QoS provisioning in IEEE 802.11-compliant networks: Past, present, and future

Computer Networks 51 (2007) 1922–1941

www.elsevier.com/locate/comnet

QoS provisioning in IEEE 802.11-compliant networks:Past, present, and future

Hwangnam Kim a, Jennifer C. Hou b,*, Chunyu Hu c, Ye Ge d

a School of Electrical Engineering, Korea University, Republic of Koreab Department of Computer Science, University of Illinois at Urbana-Champaign, 1304 W. Springfield Avenue,

Urbana, IL 61801, United Statesc Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States

d Department of Electrical Engineering, The Ohio State University, Columbus, OH 43210, United States

Received 2 January 2006; accepted 3 July 2006Available online 25 October 2006

Responsible Editor: E. Gregori

Abstract

Proliferation of portable, wireless-enabled laptop computers and PDAs, cost-effective deployment of access points, andavailability of the license-exempt bands and appropriate networking standards contribute to the conspicuous success ofIEEE 802.11 WLANs. In the article, we provide a comprehensive overview of techniques for capacity improvementand QoS provisioning in the IEEE 802.11 protocol family. These techniques represent the R&D efforts both in the researchcommunity and the IEEE 802.11 Working Groups. Specifically, we summarize the operations of IEEE 802.11 legacy aswell as its extension, introduce several protocol modeling techniques, and categorize the various approaches to improveprotocol capacity, to provide QoS (by either devising new MAC protocol components or fine-tuning protocol parametersin IEEE 802.11), and to judiciously arbitrate radio resources (e.g., transmission rate and power). To demonstrate how toadapt QoS provisioning in newly emerging areas, we use the wireless mesh network as an example, discuss the role IEEE802.11 plays in such a network, and outline research issues that arise.� 2006 Published by Elsevier B.V.

Keywords: Wireless local area networks; IEEE 802.11 enhancement and extension; Quality of service; Wireless MAC protocol design andevaluation

1389-1286/$ - see front matter � 2006 Published by Elsevier B.V.

doi:10.1016/j.comnet.2006.07.017

* Corresponding author. Tel.: +1 217 265 6329; fax: +1 217 2446500.

E-mail addresses: [email protected] (H. Kim), [email protected] (J.C. Hou), [email protected] (C. Hu), [email protected] (Y. Ge).

1. Introduction

Thanks to proliferation of portable, wireless-enabled laptops and PDAs, cost-effective deploy-ment of access points, and availability of thelicense-exempt bands and appropriate standards,IEEE 802.11-based wireless Local Area Networks(WLANs) have become popular at an unprecedented

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H. Kim et al. / Computer Networks 51 (2007) 1922–1941 1923

rate. However, IEEE 802.11 has been shown to (i)achieve network capacity (i.e., the throughput thatcan be attained in a given wireless channel) that issignificantly below the theoretically achievablecapacity bound [16], and (ii) suffer from a wide vari-ety of packet delays and cannot provide servicedifferentiation among traffic of different classes. Bothare caused by collisions and subsequent retransmis-sions in the shared wireless medium, and are a directresult of that IEEE 802.11 employs a carrier sensemultiple access mechanism with collision avoidance(CSMA/CA), called Distributed Coordination Func-tion (DCF), as the basic access method. Even withthe floor acquisition mechanism, called request to

send/clear to send (RTS/CTS), DCF cannot com-pletely eliminate the adverse effects of collisions andretransmissions [10,38,60]. Although IEEE 802.11includes another centralized, polling-based accessscheme, called Point Coordination Function (PCF),for time bounded services, it cannot fully supportthe required QoS requirements due to several reasons(that will be elaborated on in subsequent sections).

To deal with the capacity degradation and QoSprovisioning problems, quite a number of research-ers have proposed various approaches and protocolextensions in the literature. Moreover, the IEEE802.11 Working Group has chartered a 802.11eWorking Group to consider MAC enhancementsfor QoS. In this article, we give a comprehensiveoverview of representative approaches reported inthe literature and in the 802.11 Working Groups.Although there have been several excellent surveysthat summarize QoS support in wireless mobile net-works prior to 2002, e.g., [18,51], many innovativeapproaches have been proposed since then and wefeel it is both timely and necessary to have anotherupdate. In contrast to previous surveys, we present ataxonomy that classifies various R&D efforts intodifferent categories, according to the following crite-ria: (a) the major objective (protocol capacityimprovement, QoS provisioning, better arbitrationof wireless resources, and their modeling and evalu-ation); (b) the type of traffic considered (single-classtraffic vs. multi-class traffic); and (c) the major solu-tion approaches (analytic-model-based, heuristic-based, or simulation-based). For each category ofalgorithms/protocols, we first introduce the key fac-tors that may be used as ‘‘control knobs’’ to effectthe design, and then delve into solutions proposedin each work. We also discuss design alternativeswherever appropriate. To demonstrate how toadapt QoS provisioning in newly emerging areas,

we use the wireless mesh network (a.k.a. the commu-

nity wireless network) as an example and discusshow this new network architecture can benefit fromQoS-enhanced approaches discussed in the article.We discuss the role IEEE 802.11 plays in wirelessmesh networks and research problems that arise.

