R E S E A R C H
Open Access
Achieving per-flow and weighted fairness for
uplink and downlink in IEEE 802.11 WLANs
Chiapin Wang
Abstract
In this article, we investigate a fairness issue between uplink and downlink flows in IEEE 802.11 Wireless Local Area Networks (WLANs). We propose a cross-layer adaptive algorithm which dynamically adjusts the minimum
contention window size of access point according to the amount of downlink users and channel conditions to achieve per-flow fairness. In case that uplink and downlink transmissions are with different bandwidth demands for various applications, our algorithm can efficiently find the optimal minimum contention window size which provides weighted fairness based on their resource requirements. The simulation results demonstrate that our scheme can effectively provide both per-flow fairness and weighted fairness in a varying WLAN environment. Keywords: Fairness, IEEE 802.11 WLAN, MAC contention control, Bandwidth allocation
Introduction
In recent years, IEEE 802.11 Wireless Local Area Net-works (WLAN) [1,2] have become increasingly popular with the wide deployment of infrastructures and the prevalence of mobile/handheld devices. Mobile users over WLAN now can access various broadband and real-time services, e.g., video streaming, peer-to-peer applications, Internet protocol television, and Voice over IP. In general, IEEE 802.11 WLANs employ an infrastructure mode in which an access point (AP) acts as a bridge for exchanging two-direction data traffic, i.e., downlink and uplink, be-tween wireless and wired domains.“Downlink” refers to a traffic flow transmitted from AP to a mobile device, while “uplink” refers to a flow with a reverse direction. The 802.11 medium access control (MAC) layer employs a contention-based channel access mechanism, named dis-tributed coordination function (DCF) for its disdis-tributed and simple manner. With DCF, all 802.11 nodes with packets to send including AP and mobile stations gener-ally have the same channel-access probabilities. Since AP is responsible for all the deliveries of downlink flows, therefore, the total transmission opportunities of downlink flows will be equal to 1/(M + 1) where M is the number of stations. However, such the bandwidth allocation between uplink and downlink flows may not match the user
behavior in real situations while the traffic load of down-link generally is much heavier than that of updown-link. The un-fairness problem between uplink and downlink can particularly be serious when the amounts of downlink flows increase or the traffic load of downlink is much heavier than that of uplink.
In order to provide fair channel utilization between uplink and downlink, AP and mobile stations should be granted suitable transmission opportunities based on their bandwidth demands. In this article, we propose a cross-layer adaptive algorithm which dynamically adjusts the minimum contention window size of AP based on the amount of downlink flows to achieve per-flow fair-ness. In case that uplink and downlink transmissions are under diverse channel conditions, our algorithm can also efficiently find the optimal contention window size to provides fairness according to channel conditions while the channel utilization can be affected by not only the amount of contending flows, but also the link qualities, i.e., bit error rate (BER). Furthermore, if uplink and downlink flows are with different bandwidth demands for various applications, our algorithm can adaptively tune the contention window size to provide weighted fairness based on their resource requirements. The con-tribution of this article is that we present a cognitive al-gorithm based on a cross-layer design which can sense the changes of wireless environments (e.g., the number of flows, channel conditions, and bandwidth demands), Correspondence:chiapin@ntnu.edu.tw
Department of Applied Electronic Technology, National Taiwan Normal University, Taipei, Taiwan
© 2012 Wang; licensee Springer. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
and then adapts the system parameters intelligently to achieve per-flow fairness or weighted fairness. We con-duct simulations to evaluate the performance of the pro-posed adaptive algorithm. The simulation results demonstrate that our approach can effectively provide fairness of channel utilization between uplink and down-link in varying WLAN environments.
The remainder of this article is organized as follows. The following section presents some numerical results to illustrate the fairness problem and the related study. In Section “Proposed cross-layer adaptative algorithm”, we illustrate our proposed adaptive control algorithm as a solution. In Section “Performance evaluations and results”, we construct simulation scenarios to evaluate the effectiveness of the proposed scheme. Finally the art-icle ends with conclusions.
