Achieving per-flow and weighted fairness for uplink and downlink in IEEE 802.11 WLANs
© Wang; licensee Springer. 2012
Received: 13 February 2012
Accepted: 12 July 2012
Published: 31 July 2012
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.
In recent years, IEEE 802.11 Wireless Local Area Networks (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, between 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 distributed coordination function (DCF) for its distributed and simple manner. With DCF, all 802.11 nodes with packets to send including AP and mobile stations generally 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 downlink generally is much heavier than that of uplink. The unfairness 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 fairness. 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 contribution of this article is that we present a cognitive algorithm 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), and then adapts the system parameters intelligently to achieve per-flow fairness or weighted fairness. We conduct simulations to evaluate the performance of the proposed adaptive algorithm. The simulation results demonstrate that our approach can effectively provide fairness of channel utilization between uplink and downlink 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 article ends with conclusions.
Unfairness problem and related work
The adopted IEEE 802.11b parameter
The fairness problems in IEEE 802.11 WLANs have largely been investigated in previous work[4–29]. The authors of 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 presents a measurement-based 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 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 occupied by AP before the required utilization ratio is matched. The study presents a dynamic contention window control (DCWC) scheme based on the number of downlink flows to achieve per-flow fairness. Nevertheless, it does not consider some dynamics in WLAN environments such as channel conditions and traffic loads that can greatly impact the performance of fairness. The authors of use an analytical approach to find optimal contention window sizes based on the observed idle slot intervals to achieve utility fairness between AP and wireless stations. However, the approaches proposed in may need substantial modifications 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 present the Bidirectional DCF which provides a preferential treatment to downlink traffic by piggybacking 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 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 applied differentiated minimum contention windows (CW) for AP and wireless stations 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 provides a detail analysis of per-flow and per-station fairness for TCP flows. The authors of proposed a differentiated approach which involves multidimensional parameters including minimum 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 propose a cross-layer feedback approach to achieve per-station fairness by estimating each station’s access time and queue length. The study solves the TCP fairness problem by using a dual queue scheme in which one queue is specified for data packets of downlink TCP flows and the other is for ACK packets.
Proposed cross-layer adaptative algorithm
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 CW of AP according to the dynamics of WLAN environments such as the numbers of traffic flows, channel conditions, and application-layer bandwidth demands to achieve both per-flow fairness and weighted fairness.
Architecture of the proposed adaptive cross-layer algorithm
The channel utilization can be affected by both the amount of contending flows and channel conditions, i.e., BER
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 (environmental 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), minimum CW, and TXOP. In this article, we adopt the parameter of minimum CW (CWmin) for the proposed adaptation scheme since it is a key parameter affecting not only the access priority but also the overall system performance.
Contention window adjuster
Another context metric is the ratio of average uplink bandwidth requirements to average downlink bandwidth requirements, ψ. The metric ψ is defined as
where r j u and r k d is 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 periodically 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 fairness, 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 adjust CWmin such that η is equal to 1 to provide per-flow fairness. Alternatively, if ψ 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 to ψ to provide weighted fairness.
where CWmin,i is the CWmin of i th adaptation; Δ i is i th adaptation step size; A is the normalized step size (A > 0). From Equation (4), we can see that the adaptation step size Δ i depends on A, η i and ψ i . If ψ i /η i is far from 1, Δ i will be larger; when ψ i /η i is close to 1, Δ i becomes smaller. Note that the adaptation mechanism can work well to provide fairness in both cases when ψ i /η i is larger or smaller than 1. In general cases when ψ i /η i is smaller than 1, the step size Δ i will be positive to decrease CWmin. It is noticeable that although Δ i can be an arbitrary real number, CWmin must be a positive integer. Thus, the chosen value for CWmin will be rounded to the closest integer. Alternatively, when ψ i /η i is larger than 1, e.g., the amount of uplink flows is rare or bandwidth requirements of downlink are less than that of uplink, Δ i will be negative to increase CWmin. After several adaptation steps when ψ i /η i is close to 1, Δ i will be rather small or even zero, and consequently CWmin will almost keep steady. At this time the adaptation of CWmin converges to the optimal value in terms of the best performance of fairness.
Note that the value of ψ i /η i can vary throughout the whole transmission period due to the dynamics of WLAN environments such as channel conditions, numbers of traffic flows, application-layer bandwidth demands, etc. When ψ i /η i is removed from the target value of 1 due to the change of WLAN environments, the proposed feedback control mechanism aforementioned can automatically tune CWmin to the optimal value with regard to the current situations.
Γ has a range of (0, 1] to evaluate the fairness; the value closer to 1 refers to better performances of fairness. 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 performance of the proposed algorithm. From the simulation results, we demonstrate the effectiveness of our algorithm to provide per-flow fairness between downlink and uplink traffics, and further to provide weighted fairness according to users’ bandwidth requirements. The IEEE 802.11 simulation model was built based on our analytical approach which has been developed by extending a verified two-dimensional Markov chain model proposed by Bianchi. However, our analytical model takes into account more realistic factors, including 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 restriction 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 relative signal strength). We wrote Matlab codes to implement the IEEE 802.11 model and provide numerical results in the simulations. The adopted 802.11b parameters are shown in Table1. 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, and in some scenarios further with that of the DCWC scheme. 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
Scenario II: dynamic flow amounts in the network
Scenario III: equal bandwidth requirements under diverse and time-varying 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 Figure10). When the link quality of downlink flows deteriorates later at the adaptation sequence of 10, the value of CWmin adjusted earlier 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 downlink 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
Scenario V: diverse bandwidth requirements
In this article, we investigate a fairness issue between uplink and downlink 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 feedback control mechanism which dynamically adjusts the contention window size of AP according to the dynamics of WLAN environments such as the numbers of traffic 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 comprehend the application layer information, i.e., users’ bandwidth demands for the provision of weighted fairness. Thus it is required for our algorithm to implement a mechanism which governs the exchange of application-layer contexts between mobile stations and AP. We will keep this issue as future work.
This work was supported in part by Taiwan National Science Council under Grant 99-2221-E-003-005 and 100-2221-E-003-020.
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