Intelligent MAC model for traffic scheduling in IEEE 802.11e wireless LANs

Applied Mathematics and Computation 205 (2008) 109–122

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Applied Mathematics and Computation

journal homepage: www.elsevier .com/ locate/amc

Intelligent MAC model for traffic scheduling in IEEE 802.11e wireless LANs

Rongbo Zhu a,*, Jiangqing Wang a, Maode Ma b

a College of Computer Science, South-Central University for Nationalities, 708 Minyuan Road, Wuhan 430074, Chinab School of Electrical and Electronic Engineering, Nangyang Technological University, Singapore 639798, Singapore

a r t i c l e i n f o a b s t r a c t

Keywords:MAC model
IEEE 802.11eQuality of serviceSchedulerWireless local area networks
0096-3003/$ - see front matter � 2008 Elsevier Incdoi:10.1016/j.amc.2008.05.052

* Corresponding author.E-mail addresses: [email protected] (R. Zhu

IEEE 802.11e hybrid coordination function (HCF) medium access control (MAC) protocolaims to provide quality of service (QoS) in wireless local area networks (WLANs), whichincludes a new access mechanism, called enhanced distributed channel access (EDCA),and a polling-based scheme, called HCF controlled channel access (HCCA). In this paper,focusing on the problem of improving QoS in a mixed EDCA and HCCA scenario, an intel-ligent MAC model for traffic scheduling located at the QoS-enhanced access point (QAP)is proposed. The proposed MAC model includes two parts: two status estimators and anadaptive scheduler. The model not only takes into account the requirements of trafficstreams (TSs) such as throughput, delay and priority, but also takes into account currentsystem status. In order to evaluate current system status, decide call admission controland reserve resource accurately, the MAC model employs an unsaturation EDCA modelin EDCA mode and a dynamic HCCA scheduling algorithm in HCCA mode. With the require-ments information sent by TSs and the evaluated system status, the scheduler adaptivelyschedules TSs to transmit frames in the appropriate access mode. Simulation results provethe effectiveness of our approach and demonstrate that the proposed MAC model is effec-tive and significantly improves QoS in terms of throughput and delay metrics.

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1. Introduction

Wireless local area networks (WLANs) hold the promise of providing unprecedented mobility, flexibility and scalability.In the last few years, IEEE 802.11 WLAN [1] has been widely used for its simple deployment and low cost. This trend hasencouraged the development of wireless household appliances by which video/audio signals are transmitted within a housewithout wiring. However, legacy IEEE 802.11 WLAN could not provide quality of service (QoS) assurance. In order to supportbetter service, a new access mechanism, called enhanced distributed channel access (EDCA), and a polling-based scheme,called hybrid coordination function (HCF) controlled channel access (HCCA) were developed by the IEEE 802.11 task groupe [2]. In the new MAC protocol of 802.11e HCF, EDCA is used to provide a prioritized QoS service. With EDCA, frames withdifferent priorities are transmitted using different carrier sense multiple access/collision avoidance (CSMA/CA) parameters.HCCA is used to provide a parameterized QoS service. With HCCA, a station negotiates the QoS requirements of its streamwith the hybrid coordinator (HC). Once the stream is established, the HC allocates transmission opportunities (TXOPs) viapolling, to the station, in order to guarantee the stream’s QoS. 802.11e also provides a reference scheduler [3] to multiplexbetween the two modes of medium access and configure parameters of each mode appropriately.

The performance of the IEEE 802.11e HCF has been comprehensive studied by simulation in many literatures [4,5], whichshow that EDCA is effective to the service differentiation at light load and is well suited for burst traffic flows with unknown

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110 R. Zhu et al. / Applied Mathematics and Computation 205 (2008) 109–122

traffic requirements, and HCCA is effective to provide strict QoS at heavy traffic and is well suited for flows that require guar-anteed channel access and have predictable traffic. Considering that the EDCA contention-based admission control algorithmis open, there are many good attempts [6–8] to improve QoS in EDCA mode. At the same time there have been several per-formance studies and enhancement schemes proposed [9,10] for the HCCA reference simple scheduler, which demonstratethat the reference scheduler is only effective to constant bit rate (CBR) traffic. However, all those studies just concentrate onhow to improve QoS either in EDCA mode or HCCA mode.

In this paper, we focus on the problem of improving QoS of HCF by dynamically scheduling traffic flows to the appropriateaccess mode in a mixed EDCA and HCCA scenario. The proposed scheduler takes into account the requirements of trafficflows such as throughput, delay, priority and current system status. In order to evaluate current system status accurately,the scheduler employs an unsaturation EDCA model in EDCA mode and a dynamic HCCA scheduling algorithm in HCCAmode. By obtaining the requirements sent by traffic streams and the monitoring system status in a real-time manner, thescheduler adaptively schedules traffic flows to transmit frames in the appropriate access mode.

The rest of this paper is organized as follows. In Section 2, the IEEE 802.11e HCF scheme is briefly introduced and relatedwork is reviewed. Section 3 proposes a MAC model and an adaptive scheduler to improve QoS in detail. Section 4 validatesthe efficiency of the proposed schemes by simulations. Finally, Section 5 concludes the paper.

2. Preliminaries

2.1. Review of IEEE 802.11e hybrid coordination function

IEEE 802.11e HCF includes two access modes: EDCA and HCCA. The access point (AP) and those stations (STAs) that imple-ment the QoS facilities are called QoS-enhanced AP (QAP) and QoS-enhanced STAs (QSTAs), respectively. EDCA is designed toprovide prioritized QoS. There are four different access categories (ACs) in EDCA and eight user priorities for all traffic flows.Each data packet from the higher layer along with a specific user priority value (from 0 to 7) should be mapped into a cor-responding AC. In EDCA, the arbitrary interframe space (AIFS) is introduced in place of distributed interframe space (DIFS) inIEEE 802.11 distributed coordination function (DCF). Each AC behaves as a single DCF contending entity with its own con-tention parameters (CWmin[AC], CWmax[AC], AIFS[AC] and TXOPLimit[AC]), which are announced by the QAP periodicallyin beacon frames. Basically, the smaller the values of CWmin[AC], CWmax[AC], and AIFS[AC], the shorter the channel accessdelay for the corresponding AC and the higher the priority for access to the medium. In order to solve internal collisions ofdifferent ACs in one QSTA, a virtual scheduler in each QSTA allows the highest priority AC to transmit frames. An EDCA con-tention-based admission control mechanism is suggested in 802.11e. Each AC in a QSTA transmits a QoS request to the QAPcontaining a traffic specification (TSPEC) of its application. When the QAP receives the request, it decides whether to acceptthe request and calculates the amount of time for admitted traffic to access the medium. The algorithms used by the QAP tomake an admission decision and calculate the medium time are open.