Unfairness problem and related work
In this section, we conduct simulations to explore the fairness problem between uplink and downlink in IEEE 802.11 WLANs based a verified two-dimensional Mar-kov chain model [3]. Figure 1 shows the transmission scenario where there are Ndmobile stations with
down-link traffic and Nu mobile stations with uplink traffic in
an infrastructure 802.11b WLAN environment. Consider that each station processes a User-Datagram-Protocol traffic flow, and assume that all the transmissions are under ideal channel conditions using the highest data rate of 11 Mbps. Assume that all the flows always have packets to send (i.e., under a saturated condition). The adopted 802.11b parameters are shown in Table 1.
Figure 2 presents the aggregate throughput of down-link traffic and that of updown-link traffic varying with the number of uplink stations, Nu. It is shown that as the
number of uplink station increases from 1 to 15, the ag-gregate throughput of downlink traffic decrease from 3.364 to 0.396 Mbps, while that of uplink traffic increase
from 3.364 to 5.945 Mbps. We can observe that an in-creasing amount of uplink stations will reduce the trans-mission opportunity (TXOP) of downlink traffic and consequently the downlink throughput, while the down-link throughput is almost 1/Nd times the uplink
throughput. It is due to the fact that the DCF protocol actually provides all 802.11 transmitting nodes including AP and each uplink station with the same TXOP, i.e., their channel-access probability is equal to 1/(Nu+ 1).
However, AP is responsible for the deliveries of Nd
downlink flows; therefore, the TXOP of one downlink flow is only 1/Nd times that of one uplink flow. It can
introduce an unfair resource allocation between uplink and downlink, and this problem can particularly be crit-ical when the amount of downlink flows increases or the traffic load of downlink is much heavier than that of uplink.
The fairness problems in IEEE 802.11 WLANs have largely been investigated in previous work [4-29]. The authors of [8] proposed to dynamically determine AP’s minimum contention window size and TXOP limit according to the packet error rate and the number of stations. The study [9] presents a measurement-based
Figure 1 The downlink and uplink transmissions in WLANs.
Table 1 The adopted IEEE 802.11b parameter
Parameter Value Transmission rate 11 Mbps Slot-time 20μs SIFS 10μs DIFS 50μs Payload 1500 bytes
PHY header 24 bytes
MAC header 28 bytes
ACK frame 38 bytes
CWmin 32
CWmax 1024
Retry limit 5
Figure 2 The aggregate throughput of uplink and downlink traffic varying with the number of uplink stations.
adaptation algorithm which dynamically controls the enhanced distributed channel access parameter set to achieve a predetermined utilization ratio between uplink and downlink flows in 802.11e WLANs. The authors of [13] proposed an approach that reduces AP’s channel sensing time from DCF inter-frame space (DIFS) to PCF inter-frame space (PIFS) in order to meet the required utilization ratio for downlink traffic flows. This approach grants AP the highest priority to transmit its data frames immediately, but may cause the entire channel slots oc-cupied by AP before the required utilization ratio is matched. The study [14] presents a dynamic contention window control (DCWC) scheme based on the number of downlink flows to achieve per-flow fairness. Neverthe-less, it does not consider some dynamics in WLAN environments such as channel conditions and traffic loads that can greatly impact the performance of fair-ness. The authors of [16] use an analytical approach to find optimal contention window sizes based on the observed idle slot intervals to achieve utility fairness be-tween AP and wireless stations. However, the approaches proposed in [16] may need substantial modi-fications in the MAC layer protocols.
In general, the traffic load of downlink flows may be much heavier than that of uplink flows. The study in [21-23] investigates weighted fairness in case that the downlink and uplink traffic loads are asymmetric. The authors of [21] present the Bidirectional DCF which pro-vides a preferential treatment to downlink traffic by pig-gybacking AP’s data packets after acknowledge (ACK) frames. This approach can provide a ratio of downlink throughput to uplink throughput up to 1. The study [22] developed adaptive schemes to achieve weighted fairness between uplink/downlink traffic flows by dynamically adjusting the backoff counters of AP and stations. The authors of [23] applied differentiated minimum conten-tion windows (CW) for AP and wireless staconten-tions to tune their channel utilization ratio.
