17-12-2012, 06:33 PM
Achieving per-flow and weighted fairness for uplink and downlink in IEEE 802.11 WLANs
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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 perflow
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.
Introduction
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.
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 Markov chain
model [3]. Figure 1 shows the transmission scenario where there are Nd mobile stations
with downlink 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.
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
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 applications, etc.
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 [31] which has been developed by extending a verified twodimensional
Markov chain model proposed by Bianchi [3]. However, our analytical
model takes into account more realistic factors, including error-prone channels, multiple
data rates, the finite retransmission limit, etc.