In order to provide parameterized QoS support, HCCA has been proposed in 802.11e. In HCCA different traffic classescalled traffic streams (TSs) are introduced. HCCA uses a QoS-aware hybrid coordinator (HC), which is typically located atthe QAP in infrastructure WLANs. HC uses point interframe space (PIFS) to gain control of the channel and then allocatesTXOPs to QSTAs, which are referred as HCCA TXOPs or polled TXOPs. A TXOPLimit is used to bound the transmission timeof a polled QSTA. HCCA can poll the QSTAs during both contention free periods (CFPs) and contention periods (CPs). To leaveenough space for EDCA, a variable TCAPLimit is used to limit the maximum duration of HCCA in a beacon interval. Fig. 1 showsan example of an 802.11e beacon interval.

In the reference scheduler of HCCA, the QAP calculates a ratio of the transmission time reserved for HCCA of all existingQSTAs over a service interval (SI) using the TSPEC information. In order to decide whether or not a request from a new traffic

Beacon

QoS CF-poll

Polled TXOP

RTS/CTS/fragmented data/ACK (polled by HC)

TBTT

Optional contention-free period(polling throughp HCF)

CF-end QoS CF-poll

EDCATXOP

EDCATXOP

RTS/CTS/data/ACK(after contention)

RTS/CTS/fragmented data/ACK (polled by HC)

Polled TXOP

Transmittedby HC

Transmittedby stations

Beacon

TBTT

Contention period(EDCA and polling through HCF)

time

802.11e periodic super frame

Fig. 1. An example of an 802.11e beacon interval.

R. Zhu et al. / Applied Mathematics and Computation 205 (2008) 109–122 111

flow can be accepted in HCCA, the QAP scheduler only needs to check if the new request TXOP plus all the current TXOPallocations are lower than or equal to the maximum fraction of time that can be used by HCCA.

2.2. Related work

2.2.1. EDCA improvementIn order to protect active QoS flow, Xiao and Li [6] proposed a distributed admission control scheme for EDCA, which used

AP to announce the transmission budget via beacon. However, such scheme does not provide direct relationships betweenTXOP parameters and QoS requirements from applications. At the same time, such scheme can only protect existing flows atnot very heavy traffic load and refuse the access request of the new arrival traffic flow, which could not utilize network re-sources maximally. In order to provide a simple admission control mechanism to avoid congestion, Zhang and Zeadally pro-posed the HARMONICA scheme [7], which employed statistical parameters such as drop rate, link-layer end-to-end delayand throughput to indicate system status. The HARMONICA shows that through dynamically adjusting channel accessparameters, it is possible to simultaneously match the QoS requirements, maximally utilize network resources, and guaran-tee a minimal bandwidth for best effort traffic. However, how to find the optimal increment or decrement of the channelaccess parameters is still unknown. Another problem is it introduced so many parameters that the threshold values are dif-ficult to set. Rindhani et al. [13] proposed a simple tuning mechanism called AEDCA, which tuned the contention window(CW) sizes for different classes adaptively by monitoring the QSTAs collision rates. However, it is limited to improvethroughput only by tuning CW sizes for decreasing CW sizes also increasing collision probability simultaneously especiallyat heave traffic load case. In order to estimate network status accurately, Pong and Moors [11] proposed an admission controlscheme based on a Markov model. Such scheme utilized collision statistics of each flow to predict the achievable throughputand the estimation of achievable throughput for each data flow based on the 802.11 analytical model proposed in [12]. How-ever, the analytical model is derived under saturation conditions, where each QSTA always has packets to transmit. Thus, ifthe load is very light, the analytical model is inaccurate. In our previous work [8], an admission control scheme called MBACthat performs real-timely at medium access control (MAC) layer was proposed for the decision of accepting or rejecting re-quests for adding traffic streams to an IEEE 802.11e EDCA WLAN. The admission control strategy is implemented in accesspoint (AP), which employs collision probability and access delay measures from active flows to estimate throughput andpacket delay of each traffic class by the proposed unsaturation analytical model.

2.2.2. HCCA improvementThere have been several performance studies and enhancement schemes proposed [9,10] for the IEEE 802.11e reference

simple scheduler. In order to overcome the unfair for variable bit rate (VBR) flows, Ansel et al. [9] proposed a new HCF sched-uling algorithm called FHCF that used queue length estimations to tune its time allocation to QSTAs. Although the schedulerimproved the fair for VBR traffic, in heavy traffic load, in order to be fair for all flows, it will reduce every flow’s TXOP dura-tion in certain percentage, which will increase delay and decrease throughput for every traffic flow. What is more, such sta-tistic estimation method cannot calculate TXOP duration accurately. In [10], a nice framework for supporting a much moreflexible scheduling scheme is proposed, called estimated transmission times–earliest due date (SETT–EDD) algorithm, whichutilized a TXOP timer to allocate variable length TXOPs and poll each QSTA at variable and different service intervals. TheSETT–EDD algorithm is very simple. However, the TXOP durations were calculated by estimating the generated traffic basedon TSPEC values and the time internal between two consecutive transmissions of the same QSTA. Another problem is that theSETT–EDD will abruptly degrade performance when the traffic load is heavy.

