Supporting QoS in IEEE 802.11e wireless LANs over fading channel
Introduction
One of the channel access mechanisms in IEEE 802.11 is Distributed Coordination Function (DCF). DCF is based on CSMA/CA.1 In this mechanism, a station waits for a quiet period in wireless media, and then begins to transmit data while detecting collisions. The time lapse between successive carrier senses, when channel is occupied, is given by a back-off counter which has an initial random value within a predetermined range. DCF does not support any type of priority access to the wireless medium. As a consequence, DCF provides only best-effort service, and there is no mechanism to provide better service for real-time traffic. Another drawback against QoS provisioning is that the 802.11 MAC does not specify any admission control mechanism. This implies that under heavy traffic load the performance deteriorates in an uncontrollable manner. To address these deficiencies, the “IEEE 802.11 Task Group E” recently has proposed a new contention-based channel access method called EDCA for the IEEE 802.11e standard [1].
Many research efforts have been done to study the IEEE 802.11 DCF performance, by both of analysis and simulation. Most of them assume the ideal channel condition, which means that the packet corruptions are only due to collisions [2], [3], [4].
In [5], based on the IEEE 802.11 DCF, a novel scheme named DCFf is proposed to improve the performance of Wireless Local Area Network (WLAN) in fading channel.
Several priority studies have been reported in the literature for EDCA. In [6] three approaches are proposed; first, dynamic ReAllocative Priority (ReAP) scheme, wherein the priorities of packets in the MAC queues are not fixed, but keep changing dynamically. It uses the laxity and the hop length information to decide the priority of the packet. Second, Adaptive-TXOP2 (A-TXOP), that involves modifying TXOP interval dynamically based on the packets in the queue. Third, TXOP-sharing, that uses TXOP to transmit to multiple receivers in order to utilize the TXOP interval completely.
The method mentioned in [7] is based on the network condition. It gradually lessens the contention window, instead of resetting the initial window value to directly, to avoid continuous collisions.
The authors of [8] have proposed a method called MEDCF.3 This method adjusts the contention window dynamically according to the average collision rate, which is an indication of the traffic load.
The proposed model in [9] distinguishes internal collision from external collision. It also differentiates the two cases when the backoff counter decreases, i.e. an arbitration interframe space (AIFS) period after a busy duration and a time slot after the AIFS period.
The authors of [10] have proposed a three-dimensional Markov chain model for the EDCA mode, and based upon this Markov model, they have computed the throughput that different traffic classes can sustain, and the distribution of the channel-access delay that each head-of-line (HOL) packet experiences when the network is heavily loaded.
In [11] a method has been proposed to modify the contention behaviors of voice and data nodes. For a contending voice (or data) node, after waiting for the channel to be idle for an AIFS[AC voice] (or AIFS[AC data]), the node will send a black burst to jam the channel. The length of the black burst is equal to the node backoff timer.
In [12], an analytical model is proposed to evaluate the performance of the 802.11e in the saturated case.
The authors in [13] analyze the timing behavior of the EDCA function, when it is used to support real-time traffic.
In [14], the authors evaluate, through simulations, the benefits derived from the adoption of the IEEE 802.11e standard in delivering multimedia over WLANs.
To contemplate call admission control in EDCA, Ref. [15] proposes two mechanisms. The idea is, when accepting a new real-time flow, the admission control algorithm considers its effect on the channel utilization and the delay experienced by existing real-time flows, ensuring that the channel is not overloaded and the delay requirements are not violated. At the same time, the rate control algorithm allows the best effort traffic to fully use the residual bandwidth left by the real-time traffic. In this study the channel is assumed to be perfect with no packet loss due to channel fading.
In this paper, first we develop an analytical model to derive an average delay and loss estimate for 802.11e over fading channel. This study leads us to propose a modification to the MAC protocol to bring about better performance. As the second contribution, an adjustment for the maximum number of retransmissions is proposed to maintain the QoS requirements of the real-time traffic sources.
The remainder of this paper is organized as follows. In Section 2, we give a brief introduction to IEEE 802.11e EDCA. The effect of channel failures on EDCA performance is explained in Section 3. The average transmission delay in a fading channel is analyzed in Section 4, and the loss probability is evaluated in Section 5. We then present our proposals for MAC protocol and maximum number or retransmissions adjustment in Section 6. In Section 7, the performances of the proposed schemes are evaluated through simulation studies. Finally, Section 8 concludes this paper.
Section snippets
EDCA overview
The legacy IEEE 802.11 DCF is base on CSMA/CA. DCF defines a basic access mechanism and an optional RTS/CTS4 mechanism. In the DCF a station with a frame to transmit monitors the channel activities until an idle period equal to a distributed inter-frame space (DIFS) is detected. After sensing an idle DIFS, the station waits for a random backoff interval before transmitting. The backoff timer is decremented in terms of timeslots as long as the channel is sensed
Effect of channel failure
One of the challenging problems in wireless networks is the failure of transmitting media due to fading. In this section the back-off procedure in presence of channel fading is investigated.
As we stated previously, there are four ACs with priority levels i = 0, 1, 2, 3. Let and be the minimum and maximum contention windows for priority i queue which have the following relationship:where m is the maximum number of stages allowed in the exponential backoff
Delay analysis
In this section we analyze and compare the effect of channel failure due to fading on EDCA transmission delay for DCWCF and CAFD.
The average delay for the packet transmissions is:where is the average MAC layer service time and is the average waiting time in the queue i. Considering the G/G/1 model for the queuing system, the average waiting time for a packet in the queue can be approximated as:where is the traffic intensity, and are, the
Loss probability analysis
A packet is dropped if retransmission fails. The probability of failure in DCWCF is equal to , while in CAFD this probability is equal to . Therefore the loss probability in DCWCF can be obtained as:And in CAFD:
These two equations show another advantage of CADF; the loss probability for CADF, as depicted in Fig. 4, is less than DCWCF. Comparing the above equations, the failure probability in each channel access attempt is higher for DCWCF.
Adjusting the maximum number of retransmissions
The maximum jitter, average delay and the loss probability have direct relation with the maximum number of retransmissions (). Smaller values of results in the packet loss rate increase, but the average transmission delay and the maximum jitter decrease. Thus we propose different values of for loss sensitive, and delay-jitter sensitive traffic types. It is logical to choose small for real-time traffic which is sensitive to delay and jitter, and large for loss sensitive non real-time
Simulation results
To investigate the performance of the proposed methods, we conduct our simulations in OPNET Modeler 10.5 for mix of real-time and non-real-time traffic. The AIFS and CW parameters for all the simulations are set as followed: and .
Conclusion
In this paper we evaluated the performance of EDCA against fading channel. To enhance the 802.11e performance for the delay sensitive and loss sensitive traffic types, we proposed a new treatment for channel failures called CAFD. Delay analysis of the traditional (DCWCF) and the proposed method (CAFD) is presented. The OPNET simulations supported our analytical conclusion that the quality of different services can be improved by CAFD. Moreover we demonstrated by simulations that appropriately
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