The problem of transmission-control-protocol (TCP) unfairness in wireless networks has been researched in [24-29]. The study [24] provides a detail analysis of
per-flow and per-station fairness for TCP per-flows. The authors of [25] proposed a differentiated approach which involves multidimensional parameters including mini-mum CWs, arbitration inter-frame space and TXOP, to solve the TCP fairness problem between uplink and downlink traffic flows in 802.11e WLANs. The authors of [27] propose a cross-layer feedback approach to achieve per-station fairness by estimating each station’s access time and queue length. The study [28] solves the TCP fairness problem by using a dual queue scheme in which one queue is specified for data packets of down-link TCP flows and the other is for ACK packets.
Proposed cross-layer adaptative algorithm
In order to provide fair channel utilization between up-link and downup-link, AP and mobile stations should be granted suitable transmission opportunities based on their bandwidth demands. In this article, we propose a cross-layer adaptive algorithm which dynamically adjusts the minimum CW of AP according to the dynamics of WLAN environments such as the numbers of traffic flows, channel conditions, and application-layer band-width demands to achieve both per-flow fairness and weighted fairness.
Architecture of the proposed adaptive cross-layer algorithm
Figure 3 shows the architecture of the proposed cross-layer approach. The architecture involves a throughput monitor at AP to periodically calculate the ratio between downlink and uplink throughputs. On the other hand, the optimal bandwidth sharing between uplink and downlink flows is determined by some external factors, including the situation of uplink/downlink traffic contentions, PHY-layer channel conditions, bandwidth demands of applica-tions, etc. The reasons of considering these external fac-tors for determining the bandwidth sharing are
(1) The channel utilization can be affected by both the amount of contending flows and channel conditions, i.e., BER
(2) In case that uplink and downlink flows are with different bandwidth demands for various
applications, the resources should be allocated based on their bandwidth requirements to provide
weighted fairness.
Based on the changes of these external factors (environ-mental contexts and cross-layer impacts), our scheme will therefore adaptively adjust the internal factors (i.e., MAC parameters) to achieve per-flow fairness or weighted fairness. The 802.11 channel utilization in fact can be affected by many MAC parameters, e.g., inter-frame space (IFS), mini-mum CW, and TXOP. In this article, we adopt the param-eter of minimum CW (CWmin) for the proposed adaptation
scheme since it is a key parameter affecting not only the ac-cess priority but also the overall system performance [30].
Contention window adjuster
We design a contention window adjuster (CWA) based on a feedback control mechanism to adaptively adjust CWmintoward the optimal value. Figure 4 shows the
feed-back control mechanism of CWA. There are two context metrics for CWA. One metric is the ratio of average up-link throughput to average downup-link throughput,η. It can be determined by means of a measurement-based ap-proach at the AP site. The metricη is defined as
η ¼ 1 Nu XNu j¼1 Thju !, 1 Nd XNd k¼1 Thkd ! ; ð1Þ
where Nuand Ndare the number of uplink and downlink
flows, respectively; Thjuand Thkdis the throughput of
up-link flow j and downup-link flow k, respectively, measured at AP periodically.
Another context metric is the ratio of average uplink bandwidth requirements to average downlink bandwidth
requirements,ψ. The metric ψ is defined as
ψ ¼ 1 Nu XNu j¼1 rju !, 1 Nd XNd k¼1 rkd ! ; ð2Þ
where rjuand rkdis the bandwidth requirement of uplink
flow j and downlink flow k, respectively. This context
can be obtained by packet exchanges between mobile stations and AP. For example, mobile stations can peri-odically advertise AP of their bandwidth demands with a piggy-back technique using ACK frame.
With the two information metrics,η and ψ, CWA will therefore adaptively adjust CWmin such that ψ / η is
equal to 1 to provide per-flow fairness or weighted fair-ness, depending on the value of ψ. For example, if ψ is equal to 1, i.e., uplink and downlink flows are with the same bandwidth requirements, CWA will adaptively ad-just CWminsuch thatη is equal to 1 to provide per-flow
fairness. Alternatively, ifψ is an arbitrary number larger or smaller than 1 when uplink and downlink flows are with different bandwidth demands, CWA will adjust CWmin according to users’ requirements such that η is
equal toψ to provide weighted fairness.