2.3. Contribution

There are so many attempts to improve QoS in each access mode, however, all those studies just concentrate on how toimprove QoS either in EDCA mode or HCCA mode. Our approach can be considered to be a convergence between these twothreads of research. However, it improves the QoS on both sides in terms of throughput and delay. Considering that in theIEEE 802.11e reference scheduler [3], the QAP maintains a clear separation between the EDCA and HCCA periods. This paperproposes an adaptive hybrid coordination function scheduler (AHCFS) to schedule traffic flows to transmit frames in theappropriate access mode. In the AHCFS scheme, in order to accurately estimate the current load in the EDCA and HCCA peri-ods, an unsaturation EDCA model and a dynamic HCCA scheduling algorithm will be employed. For traffic streams that EDCAfails to provide required QoS, the AHCFS attempts to allocate time for them in HCCA period. For the HCCA flows when HCCAperiod cannot satisfies their requirements, the AHCFS encourages HCCA flows to transmit in EDCA period if their require-ments can be satisfied at current status. The proposed scheme breaks the clear separation between the EDCA and HCCA peri-ods and completely utilizes available resource to improve system performance.

3. Intelligent MAC model for adaptive scheduling

In this section, an intelligent MAC model for adaptive scheduling will be introduced, which employs an adaptive hybridcoordination function scheduler (AHCFS) to schedule traffic flows to transmit frames in the appropriate access mode.


3.1. Proposed MAC model

In the content of the WLAN where each QSTA only has a partial view of the network, it is very important for each QSTA toestimate the system status by an accurate method. The proposed intelligent MAC model is shown as in Fig. 2, which can eval-uate system status accurately and is made up of two elements: EDCA estimator and HCCA estimator. The EDCA estimatorcollects some parameters of EDCA flow in real-time in EDCA mode to evaluate the system status. The HCCA estimator mea-sures system status in HCCA mode in each beacon interval. Then the two estimators provide information to the schedulerAHCFS. Based on the information the scheduler will adaptively decide call admission control and reserve resource accuratelyand schedule traffic to transmit frames in appropriate mode.

3.2. Estimator in EDCA

In order to indicate the system status accurately, we will employ the discrete time Markov chain model in our previouswork [8] capable of analyzing the performance of the EDCA at both unsaturation and saturation cases.

3.2.1. Proposed model for system status estimationThe key idea of the model in [8] is that the proposed model does not assume that each priority class always has packets to

transmit, and it depicts packets arrive to the priority class according to a Poisson process with rate k packets. To depict theEDCA scheme accurately, it employs ‘Idle’ and ‘Ft’ states. The ‘Idle’ state is the state in which the queue does not have anypacket to transmit. The ‘Ft’ state represents the first transmission of a packet after the ‘Idle’ state if the channel is sensed idleimmediately after receiving a packet. Post-backoff process (�1,Wi,j) is added, which represents the backoff procedure thatthe station enters in while its queue is empty after transmitting or dropping a frame. There are N priorities class in the sys-tem. Let ni denote the number of active queues in the priority i class. Assumption in a clear channel environment, then wecan get the model’s non-zero one-step transition probabilities as follows:

Pfj; kjj; kg ¼ pb; j 2 ½0;mi þ r�; k 2 ½1;Wi;j � 1�; ð3:1ÞPfj; kjj; kþ 1g ¼ 1� pb; j 2 ½0;mi þ r�; k 2 ½1;Wi;j � 1�; ð3:2ÞPf�1; kj � 1; kþ 1g ¼ 1; k 2 ½1;Wi;0 � 1�; ð3:3ÞPf0; 0j � 1;1g ¼ 1� PfIj � 1g; ð3:4ÞPfIdlej � 1;1g ¼ PfIj � 1g; ð3:5ÞPf0; kjj;0g ¼ ð1� piÞð1� qÞ=Wi;0; j 2 ½0;mi þ r � 1�; k 2 ½0;Wi;0 � 1�; ð3:6ÞPf0; kjmi þ r; 0g ¼ ð1� qÞ=Wi;0; k 2 ½1;Wi;0 � 1�; ð3:7ÞPf�1; kjj;0g ¼ ð1� piÞq=Wi;0; j 2 ½0;mi þ r � 1�; k 2 ½1;Wi;0 � 1�; ð3:8ÞPf�1; kjmi þ r;0g ¼ q=Wi;0; k 2 ½1;Wi;0 � 1�; ð3:9ÞPfj; kjj� 1;0g ¼ pi=Wi;j; j 2 ½1;mi�; k 2 ½0;Wi;j � 1�; ð3:10ÞPfFtjIdleg ¼ PfFjIg; ð3:11ÞPf0; kjIdleg ¼ PfBjIg=Wi;0; k 2 ½0;Wi;0 � 1�; ð3:12ÞPf�1; kjFtg ¼ ð1� piÞq=Wi;0; k 2 ½1;Wi;0 � 1�; ð3:13ÞPf0; kjFtg ¼ pi=Wi;0; k 2 ½0;Wi;0 � 1�; ð3:14Þ

where the probability pi(i 2 [0,N � 1]) is a transmission from a station in the priority i class collides in any time slot, mi is thepriority i class backoff state and r is retransmissions, pb denote the probability that the channel is busy, q is the probabilitythat the station enters in backoff with exactly one packet to transmit and no new packets arrive until current packet is

EDCA estimator HCCA estimator

Adaptive HCF Scheduler (AHCFS)

Parameters of EDCA flowsParameters of HCCA flows

MAC model

Call admission control and traffic scheduling

Fig. 2. Model of IEEE 802.11e MAC.


transmitted successfully, P{FjI} is the probability that the channel is sensed idle after receiving a packet from the ‘Idle’ state,P{BjI} is the probability that the channel is sensed busy after receiving a packet from the ‘Idle’ state and P{Ij�1} is the prob-ability that station enters in ‘Idle’ state from the post-backoff state.

