements). Similarly, Fig. 23 shows the average throughput of uplink and
downlink data flows when there are 10 voice and 10 video flows both in the uplink and downlink. We also
compare the results with the proposed algorithm of [13]. As the results of default EDCA and [13] imply,
sticking with constant EDCA parameters for any number of flows does not result in fair access no matter
which EDCA parameter setting is used. On the other hand, the proposed adaptive algorithm effectively
manages fair resource allocation for any number of stations. Note that we have not included the average
throughput of the flows with QoS requirements in Fig. 22 and Fig. 23, since all audio and video flows
get necessary bandwidth to serve offered load with zero packet loss rate. Fig. 24 compares the average
delay of each QoS flow in each direction for default EDCA and the proposed algorithm when there are
a total of 20 flows with QoS requirements. Similarly, Fig. 25 compares the average delay of each QoS
flow in each direction for default EDCA and the proposed algorithm when there are a total of 40 flows
with QoS requirements. As the results show, the QoS flows experience slightly larger delays when the
proposed algorithm is used (due to smaller CW and larger TXOP assignment for data flows). On the other
hand, the delay increase is well within the limits of QoS requirements. Moreover, fair resource allocation
for data flows is provided.
V. CONCLUSIONS
We have proposed a model-assisted measurement-based dynamic EDCA parameter adaptation algorithm
that achieves a predetermined utilization ratio between uplink and downlink flows of the same AC while
keeping the prioritization among ACs. The key contribution is that depending on simple network measures,
the proposed algorithm dynamically adapts the EDCA parameters calculated via a proposed analytical
model. Another key insight is that the proposed algorithm differentiates the way of adaptation between
UDP and TCP flows regarding their characteristics.
The proposed algorithm is fully compliant with the 802.11e standard. We propose AP to use any CW
value, not necessarily exponents of 2. Our observation is that the 802.11e standard does not restrict the
CW settings of the ACs at the AP to be the powers of 2, while the CW setting of the ACs at the STA
should be powers of 2 due to the definition of specific fields in the beacon packet. Our approach provides
the AP the freedom of satisfying any required utilization ratio through fine tuning on CW settings.
Via simulations, it is shown that fair resource allocation between uplink and downlink flows of an AC
can be maintained in a wide-range of scenarios when the proposed model-assisted measurement-based
dynamic EDCA parameter adaptation algorithm is used. The performance of the proposed algorithm in
terms of fair resource allocation is shown to be independent of the duration of the round trip time of a
connection. Short flows experience significantly low delays and no connection timeouts. Therefore, we
conclude that the proposed method also provides short-term fairness. The QoS requirements of existing
audio and video flows in the 802.11e WLAN are maintained. Our results also show that sticking with
constant EDCA parameters at any scenario does not result in fair access no matter which EDCA parameter
setting is used.
REFERENCES
[1] IEEE Standard 802.11: Wireless LAN medium access control (MAC) and physical layer (PHY) specifications, IEEE 802.11 Std., 1999.
[2] IEEE Standard 802.11: Wireless LAN medium access control (MAC) and physical layer (PHY) specifications: Medium access control
(MAC) Quality of Service (QoS) Enhancements, IEEE 802.11e Std., 2005.
[3] H. Balakrishnan, V. Padmanabhan, and R. H. Katz, “The Effects of Asymmetry on TCP Performance,” ACM Baltzer Mobile Networks
and Applications (MONET), 1999.
[4] (2006) The Network Simulator, ns-2. [Online]. Available: http://www.isi.edu/nsnam/ns
[5] IEEE 802.11e HCF MAC model for ns-2.28. [Online]. Available: http://newport.eecs.uci.edu/$\sim$fkeceli/ns.htm
[6] S. Pilosof, R. Ramjee, D. Raz, Y. Shavitt, and P. Sinha, “Understanding TCP Fairness over Wireless LAN,” in Proc. IEEE Infocom
’03, April 2003.
http://www.isi.edu/nsnam/ns
http://newport.eecs.uci.edu/$\sim $fkeceli/ns.htm
[7] Y. Wu, Z. Niu, and J. Zheng, “Study of the TCP Upstream/Downstream Unfairness Issue with Per-flow Queueing over Infrastructure-
mode WLANs,” Wireless Communications and Mobile Computing, pp. 459–471, June 2005.
[8] J. Ha and C.-H. Choi, “TCP Fairness for Uplink and Downlink Flows in WLANs,” in Proc. IEEE Globecom ’06, November 2006.