CWA iteratively adjusts CWmin in order to have the
value of ψ/η as close to 1 as possible. The iterative for-mula to adjust CWminis
CWmin;iþ1¼ CWmin;iþ Δi ð3Þ
Δi¼ A log2 ψi=ηi
; ð4Þ
where CWmin,iis the CWmin of ith adaptation; Δi is ith
adaptation step size; A is the normalized step size (A > 0). From Equation (4), we can see that the adapta-tion step size Δidepends on A, ηi and ψi. If ψi/ηi is far
from 1, Δi will be larger; when ψi/ηi is close to 1, Δi
becomes smaller. Note that the adaptation mechanism can work well to provide fairness in both cases whenψi/
ηiis larger or smaller than 1. In general cases whenψi/ηi
is smaller than 1, the step sizeΔiwill be positive to
de-crease CWmin. It is noticeable that althoughΔican be an
arbitrary real number, CWminmust be a positive integer.
Thus, the chosen value for CWmin will be rounded to
the closest integer. Alternatively, when ψi/ηi is larger
than 1, e.g., the amount of uplink flows is rare or band-width requirements of downlink are less than that of up-link,Δiwill be negative to increase CWmin. After several
adaptation steps when ψi/ηi is close to 1, Δi will be
ra-ther small or even zero, and consequently CWminwill
al-most keep steady. At this time the adaptation of CWmin
i
η
) ( log2 i i i =A ψ η Δ i CWmin,CW
min,i+1 iψ
converges to the optimal value in terms of the best per-formance of fairness.
Note that the value of ψi/ηi can vary throughout the
whole transmission period due to the dynamics of WLAN environments such as channel conditions, num-bers of traffic flows, application-layer bandwidth demands, etc. When ψi/ηi is removed from the target
value of 1 due to the change of WLAN environments, the proposed feedback control mechanism aforemen-tioned can automatically tune CWmin to the optimal
value with regard to the current situations.
To evaluate the performance of fairness between up-link and downup-link, we use the Jain fairness index [18]. It is a pertinent criterion index for the fairness of channel utilization in contending 802.11 WLANs. The Jain fair-ness index,Γ, in this study can be represented as
Γ ¼ PNu j¼1 Thu j ru j þ PNd k¼1 Thd k rd k !2 Nuþ Nd ð Þ PNu j¼1 Thu j ru j 2 þPNd k¼1 Thd k rd k 2 " # ð5Þ
Γ has a range of (0, 1] to evaluate the fairness; the value closer to 1 refers to better performances of fair-ness. The index shown in Equation (5) can be used to assess both the per-flow fairness and weighted fairness.
Performance evaluations and results
In this section, we conduct simulations of an IEEE 802.11 transmission scenario to estimate the perform-ance of the proposed algorithm. From the simulation results, we demonstrate the effectiveness of our algo-rithm to provide per-flow fairness between downlink and uplink traffics, and further to provide weighted fair-ness according to users’ bandwidth requirements. The IEEE 802.11 simulation model was built based on our analytical approach [31] which has been developed by extending a verified two-dimensional Markov chain model proposed by Bianchi [3]. However, our analytical model takes into account more realistic factors, includ-ing error-prone channels, multiple data rates, the finite retransmission limit, etc. Thus, our approach could be more practical to provide performance evaluations of 802.11 DCF in realistic WLAN environments. The re-striction of this model is that it considers only saturated traffic (i.e., all the flows always have packets to send), and that it does not take into account the capture effect (i.e., all the data transmissions will fail in the presence of packet collisions without the consideration of their rela-tive signal strength). We wrote Matlab codes to imple-ment the IEEE 802.11 model and provide numerical results in the simulations. The adopted 802.11b para-meters are shown in Table 1. In our adaptive scheme,
the normalized step size A in Equation (4) is set as 2. We compare the performance of the proposed adaptive control algorithm with that of IEEE 802.11 DCF protocol [1], and in some scenarios further with that of the DCWC scheme [14]. The DCWC scheme determines the optimal value of minimum CW of AP to achieve per-flow fairness according to the number of downlink flows. The performances are indexed as uplink/downlink throughputs and the Jain fairness index.