Following the deducing, we can get si, the probability that a station subscribing to the priority i class transmits a packet ina slot time, pi and pb:

si ¼Xmiþr

j¼0

bi;j;0 þ PFt ¼1� pmiþrþ1

i

1� pibi;0;0 þ PFt; i 2 ½0;N � 1�; ð3:15Þ

pi ¼ 1� ð1� siÞni�1YN�1

j¼0;j–i

ð1� sjÞnj ; i 2 ½0;N � 1�; ð3:16Þ

pb ¼ 1�YN�1

j¼0

ð1� sjÞnj ; i 2 ½0;N � 1�: ð3:17Þ

Let Si (i 2 [0,N � 1]) denote the normalized throughput for the priority i class. Let Ptr(i) (i 2 [0,N � 1]) be the probability thatthere is exactly one transmission from the tagged station in the service i class in the considered slot time. Let ps,i

(i 2 [0,N � 1]) denote the probability that a successful transmission occurs in a slot time for the priority i class. We have

PtrðiÞ ¼ sið1� siÞni�1YN�1

j¼0;j–i

ð1� sjÞnj ; ð3:18Þ

ps;i ¼ niPtrðiÞ; ð3:19Þ

Si ¼ps;iEðLÞ

ð1� pbÞrþ ps;iTs;i þ ðpb � ps;iÞTc;i; ð3:20Þ

where Li denotes payload size of priority i class. Assumption that for each priority class, payloads of all transmissions are thesame fixed size. For RTS/CTS mechanism and the basic access method, the values of Ts,i, Tc,i can be calculated as follows:

Tbass;i ¼ H þ lþ ACKþ 2dþ SIFSþ AIFS½i�;

Trtss;i ¼ H þ lþ RTSþ CTSþ ACKþ 4dþ 3SIFSþ AIFS½i�;

(ð3:21Þ

Tbasc;i ¼ H þ lþ AIFS½i� þ d;

Trtsc;i ¼ RTSþ AIFS½i� þ d;

(ð3:22Þ

where H is the time to transmit the header including MAC header and physical layer (PHY) header, l is the time to transmitthe payload, ACK is the time to transmit the acknowledgement (ACK) packet, AIFS[i] is the time of AIFS of priority i class, RTSis the time to transmit the packet RTS, CTS is the time to transmit the packet CTS and d is the time of the propagation delay.

The packet delay for a priority i class is composed of access delay and transmission delay. By using G/G/1 model to com-pute the expect packet delay, the access and transmission delays can be viewed as waiting time in queue and service time,respectively. So the access delay for priority i class packet satisfies

EðADiÞ 6kðr2

ADiþ r2

TiÞ

2ð1� kE½Ti�Þ; ð3:23Þ

where r2ADi

is variance of packet inter-arrival times and r2Ti

is variance of transmission delays for priority i class packets,respectively. E(Ti) is average transmission delay for priority i class packet, which is the time interval from the packet beingready for acquiring the channel access to the packet being successfully received by the receiver.

Let Bi represent the total number of backoff slots without considering the freezing case for the priority i class. We can get

EðBiÞ ¼Xmiþr

c¼0

pci ð1� piÞ

1� pmiþrþ1i

Xc

d¼0

Wi;d � 12

þ qXmiþr

j¼0

bi;j;0Wi;�1 � 1

2: ð3:24Þ

Let Fi denote the total slots number of counter freezing the priority i class. We have

EðFiÞ ¼pb

1� pb

Xmiþr

c¼0

pci ð1� piÞ

1� pmiþrþ1i

Xc

d¼0

Wi;d � 12

: ð3:25Þ

Let E(Ri) represent the average number of retries for the priority i class. We can get

EðRiÞ ¼Xmiþr

c¼0

cpci ð1� piÞ

1� pmiþrþ1i

: ð3:26Þ

Then we can get E(Ti):

EðTiÞ ¼ EðBiÞdþ EðFiÞ½ps;iTs;i þ ðpb � ps;iÞTc;i� þ EðRiÞðTc;i þ TWÞ þ Ts;i; ð3:27Þ


where TW is the waiting time:

TbasW ¼ SIFSþ TACKtimeout;

TrtsW ¼ SIFSþ TCTStimeout:

(ð3:28Þ

Then we can get the expected packet delay for a priority i class as

EðDiÞ ¼ EðADiÞ þ EðTiÞ: ð3:29Þ

3.2.2. Scheduling procedureThe proposed AHCFS scheme guarantees a uniform QoS level in terms of maximum tolerable packet delay Dmax,i and re-

quired bandwidths St,i for all real-time traffic. There are two criteria are applied to the proposed scheme. The first criterion isthat the scheme admits a new real-time flow only if the requested resource is available, namely the required bandwidths ofall traffic class are satisfied and the expected packet delay for each priority class will not exceed the maximum tolerablepacket delay. The second criterion is that the QoS provided for the currently existing real-time flows are not affected. Thiscan be guaranteed as long as the first criterion is satisfied.

We use some MAC management messages specified in IEEE 802.11e to transmit the required information. Each QSTA firsttransmits a request message to the QAP before transmitting a TS, which contains the traffic class information of the streamand the MAC address of this QSTA. The QAP collects information of load conditions from each QSTA to estimate the radioperformance. Each active flow needs a counter to keep track of the collision rate, which is calculated every update period.This update interval can be tuned to achieve an optimal effect and efficiency. In our implementation, we adopt the super-frame duration as the update interval. To minimize the bias against collision rate, a factor a is introduced to smoothenthe estimated values. Then the collision rate is estimated as

piðnÞ ¼ ð1� aÞpi þ apiðn� 1Þ: ð3:30Þ
When there is a new request TS of priority i class, the AHCFS located in QAP performs the following policies:
Step 1. The new request TS of priority i class transmits a REQUEST message to the QAP, which contains requirements Dmax,i