[9] F. Keceli, I. Inan, and E. Ayanoglu, “TCP ACK Congestion Control and Filtering for Fairness Provision in the Uplink of IEEE 802.11
Infrastructure Basic Service Set,” to appear in Proc. IEEE ICC ’07.
[10] M. Gong, Q. Wu, and C. Williamson, “Queue Management Strategies to Improve TCP Fairness in IEEE 802,11 Wireless LANs,” in
Proc. IEEE WiOpt ’06, April 2006.
[11] N. Blefari-Melazzi, A. Detti, A. Ordine, and S. Salsano, “Controlling TCP Fairness in WLAN access networks using a Rate Limiter
approach,” in Proc. ISWCS ’05, September 2005.
[12] C. Casetti and C. F. Chiasserini, “Improving Fairness and Throughput for Voice Traffic in 802.11e EDCA,” in Proc. IEEE PIMRC ’04,
September 2004.
[13] D. J. Leith, P. Clifford, D. Malone, and A. Ng, “TCP Fairness in 802.11e WLANs,” IEEE Commun. Lett., pp. 964–966, November
2005.
[14] S. W. Kim, B.-S. Kim, and Y. Fang, “Downlink and Uplink Resource Allocation in IEEE 802.11 Wireless LANs,” IEEE Trans. Veh.
Technol., pp. 320–327, January 2005.
[15] I. Tinnirello and S. Choi, “Efficiency Analysis of Burst Transmissions with Block ACK in Contention-Based 802.11e WLANs,” in
Proc. IEEE ICC ’05, May 2005.
[16] J. Jeong, S. Choi, and C.-K. Kim, “Achieving Weighted Fairness between Uplink and Downlink in IEEE 802.11 DCF-based WLANs,”
in Proc. IEEE QSHINE ’05, August 2005.
[17] J. Freitag, N. L. S. da Fonseca, and J. F. de Rezende, “Tuning of 802.11e Network Parameters,” IEEE Commun. Lett., pp. 611–613,
August 2006.
[18] S. Shin and H. Schulzrinne, “Balancing Uplink and Downlink De;ay of VoIP Traffic in WLANs using Adaptive Priority Control (APC),”
in Proc. IEEE QSHINE ’06, August 2006.
[19] I. Inan, F. Keceli, and E. Ayanoglu, “Performance Analysis of the IEEE 802.11e Enhanced Distributed Coordination Function using
Cycle Time Approach,” Center for Pervasive Communications and Computing, University of California, Irvine, Tech. Rep., March
2007. [Online]. Available: http://newport.eecs.uci.edu/$\sim$iinan/publications.htm
[20] Z. Kong, D. H. K. Tsang, B. Bensaou, and D. Gao, “Performance Analysis of the IEEE 802.11e Contention-Based Channel Access,”
IEEE J. Select. Areas Commun., pp. 2095–2106, December 2004.
[21] I. Inan, F. Keceli, and E. Ayanoglu, “Saturation Throughput Analysis of the 802.11e Enhanced Distributed Channel Access Function,”
to appear in Proc. IEEE ICC ’07.
[22] J. W. Robinson and T. S. Randhawa, “Saturation Throughput Analysis of IEEE 802.11e Enhanced Distributed Coordination Function,”
IEEE J. Select. Areas Commun., pp. 917–928, June 2004.
[23] J. Hui and M. Devetsikiotis, “A Unified Model for the Performance Analysis of IEEE 802.11e EDCA,” IEEE Trans. Commun., pp.
1498–1510, September 2005.
[24] G. Bianchi, “Performance Analysis of the IEEE 802.11 Distributed Coordination Function,” IEEE Trans. Commun., pp. 535–547, March
2000.
[25] H. Wu, Y. Peng, K. Long, S. Cheng, and J. Ma, “Performance of Reliable Transport Protocol over IEEE 802.11 Wireless LAN: Analysis
and Enhancement,” in Proc. IEEE Infocom ’02, June 2002.
[26] A. Banchs and L. Vollero, “Throughput Analysis and Optimal Configuration of IEEE 802.11e EDCA,” Comp. Netw., pp. 1749–1768,
August 2006.
http://newport.eecs.uci.edu/$\sim $iinan/publications.htm
[27] G. Bianchi and I. Tinnirello, “Kalman Filter Estimation of the Number of Competing Terminals in an IEEE 802.11 Network,” in Proc.
IEEE Infocom ’03, April 2003.
[28] J. H. Kim and J. K. Lee, “Capture Effects of Wireless CSMA/CA Protocols in Rayleigh and Shadow Fading Channels,” IEEE Trans.
Veh. Technol., pp. 1277–1286, July 1999.