Scenario I: equal bandwidth requirements under ideal channel conditions
The simulation set-up for this scenario assumes 12 downlink and 8 uplink flows of the same class with simi-lar QoS requirements under ideal channel environments. Figure 5 shows per-flow throughput of uplink and down-link with 802.11 DCF and the proposed adaptation scheme, respectively. Figure 5 shows that the downlink and uplink throughputs with 802.11 DCF are 60 and 730 kbps, respectively; the bandwidth sharing between uplink and downlink is quite unfair. To achieve fairness, more resources should be taken from uplink flows and then allocated to downlink flows. With the proposed adaptive scheme, the bandwidth sharing between uplink and downlink is regulated by adapting the parameter of CWmin. Figure 5 also shows that the downlink
through-put can gradually approach the level of 310 kbps at the eighth adaptation sequence while the uplink throughput comes to 360 kbps nearby; the fairness of bandwidth sharing between uplink and downlink is greatly improved.
Figure 6 presents the Jain utility fairness index Γ with 802.11 DCF and the proposed scheme, respectively. It is shown that Γ with DCF is 50.1% steadily. With our scheme, Γ rises and reaches the level of 99.5% (1 refers to the best fairness) at the eighth adaptation sequence and the throughput levels of uplink and downlink flows are rather close at this moment (refer to Figure 5). The
Figure 5 In Scenario I, the per-flow throughput of uplink and downlink traffics with 802.11 DCF and the proposed adaptive scheme, respectively.
results demonstrate that our cross-layer approach can effectively provide per-flow fairness between uplink and downlink in WLAN environments.
Scenario II: dynamic flow amounts in the network
In this scenario, we assume eight downlink flows and eight uplink flows in the network initially and two more downlink flows joining in later. Figure 7 presents per-flow throughput of uplink and downlink with 802.11 DCF and the proposed adaptation scheme, respectively. With 802.11 DCF, the uplink throughput is 730 kbps steadily, while the downlink throughput is 91 kbps ini-tially and degrades to 73 kbps when two more downlink flows joins in the network at the adaptation sequence of 12. The unfairness of bandwidth sharing between uplink and downlink becomes severer when the number of downlink flows increases. With the proposed scheme, it is shown in Figure 7 that the uplink and downlink throughputs converge to the level of about 410 kbps at the nineth adaptation sequence. When two more down-link flows join in the network later (at the 12th adapta-tion sequence), the value of CWmin, which achieves fair
utility earlier ceases to be effective at this moment. With
our adaptive mechanism sensing the change of WLAN conditions (i.e., the number of downlink flows) and adjusting CWmin accordingly, the uplink and downlink
throughputs approach each other again (about 366 kbps) at the sequence of 13.
Figure 8 shows the Jain utility fairness index Γ with 802.11 DCF and the proposed scheme, respectively. It is shown thatΓ with DCF is 62.3% initially and degrades to 55.6% when two more downlink flows joins in the net-work. With our scheme, it is shown that Γ rises and reaches the level of 99.9% at thenineth adaptation se-quence when the uplink and downlink throughputs are quite near (about 410 kbps) at this moment (refer to Figure 7). Later when two more downlink flows joins in the network, Γ slightly drops to 98.4% since the differ-ence between uplink and downlink throughputs is enlarged. It is shown thatΓ rises to 99.9% at the 14th se-quence while the uplink and downlink throughputs are almost equal again (about 366 kbps) at this moment. The results demonstrate that our approach can sense the changes of WLAN environments (i.e., the number of traffic flows) and adjust system parameters accordingly to provide fair utilities between uplink and downlink.