and St,i.Step 2. The AHCFS uses the above proposed non-saturation model to calculate si and throughput and expected delay to seeif the QoS requirements for current all QSTAs (exclude the new arrival TS) can be guaranteed by the receiving collisionrate information from each active flow. Then if the current QoS requirements cannot be guaranteed in EDCA period,the AP will check that there is time available in HCCA period. If there is no time in HCCA period too, the AHCFS will trans-mit a REJECT message to reject the new arrival request, else continues.Step 3. If the current QoS requirements can be guaranteed in EDCA period, the AHCFS uses the above proposed non-sat-uration model to calculate throughput and expected delay to see if the QoS requirements for all QSTAs can be guaranteedwhen the new TS is permitted into the system. For each active flow if E(Di) 6 Dmax,i and St,i P St,i the request will beaccepted and the QAP will respond a ACCEPT message to notify the QSTA of approving the TS into the system in EDCAperiod. If the current QoS requirements cannot be guaranteed in EDCA period and there is enough time available in HCCAperiod, the AHCFS will allocate appropriate TXOPs (the scheme will introduced in the next subsection) to satisfy currentrequirements and respond a ACCEPT message to notify the new arrival TS. Otherwise, the QAP transmits a REJECTmessage.Step 4. If the new arrival TS receives a ACCEPT message, it can transmit packets in corresponding access mode. As thetransmission queue of the TS gets empty, it will transmit a DELETE message to notify the QAP. Upon receipt of the DELETEmessage, the QAP responds an ACK to confirm the receipt. Such method will save resource for the active flows or otherrequest TSs and make AHCFS estimate load conditions accurately. Meanwhile the TSs encouraged transmitting in HCCAmode also can switch to EDCA mode when the traffic load comes to light in EDCA period. If the TS receive a REJECT mes-sage, it will send REQUEST message again after some random waiting time.

3.3. Estimator in HCCA

In order to allocate the TXOP duration accurately, it is important that the AHCFS in QAP be aware of the amount of queuedtraffic of the TSs in the polled QSTA. This knowledge can be given to the scheduler by the QSTA with the following mecha-nism. Noting that each QoS data frame has a new field named QoS control field and 802.11e allows data to be send ‘‘piggy-backed” on polls to reduce overhead, the scheduler is informed about the amount of queued traffic that belongs to a TS bypiggybacking this information with the QoS data frames transmitted by QSTAs. It is obvious that such piggybacking methodcan improve overall network performance. The QSTA can employ the QoS control field to send current traffic load informa-tion, which just includes the amount of buffered frames for each TS and the QoS frame indicates its TS and the size of thecorresponding queue in the QSTA, to the scheduler. Which means that the proposed scheduling scheme determines the TXOPduration based not on estimations but on current traffic load information sent by each TS. It is obvious that the scheduler canaccurately allocate an appropriate TXOP duration to satisfy traffic requirements.


We employ such parameters: Maximum MSDU Size (M), Maximum Burst Size (MBS), Minimum PHY Rate (R), Mean DataRate (q), Maximum Delay Bound (MD), Peak Data Rate (PR), User Priority (UP) which go from 0 (lowest) to 7 (highest), Min-imum TXOP Duration (mTD), Maximum TXOP Duration (MTD), Nominal MSDU Size (L), Minimum Service Interval (mSI),Maximum Service Interval (MSI), where the mTD, MTD and mSI calculate as

mTDk ¼maxMi

Ri

� �; i 2 ½1;n�; k 2 ½1;N�; ð3:31Þ

MTDk 6

PNi¼1MBSs

R; k 2 ½1;N�; ð3:32Þ

mSIk ¼minLi

q

� �; i 2 ½1;n�; k 2 ½1;N�; ð3:33Þ

where n is the number of TSs in a QSTA k, N is the number of QSTA.For TXOP allocation with the proposed scheduler, at time tc, the scheduler at AP received buffered frames information Rc,i

(i 2 [0,n � 1]) from the QSTA k, which includes the amount of buffered frames in each TS queue. Then such requirementswould be satisfied by the assigning time tc. At the same time new data will generate between tc and tc+1 in QSTA k, whichincludes the newly arrived data DNA,i from upper application layer and the data DNT,i that were not transmitted successfullyduring the duration TXOPk,c. DNT,i can be retransmitted at next TXOP duration TXOPk,c+1. However, for each TCi (i 2 [0,n � 1]),such data DNA,i that generated between the time request information Rc+1,i and tc+1 can only be transmitted at the futureTXOP duration TXOPk,c+2 or other future TXOP duration because Rc+1,i impossibly includes the unknown upcoming dataamount.

For convenience we consider the worst case that the time request information Rc+1,i transmitted equals to tc. Consideringthe frame deadline and maximum retransmission time K at the same time, the worst situation is that the frame deadline ismaximum TXOP duration MTDk and the packet arrived at time tc will be transmitted at the Kth retransmission, namely attime tc+2+K, so we have

Tm 6MSIk; m 2 ½1;K þ 2�;PKþ2

m¼1Tm 6 ð2þ KÞMSIk 6 Dk �MTDk:

8><>: ð3:34Þ

Then we can get

MSIk 6Dk �MTDk

2þ K: ð3:35Þ

With all traffic load information, considering that R is the physical bit rate, the scheduler can calculate TXOP duration TDk

of QSTAk as

TDk ¼Xn

k¼1

max8� RFramei

Rþ C;mTDi

� �; ð3:36Þ

where RFramei is the buffered frames size of TCi queue in byte, C is the overhead due to physical and MAC headers, inter-frame spaces (IFSs), acknowledgement frames, and poll frames.

The scheduler also uses the following equation [9] to check the QSTAs:
t þmSIk 6 t0 6 t þMSIk: ð3:37Þ
The AHCFS located in QAP now works as follows:

Step 1. After QAP entering in the HCCA mode, the AHCFS starts a timer Tcount to count the time spent in HCCA in a beaconinterval. The AHCFS resets Tcount to zero when a new superframe starts.Step 2. The AHCFS collects queue size information of each TS before next TXOP duration assigning time.Step 3. When the channel becomes idle the AHCFS checks the QSTA that the current time satisfies the relation (3.37) andTXOP timer value is greater than mTD or not. If no QSTA satisfies such conditions the scheduler goes to the next time slotand checks the QSTAs again and the AHCFS saves the HCCA status as IDLE, else continues.Step 4. The AHCFS checks that there is enough time in current HCCA mode (Tcount < TCAPLimit). If yes, the AHCFS polls theQSTAs with the high priority and the earliest deadline. Then the AHCFS calculates the TXOP duration TDk using Eq. (3.36)for the QSTA. After allocating TXOP duration, the scheduler goes to next time slot and waits for the channel to be idleagain. Otherwise, The AHCFS indicates the HCCA status is BUSY and checks the EDCA mode status and performs thescheduling process in EDCA mode as described in Section 3.2.2.