[29] IEEE Standard 802.11: Wireless LAN medium access control (MAC) and physical layer (PHY) specifications: Further Higher Data
Rate Extension in the 2.4 GHz Band, IEEE 802.11g Std., 2003.
[30] R. Jain, The Art of Computer Systems Performance Analysis: Techniques for Experimental Design, Measurement, Simulation, and
Modeling. John Wiley and Sons, 1991.
[31] P. Seeling, M. Reisslein, and B. Kulapala, “Network Performance Evaluation Using Frame Size and Quality Traces of Single-Layer
and Two-Layer Video: A Tutorial,” IEEE Communications Surveys and Tutorials, vol. 6, no. 2, pp. 58–78, Third Quarter 2004.
[Online]. Available: http://www.eas.asu.edu/trace
http://www.eas.asu.edu/trace
Transmission/
Collision period
AIFSN
AIFSN
No Tx Zone 1 Zone 0
TC1 in Backoff
TC0 in Backoff
Fig. 1. EDCA backoff after busy medium.
1 d0 Wmin
tr 1-p
tr 1-p
Fig. 2. Transition through backoff slots in different contention zones for the example given in Fig.1.
0 5 10 15 20 25 30 35 40
Flow index
Fig. 3. Total throughput of 10 uplink UDP (indices 1-10), 10 downlink UDP (indices 11-20), 10 uplink TCP (indices 21-30) and 10
downlink TCP (indices 31-40 flows) when the AP and the stations use equal EDCA parameters.
0 5 10 15 20 25
Number of active uplink and downlink flows
non−integer
round, N
TXOP,1
round, N
TXOP,1
round, N
TXOP,1
Fig. 4. The downlink/uplink access ratio for increasing number of uplink and downlink flows.
0 5 10 15 20 25 30 35 40 45
Flow index
Fig. 5. Total throughput of 10 uplink UDP (indices 1-10), 10 downlink UDP (indices 11-20), 10 uplink TCP (indices 21-30) and 10
downlink TCP (indices 31-40 flows) when the AP uses the proposed adaptation algorithm to achieve Ur = 1.
5 10 15 20 25
Number of TCP/UDP flows at each direction
Equal RTT for each TCP flow 
Default − UDP
uplink
Default − UDP
downlink
Default − TCP
uplink
Default − TCP
downlink
Proposed − UDP
uplink
Proposed − UDP
downlink
Proposed − TCP
uplink
Proposed − TCP
downlink
Fig. 6. The total throughput of TCP and UDP flows in each direction (experiment 1).
5 10 15 20 25
Number of TCP/UDP flows at each direction
Equal RTT for each TCP flow 
Default − UDP
Default − TCP
Default − Total
Proposed − UDP
Proposed − TCP
Proposed − Total
Fig. 7. The total throughput of TCP and UDP flows as well as the total system throughput (experiment 1).
5 10 15 20 25
Number of TCP/UDP flows at each direction
Equal RTT for each TCP flow 
Default − UDP
uplink
Default − UDP
downlink
Default − TCP
uplink
Default − TCP
downlink
Proposed − UDP
uplink
Proposed − UDP
downlink
Proposed − TCP
uplink
Proposed − TCP
downlink
Fig. 8. Fairness index of individual TCP or UDP flows in the same direction (experiment 1).
5 10 15 20 25
Number of TCP/UDP flows at each direction
Different RTT for each TCP flow
Default − UDP
uplink
Default − UDP
downlink
Default − TCP
uplink
Default − TCP
downlink
Proposed − UDP
uplink
Proposed − UDP
downlink
Proposed − TCP
uplink
Proposed − TCP
downlink
Fig. 9. The total throughput of TCP and UDP flows in each direction (experiment 2).
5 10 15 20 25
Number of TCP/UDP flows at each direction
Different RTT for each TCP flow
Default − UDP
Default − TCP
Default − Total
Proposed − UDP
Proposed − TCP
Proposed − Total
Fig. 10. The total throughput of TCP and UDP flows as well as the total system throughput (experiment 2).
5 10 15 20 25
Number of TCP/UDP flows at each direction
Different RTT for each TCP flow
Default − UDP
uplink
Default − UDP
downlink
Default − TCP
uplink
Default − TCP
downlink
Proposed − UDP
uplink
Proposed − UDP
downlink
Proposed − TCP
uplink
Proposed − TCP
downlink
Fig. 11. Fairness index of individual TCP or UDP flows in the same direction (experiment 2).