Scenario III: equal bandwidth requirements under diverse and time-varying channel conditions
The simulation set-up for this scenario considers ten downlink flows and ten uplink flows with diverse and time-varying channel conditions. Assume that the uplink flows are in ideal channel conditions (i.e., BER is 0), whereas the downlink flows are with worse link qualities with BER of 5E-6 initially, and later suffer from channel degradation with BER of 1.5E–5. Figure 9 presents per-flow throughput of uplink and downlink with 802.11 DCF and the proposed adaptation scheme, respectively. With 802.11 DCF, the downlink (uplink) throughput is 50 kbps (597 kbps) initially and decreases (increases) to 35 kbps (609 kbps) as the communication environment deteriorates. As shown in Figure 10, the Jain utility Figure 6 In Scenario I, the Jain fairness index with 802.11 DCF
and the proposed adaptive scheme, respectively.
Figure 7 In Scenario II, the per-flow throughput of uplink and downlink traffics with 802.11 DCF and the proposed adaptive scheme, respectively.
Figure 8 In Scenario II, the Jain fairness index with 802.11 DCF and the proposed adaptive scheme, respectively.
fairness indexΓ decreases from 58.3 to 55.7% in the cor-responding time. The degradation of utility fairness be-tween uplink and downlink flows is due to their diverse channel conditions.
With the proposed adaptation scheme, the fairness of channel sharing is improved significantly. The downlink throughput progressively increases and reaches 331 kbps at the eighth adaptation sequence while the uplink throughput comes to a similar level (301 kbps) at this time (Γ = 99.8% as shown in Figure 10). When the link quality of downlink flows deteriorates later at the adap-tation sequence of 10, the value of CWminadjusted
earl-ier cease to be effective in the current situation. Consequently the downlink throughput drops to 233 kbps whereas the uplink throughput increases to 368 kbps; the variation between uplink and downlink throughput increases noticeably (Γ = 95.2%). With our scheme which adaptively adjusts CWmin regarding the
channel diversity of traffic flows, the uplink and down-link throughputs are almost equal (296 kbps) again after the sequence of 13 (Γ = 99.9%). The results demonstrate that our adaptation scheme can effectively tackle a
variety of channel conditions to provide fair channel utilization between uplink and downlink.
Scenario IV: comparison with the DCWC scheme
In the section, we compare the performance of the pro-posed scheme with that of the DCWC scheme [14]. The simulation scenario is similar to that in the previous sec-tion (Scenario III), except that the link qualities of downlink flows are ideal initially, become worse later with BER of 5E–6, and finally deteriorate with BER of 1.5E–5. Figure 11 presents per-flow throughput of up-link and downup-link with the DCWC scheme and our pro-posed scheme, respectively. With the DCWC scheme, the downlink (uplink) throughput is 323 kbps (327 kbps) initially and comes to 274 kbps (357 kbps) and 233 kbps (368 kbps) successively as channel conditions change at the adaptation sequence of 10 and 13 sequentially. It is shown that the difference of throughput between uplink and downlink flows sharply increases from 4 kbps (1%) to 83 kbps (30%) and 135 kbps (58%) sequentially as the communication environment deteriorates. We can ob-serve that the DCWC scheme performs well to provide fairness with ideal link qualities, but may be ineffective under varying and diverse channel conditions. The deg-radation of fairness performance with the DCWC scheme is posed by a skewed channel sharing due to using fixed parameters in varying channel conditions.
With the proposed scheme, the fairness of channel sharing between uplink and downlink can gradually be achieved. The downlink (uplink) throughput comes to 323 kbps (327 kbps) at the adaptation sequence of 8, 330 kbps (301 kbps) at the sequence of 11, and 293 kbps (300 kbps) at the sequence of 16. The variation of throughput between uplink and downlink flows corre-sponding to these moments is quite small as 4 kbps (1%), 29 kbps (10%), and 7 kbps (2%), respectively. The performance difference between the DCWC scheme and our proposed adaptation scheme can be illustrated with Figure 9 In Scenario III, the per-flow throughput of uplink and
downlink traffics with 802.11 DCF and the proposed adaptive scheme, respectively.
Figure 10 In Scenario III, the Jain fairness index with 802.11 DCF and the proposed adaptive scheme, respectively.