4. Numerical and simulation results

The well-known simulation tool NS2 [14] is used to validate the proposed AHCFS scheme. In the simulation system, allQSTAs content to transmit packets to a single non-transmitting QAP. The IEEE 802.11a PHY layer was selected for the sim-ulations. Simulation parameters are as follows in Table 1.

Table 1Simulation parameters

Parameters Value

DIFS, SIFS, PIFS 60u s, 20u s, 40u sSlot time 20u sBeacon internal 100 msDelay bound 60 msMinimum PHY Rate 24 MbpsRetransmission 5MAC header 34 bytesUser Priority class 6(VoIP), 5(Video), 0(Data)Mean data rate 24 kbps(VoIP), 600 kbps(Video)Nominal/maximum MSDU size 60 bytes(VoIP), 1000 bytes(Video, Data)Peak Data Rate 24 kbps(VoIP), 1.2 Mbps(Video) 200 kbps(Data)CAP timer update time 5120u sDot11CAPRate 21u s


4.1. EDCA period scheduling validation

In this scenario, every QSTA has three different priority classes: bi-directional VoIP traffic (priority 6 class, transmittedonly in HCCA period), unidirectional upstream video traffic (priority 5 class) and unidirectional upstream burst data traffic(priority 0 class) that are transmitted in EDCA period firstly. The maximal delay bound of data traffic is 150 ms. The averageinter-arrival times are 10 ms, 3 ms and 15 ms, respectively. Smooth factor a is 0.1. The simulation time is 50 s. In this sce-nario, at 1 s, five QSTAs begin to transmit. Then a new video stream is added every 5 s until the simulation time is larger than26 s.

Figs. 3 and 4 show the throughputs of data and video traffic with and without the AHCFS scheme. It can be noted thatfrom 1 s to 16 s, the throughputs are the same for the system with and without the AHCFS scheme because all TSs’ require-ments can be met. At 16 s, a new video stream tries to access the network. We observe that without AHCFS in Fig. 3, the newvideo stream launches its transmission without any difficulty, though the total throughput of data traffic slightly degrades to0.91 Mbps because the video traffic reaves the bandwidth resource for its higher priority. At 21 s, another new video streamalso tries to access the system. In Fig. 3, the total bandwidth for video only increases to 5.33 Mbps, which indicates that eachvideo stream only occupies about 546 kbps, lower than its requirement. At the same time, the total throughput of the WLANis spoiled, with obvious oscillation. When the new video stream accessed, the throughput is compromised to zero which isvery low. On the other hand, with the implementation of the AHCFS scheme, in Fig. 4, the throughputs of existing streams areprotected. When traffic is very heavy at 21 s, although the new video stream can be accommodated in EDCA period for itshigh priority, the data traffic’s delay will be larger than its maximal delay bound 150 ms. So the new arrival video stream isscheduled to transmit in HCCA period and the throughput of data traffic still keeps 0.91 Mbps.

Figs. 5 and 6 show delays of data and video traffic with and without the AHCFS, respectively. Comparing Fig. 6 with Fig. 5,from 1 s to 21 s, there is no difference in term of delay with and without the AHCFS scheme because all flows requirementscan be met and the delays of all traffic are very low. At 21 s, a new video stream launches the system, which makes the delayof data traffic oscillates obviously and increases abruptly to 153 ms for it can only get 0.33 Mbps without AHCFS scheme.After 26 s, each TS’s delay obviously increases without the AHCFS for the traffic is heavy. Especially for data traffic inFig. 5, its delay is messed up more than 300 ms because video traffic occupies more bandwidth. The delay of video trafficalso obviously increases to 23.2 ms because its actual bandwidth is just 546 kbps, less than its requirement 600 kbps. Onthe other hand we observe that with the AHCFS scheme, the maximum delays of video traffic and data traffic are less than4 ms and 11 ms, respectively, whereas without the AHCFS the maximum delays are higher than 20 ms and 300 ms, respec-tively. From Fig. 6, we can observe that the QoS of video traffic and data traffic are improved in terms of less delay and lessfluctuation with the AHCFS scheme, for those traffic scheduled by the AHCFS to transmit in HCCA period experience less de-lay and less fluctuation for there is enough resource in HCCA period, which also makes the traffic have enough resource inEDCA period.

4.2. HCCA period scheduling validation

In this scenario, the simulation considers unidirectional downstream burst data traffic (transmitted only in EDCA mode),bi-directional VoIP traffic and unidirectional downstream video traffic (transmitted in HCCA mode firstly) in each QSTA. Theaverage inter-arrival times are 10 ms, 3 ms and 15 ms, respectively. Smooth factor a is 0.1. Figs. 7–9 show the packet lossrate (PLR), average transmission delay (ATD) and throughput with the QSTAs number increasing from 2 to 17, respectively.

From Figs. 7 and 8, with the QSTAs number increasing, PLR and ATD of all TSs also increase. In the 802.11e referencescheme, the video traffic’s PLR and ATD are always higher than 22.3% and 36.8 ms, respectively. Especially when the QSTAsnumber is larger than 6, for the 802.11e reference scheduler, the PLR and ATD of video increase abruptly from 24.6% to 53.3%and 42.4 ms to 152.6 ms, respectively. The reason is that the reference scheduler allocates TXOP statically regardless of the

0 10 20 30 40 500

1

2

3

4

5

6

7

Simulation time (s)

Tota

l thr

ough

put (

Mbp

s)

Data trafficVideo traffic

Fig. 4. Total throughput of each traffic class with AHCFS.