0 50 100 150 200 250 300
Time (s)
UDP: Equal RTT for each TCP flow, Default
Uplink
Downlink
Fig. 12. The instantaneous UDP throughput of individual uplink and downlink flows for default EDCA (experiment 3).
0 50 100 150 200 250 300
Time (s)
TCP: Equal RTT for each TCP flow, Default
Uplink
Downlink
Fig. 13. The instantaneous TCP throughput of individual uplink and downlink flows for default EDCA (experiment 3).
0 50 100 150 200 250 300
Time (s)
UDP: Equal RTT for each TCP flow, Proposed
Uplink
Downlink
Fig. 14. The instantaneous UDP throughput of individual uplink and downlink flows for the proposed algorithm (experiment 3).
0 50 100 150 200 250 300
Time (s)
TCP: Equal RTT for each TCP flow, Proposed
Uplink
Downlink
Fig. 15. The instantaneous TCP throughput of individual uplink and downlink flows for the proposed algorithm (experiment 3).
0 50 100 150 200 250 300
Time (s)
UDP: Different RTT for each TCP flow, Default
Uplink
Downlink
Fig. 16. The instantaneous UDP throughput of individual uplink and downlink flows for default EDCA (experiment 4).
0 50 100 150 200 250 300
Time (s)
UDP: Different RTT for each TCP flow, Proposed
Uplink
Downlink
Fig. 17. The instantaneous TCP throughput of individual uplink and downlink flows for default EDCA (experiment 4).
0 50 100 150 200 250 300
Time (s)
TCP: Different RTT for each TCP flow, Default
Uplink
Downlink
Fig. 18. The instantaneous UDP throughput of individual uplink and downlink flows for the proposed algorithm (experiment 4).
0 50 100 150 200 250 300
Time (s)
TCP: Different RTT for each TCP flow, Proposed
Uplink
Downlink
Fig. 19. The instantaneous TCP throughput of individual uplink and downlink flows for the proposed algorithm (experiment 4).
0 5 10 15 20 25 30
Flow index
Equal RTT for each TCP flow
Default
Proposed
Fig. 20. The total transmission duration for individual short TCP flows(experiment 5).
0 5 10 15 20 25 30
Flow index
Different RTT for each TCP flow
Default
Proposed
Fig. 21. The total transmission duration for individual short TCP flows (experiment 6).
5 10 15 20 25
Number of TCP flows at each direction
Equal RTT for each TCP flow, number of QoS Flows: 4*5
Default − TCP
uplink
Default − TCP
downlink
Proposed − TCP
uplink
Proposed − TCP
downlink
[13] − TCP
uplink
[13] − TCP
downlink
Fig. 22. The average throughput of uplink and downlink data flows when there are 5 voice and 5 video flows both in the uplink and
downlink (experiment 7).
5 10 15 20 25
Number of TCP flows at each direction
Equal RTT for each TCP flow, number of QoS Flows: 4*10
Default − TCP
uplink
Default − TCP
downlink
Proposed − TCP
uplink
Proposed − TCP
downlink
[13] − TCP
uplink
[13] − TCP
downlink
Fig. 23. The average throughput of uplink and downlink data flows when there are 10 voice and 10 video flows both in the uplink and
downlink (experiment 7).
5 10 15 20 25
Number of TCP flows at each direction
Equal RTT for each TCP flow , number of QoS Flows: 4*5
Default − VoIP
uplink
Default − VoIP
downlink
Default − Video
uplink
Default − Video
downlink
Proposed − VoIP
uplink
Proposed − VoIP
downlink
Proposed − Video
uplink
Proposed − Video
downlink
Fig. 24. The average delay of each QoS flow in each direction when there are a total of 20 flows with QoS requirements (experiment 7).
5 10 15 20 25
Number of TCP flows at each direction
Equal RTT for each TCP flow , number of QoS Flows: 4*10
Default − VoIP
uplink
Default − VoIP
downlink
Default − Video
uplink
Default − Video
downlink
Proposed − VoIP
uplink
Proposed − VoIP
downlink
Proposed − Video
uplink
Proposed − Video
downlink
Fig. 25. The average delay of each QoS flow in each direction when there are a total of 40 flows with QoS requirements (experiment 7).
	Introduction
	Background
	Problem Definition
	Related Work
	Weighted Fair Access between Uplink and Downlink Flows
	Analytical Model
	Weighted Fairness between Uplink and Downlink Flows
	Implementation of the Numerical Solution
	Proposed BEB Algorithm for non-integer CW values
	Dynamic Parameter Adaptation
	TCP-MAC Interactions
	Numerical and Simulation Results
	Conclusions
	References