Figure 11 In Scenario IV, the per-flow throughput of uplink and downlink traffics with the DCWC scheme and our proposed adaptive scheme, respectively.
their adaptation trajectories of CWmin as shown in
Figure 12. We can observe that the DCWC scheme adopts the constant value of 5 as the optimal value for CWmin, no matter how the channel environment may
vary. In contrast, our scheme adaptively tunes CWmin
with regard to channel conditions, and it is shown that the optimal value change from 5 to 4 and 3 consecu-tively in different channel situations.
The performance difference can also be clearly shown in Figure 13 which presents the Jain utility fairness index Γ with the DCWC scheme and the pro-posed scheme, respectively. It is shown that Γ with the DCWC scheme decreases from 99.9 to 98.3% and 88.2% sequentially as the communication environment deteriorates. With our scheme, it is shown that Γ converges to 99.9, 99.8, and 99.9% sequentially at the adaptation sequence of 8, 11, and 16, respectively. The results demonstrate that our cross-layer approach can sense the change of WLAN environments (i.e., channel conditions) and adjust system parameters ac-cordingly to achieve fairness under varying WLAN environments.
Scenario V: diverse bandwidth requirements
Finally, we present the simulation results which demon-strate that our scheme can effectively provide weighted fairness according to users’ bandwidth requirements. The simulation set-up for this scenario considers eight downlink flows and eight uplink flows under ideal chan-nel conditions, and assumes that the bandwidth require-ment of a downlink flow is two times that of an uplink flow. Figure 14 presents per-flow throughput for uplink and downlink with 802.11 DCF and the proposed adap-tation scheme, respectively. It is shown that with 802.11 DCF, the downlink throughput is 91 kbps while the up-link throughput is 731 kbps; the ratio of downup-link throughput to uplink throughput is about 1:8 (the corre-sponding Γ = 56.2% as shown in Figure 15), which is far
from the target value 2:1 aforementioned. With the pro-posed scheme, the downlink throughput gradually increases and converges to 548 kbps at the seventh adaptation sequence, while the uplink throughput grad-ually decreases and comes to 285 kbps; the ratio of downlink throughput to uplink throughput is 1.92:1 (Γ = 99.9%), which can nearly meet the desired band-width demands between downlink and uplink, 2:1. The results demonstrate that our adaptation scheme can ef-fectively provide weighted fairness between uplink and downlink according to users’ bandwidth requirements in WLAN environments.
Conclusion
In this article, we investigate a fairness issue between up-link and downup-link flows in IEEE 802.11 WLANs. We propose a cross-layer adaptive algorithm to achieve both per-flow fairness and weighted fairness based on a feed-back control mechanism which dynamically adjusts the contention window size of AP according to the dynamics of WLAN environments such as the numbers of traffic
Figure 12 In Scenario IV, the adaptation trajectory of CWmin
with the DCWC scheme and our proposed adaptive scheme, respectively.
Figure 13 In Scenario IV, the Jain fairness index with the DCWC scheme and our proposed adaptive scheme, respectively.
Figure 14 In Scenario V, the per-flow throughput of uplink and downlink traffics with 802.11 DCF and the proposed adaptive scheme, respectively.
flows, channel conditions, application-layer bandwidth demands, etc. The simulation results demonstrate that our scheme can effectively provide both per-flow fairness and weighted fairness in a varying WLAN environment.
The proposed cross-layer algorithm has to compre-hend the application layer information, i.e., users’ bandwidth demands for the provision of weighted fairness. Thus it is required for our algorithm to im-plement a mechanism which governs the exchange of application-layer contexts between mobile stations and AP. We will keep this issue as future work. Competing interests
The authors declare that they have no competing interests. Acknowledgements
This work was supported in part by Taiwan National Science Council under Grant 99-2221-E-003-005 and 100-2221-E-003-020.
Received: 13 February 2012 Accepted: 12 July 2012 Published: 31 July 2012
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doi:10.1186/1687-1499-2012-239
Cite this article as: Wang: Achieving per-flow and weighted fairness for uplink and downlink in IEEE 802.11 WLANs. EURASIP Journal on Wireless Communications and Networking 2012 2012:239.
Figure 15 In Scenario V, the Jain fairness index with 802.11 DCF and the proposed adaptive scheme, respectively.