0 10 20 30 40 500

1

2

3

4

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Simulation time (s)

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ough

put (

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Data trafficVideo traffic

Fig. 3. Total throughput of each traffic class without AHCFS.

0 10 20 30 40 500

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simulation time (s)

Del

ay (

ms)

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Del

ay (

ms)

Data traffic

Video traffic

Fig. 5. Delay per stream without AHCFS.


0 10 20 30 40 500

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simulation time (s)D

elay

(ms)

Data traffic

0 10 20 30 40 500

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simulation time (s)

Del

ay (m

s) Video traffic

Fig. 6. Delay per traffic stream with AHCFS.

2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 170

10

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QSTAs number

Pac

ket

Loss

Rat

e(%

)

802.11e Video traffic

802.11e VoIPtraffic

AHCFS Videotraffic

AHCFS VoIPtraffic

SETT-EDD Video traffic

SETT-EDD VoIP traffic

Fig. 7. Packet loss rate against QSTAs number.

2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 170

50

100

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250

QSTAs number

Ave

rage

tra

nsm

issi

on d

elay

(ms) 802.11e Video traffic

802.11e VoIP traffic

AHCFS Videotraffic

AHCFS VoIPtraffic

SETT-EDD Video traffic

SETT-EDD VoIP traffic

Fig. 8. Average transmission delay against QSTAs number.


offered load, when the traffic load is heavy, such allocation method cannot satisfies the actual requirements and it cannotaccommodates any requirements at all though the throughput is only 3984 kbps. While in the AHCFS and SETT–EDDschemes, from Figs. 7 and 8 we can see that, when the QSTAs number is less than 6, the PLR and ATD of video traffic are

2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 170

500

1000

QSTAs number

Thro

ughp

ut(K

bps) 802.11e VoIP traffic

AHCFS VoIP trafficSETT-EDD VoIP traffic

2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 170

5000

10000

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QSTAs number

Thro

ughp

ut(K

bps) 802.11e Video traffic

AHCFS Video trafficSETT-EDD Video traffic

Fig. 9. Throughput against QSTAs number.


almost same and are obviously lower than those in the reference scheduler. However, with the QSTAs number increasingmore than 6, the video traffic’s PLR and ATD in the AHCFS are less than those in SETT–EDD. Especially when the videoTSs number is larger than 11, the errors of PLR and ATD between the AHCFS and SETT–EDD increase rapidly from 9.8% to26.3% and from 11.5 ms to 167 ms, respectively. Because with the load increasing, the proposed scheme accurately calculatesTXOP duration by QSTAs requirements, while SETT–EDD scheme estimates the TXOP values on average traffic rates and esti-mations. In the AHCFS all the traffic requirements can be satisfied. Note that when the QSTAs number is larger than 15, theATD and PLR of each traffic in the AHCFS decrease slightly because the AHCFS schedules the video and VoIP TSs to transmit inEDCA period for no available time in HCCA period. It is obviously that those video and VoIP traffic experience low ATD andPLR for there is enough resource in EDCA period.

It also shows that, in the AHCFS the ATD of VoIP traffic is lower than that in the reference scheme when the QSTAs numberis less than 6. This result is due to that the fixed TXOP duration of the reference limits the number of transmissions per ser-vice interval, which causes delay accumulation due to retransmissions. For more than 6 QSTAs, the PLR and ATD of VoIP be-come slightly lower in the reference scheduler. It seems that the reference scheme is more efficient for VoIP traffic in somesituations, but the essential reason is due to the fact that the video traffic PLR is always 22.3%, which gives more availableTXOP time for use by VoIP. While in the AHCFS, the PLR of VoIP is always lower than those in the reference scheme and inSETT–EDD when the QSTAs number is larger than 7, and the ATD of VoIP is almost always lower about 2.5 ms and 4.2 ms onaverage than those in the reference scheme and in SETT–EDD, respectively. Such simulation results also demonstrate that theAHCFS allocating variable length TXOPs by traffic load information and polling each QSTA at variable and different serviceintervals is suitable for CBR traffic for the bursts are smaller for VoIP.

As shown in Fig. 9, the reference scheduler and SETT–EDD scheme can support 8 VoIP TSs and 6 video TSs, 13 VoIP TSs and11 video TSs, respectively. When overload, the reference scheduler keeps the throughputs constant for it allocates TXOP stat-ically regardless of the offered load, while the SETT–EDD degrades the throughputs abruptly for it also allocates TXOPdynamically for the new arrival traffic though the current status is overload. While with the load increasing, the throughputsof all TSs increase linearly and the AHCFS can support all 17 VoIP and 17 video TSs because the AHCFS can monitor systemstatus accurately and adaptively schedule traffic to transmit in appropriate access mode. This demonstrates that the pro-posed scheme is effective and improves the system throughput obviously.

4.3. Mixed scenario scheduling validation

In this scenario, there are two kinds of QSTA: QSTA1 and QSTA2. Each QSTA1 has one unidirectional downstream burstdata TS and one unidirectional downstream video TS, which are transmitted in EDCA mode firstly. Each QSTA2 has one bi-directional VoIP TS and one unidirectional upstream video TS, which are transmitted in HCCA mode firstly. At 1 s, there are 5QSTA1s and 15 QSTA2s began to transmit. Then a new QSTA1 and a new QSTA2 are added every 5 s in each mode until thesimulation time is larger than 16 s. Figs. 10 and 11 show the average throughput and average transmission delay (ATD) ofeach kind of TS with and without the AHCFS scheme.

From Fig. 10, we can see that without the AHCFS the average throughputs of video and VoIP TSs are obviously lower thanthose with the AHCFS. For video traffic without the AHCFS, the throughput almost keeps the constant about 380 kbps. Thereason is that the reference scheduler cannot supports so many video TSs in HCCA mode and the total throughput is about4900 kbps by statically allocating TXOP regardless of the offered load. Although the average throughputs of those video TSsin EDCA mode are about 580 kbps, the average throughput of all video TSs is just about 380 kbps for the reference schedulermaintains a clear separation between the EDCA and HCCA periods. On the other hand, the average throughput of video trafficis always more than 600 kbps with the AHCFS, because the AHCFS dynamically adaptively schedules video traffic in two

0 5 10 15 200

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Aver

age

thro

ughp

ut (K

bps)

Data traffic with AHCFSData traffic without AHCFSVideo traffic with AHCFSVideo traffic without AHCFSVoIP traffic with AHCFSVoIP traffic without AHCFS

Fig. 10. Average throughput of each traffic class with and without AHCFS scheme.

0 5 10 15 200

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Data traffic with AHCFSData traffic without AHCFSVideo traffic with AHCFSVideo traffic without AHCFSVoIP trafficwith AHCFSVoIP trafficwithout AHCFS

Fig. 11. Average transmission delay of each traffic class with and without AHCFS scheme.


access periods, which maximizes the network resources utilization and is beneficial to the burst of VBR traffic. For VoIP TSs,we can see the similar phenomenon that with AHCFS scheme the throughput keeps constant about 24.6 kbps and is alwaysmore than that without AHCFS scheme about 9.3 kbps. From Fig. 10, for data traffic less than 16 s, there is little error withand without the AHCFS because the average throughputs of data TSs are almost same. It seems that the reference scheduleris suitable for data traffic. However, the reason is that the EDCA can satisfy all those traffic requirements in EDCA period.After 16 s, the average throughput of data traffic decreases to less than 100 kbps without the AHCFS because there arenew TSs launces in the system. For the data traffic with AHCFS, although the average throughput decreases to 158.3 kbpsfor video traffic occupy more bandwidth, it is also higher than that without the AHCFS.

In Fig. 11, the ATD of video traffic is about 150 ms without the AHCFS scheme. However, the ATD of video traffic is far lessthan 150 ms with the AHCFS, although it increases to 42.8 ms when time is larger than 16 s. For VoIP traffic, the ATD keepsconstant about 54.1 ms without the AHCFS scheme, while the ATD is less than 19.5 ms with AHCFS scheme. Although theATDs of data traffic are almost same with and without the AHCFS when time is less than 16 s, the ATD abruptly increasesto more than 160 ms without the AHCFS when there is new video traffic launching in system at 16 s, and is more than thatwith the AHCFS about 100 ms. Such phenomena demonstrate the proposed AHCFS scheme can adaptively schedule each kindof traffic by its requirements.

4.4. Simulation results summaries

The summaries of simulation results for different traffic in the scenarios are shown in the following tables. In scenario 1(EDCA period scheduling validation), the results are recorded from simulation time 20 s to 50 s. In scenario 2 (HCCA periodscheduling validation), the results are recorded at 17 STAs case in simulation. In scenario 3 (mixed scenario), the results arerecorded from simulation time 16 s to 20 s. Table 2 shows that with the proposed AHCFS, the maximal and average through-puts of video traffic are obviously higher than those without AHCFS. Especially in scenario 3 (mixed scenario), the maximal

Table 2Summary results of video traffic

Scenario Scheme Video throughput Video delay (ms)

Max Average Max Average

1 (20 s–50 s) With AHCFS 6.13 Mbps 4.67 Mbps 3.1 2.2Without AHCFS 5.34 Mbps 4.11 Mbps 23.2 14.5

2 (17 STAs) With AHCFS – 10013 kbps – 43.1Without AHCFS – 2502 kbps – 150.1

3 (16 s–20 s) With AHCFS 627 kbps 613 kbps 42.5 37.4Without AHCFS 403 kbps 378 kbps 176.1 158.4

Table 3Summary results of VoIP traffic

Scenario Scheme VoIP throughput (kbps) VoIP delay (ms)

Max Average Max Average

1 (20 s–50 s) With AHCFS – – – –Without AHCFS – – – –

2 (17 STAs) With AHCFS – 803 – 37.5Without AHCFS – 383 – 39.4

3 (16 s–20 s) With AHCFS 28.2 24.5 20.3 15.7Without AHCFS 12.7 9.4 67.3 55.8

Table 4Summary results of data traffic

Scenario Scheme Data throughput Data delay (ms)

Max (kbps) Average Max Average

1 (20 s–50 s) With AHCFS – 0.93 Mbps 27 16Without AHCFS – 0.4 Mbps 315 163

2 (17 STAs) With AHCFS – – – –Without AHCFS – – – –

3 (16 s–20 s) With AHCFS 162 157.2 kbps 63.7 48.6Without AHCFS 81 64.5 kbps 203.4 168.5


and average throughputs are about 1.5 times those without AHCFS. The delay of video traffic is greatly lower than that with-out AHCFS. For VoIP and data traffic, we can see similar results as shown in Tables 3 and 4. The results in these tables intu-itively prove that the proposed AHCFS can greatly improve QoS in terms of throughput and delay metrics under eachscenario.

5. Conclusion

In this paper, we proposed an intelligent MAC model to schedule traffic for improving QoS in IEEE 802.11e WLANs. In themixed EDCA and HCCA scenario, with the intelligent MAC model accurately evaluating system status, the scheduler attemptsto improve QoS metrics by allocating TXOP in the HCCA period if there is time available for the EDCA flows when EDCA per-iod cannot satisfies their requirements, and encourages HCCA flows to transmit in EDCA period if their requirements can besatisfied at current status for the HCCA flows when HCCA period cannot satisfies their requirements. Simulation resultsprove the effectiveness of our approach and demonstrate that the proposed MAC model can estimate the system status accu-rately and the scheduler significantly improves the QoS in terms of throughput and delay metrics.

Acknowledgements

This research is partially supported by the Natural Science Foundation of South-Central University for Nationalities underGrant No. YZZ07006. The authors are grateful to anonymous referees for their corrections, suggestions and improvements.

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Intelligent MAC model for traffic scheduling in IEEE 802.11e wireless LANs