To set the stage for discussion, we begin by sum-marizing the IEEE 802.11 protocol family in Section2. Then we introduce the various protocol modelingtechniques that underline the theoretical base inSection 3. Following that, we present in Sections4–6 R&D efforts that target to improve the protocolcapacity, to provide QoS (by either devising newMAC protocol components or fine-tuning protocolparameters in IEEE 802.11), and/or to judiciouslyarbitrate radio resources (e.g., transmission rateand power). Finally, we discuss in Section 7 IEEE802.11 MAC in the context of wireless mesh net-works, and conclude the article in Section 8.

2. Preliminaries

To set the stage for discussion, we summarize inthis section operations of IEEE 802.11 standards(and its several extensions) that pertain to this arti-cle. The interested reader is referred to [1,37] for adetailed account of the IEEE 802.11 PHY/MACspecification and its QoS extension. Then we lay aroad map for presenting different research effortsthat aim to improve the protocol capacity, enhanceQoS, and/or better arbitrate wireless resources.

2.1. Operations of IEEE 802.11

As the standard medium access control (MAC)and physical (PHY) layer, IEEE 802.11 [36] pro-vides two access methods: (i) the Distributed Coordi-

nation Function (DCF), also known as the basicaccess method, is a carrier sense multiple access pro-tocol with collision avoidance (CSMA/CA); and (ii)the Point Coordination Function (PCF) is an accessmethod similar to a polling system and uses a pointcoordinator to arbitrate the access right among sta-tions. In addition, the standard includes an(optional) floor acquisition mechanism, calledrequest to send/clear to send (RTS/CTS), to partiallyresolve the hidden terminal problem and decreasethe overhead incurred in collisions.

2.1.1. Distributed coordination function

DCF operates as follows (Fig. 1). Before the datatransmission takes place, a station senses the channel

ACK

SIFS

Slot timeDeferred access

DATABusy

DIFS Backoff

RTS CTS DATA ACK

SIFSBackoffDIFS SIFSSIFS

Slot timeDeferred access

Busy

Fig. 1. IEEE 802.11 DCF with and without RTS/CTS.

1924 H. Kim et al. / Computer Networks 51 (2007) 1922–1941

to determine whether or not the medium is busy. Ifthe medium is sensed busy, the transmission isdeferred until the ongoing transmission terminates.If the medium is sensed idle for a specified time inter-val, called the distributed inter-frame space (DIFS),the station is allowed to counter down its backoff

timer. The backoff timer is part of the slotted binaryexponential backoff technique used to arbitratemedium access: a backoff interval is uniformly cho-sen in ½0;dCW � 1� and used to initialize the backofftimer, where dCW is the current maximum contentionwindow. The backoff timer is decreased as long asthe channel is sensed idle, stopped when data trans-mission (initiated by other stations) is in progress,and reactivated when the channel is sensed idle againfor more than DIFS. The time immediately follow-ing an idle DIFS interval is slotted, with each slotequal to the time needed for any station to detectthe transmission of a frame (termed as the MAC

Protocol Data Unit (MPDU) in IEEE 802.11) fromany other station. When the backoff timer expires,the station attempts for frame transmission at thebeginning of the next slot time.

If the data frame is successfully received, thereceiver transmits an acknowledgment frame aftera specified interval, called the short inter-frame space

(SIFS), that is less than DIFS.1 If an acknowledg-ment frame is not received, the data frame ispresumed to be lost, and a retransmission is sched-uled by choosing a new backoff timer value in½0;dCW � 1�. The value of dCW is set to CWmin

(=32) in the first transmission attempt, and is dou-bled at each retransmission up to a pre-determinedvalue CWmax (=1024). For each data frame, a sta-tion maintains a short retry count (SRC) and a longretry count (LRC) that are independently incre-mented and reset each other. The frame is droppedwhen either counter reaches its limit value (i.e., theSRC limit is 7 and the LRC limit is 4).

1 The necessity of returning an acknowledgment is due to theinability of WLANs to listen while transmitting, since usuallyonly one antenna is available for both transmitting and receiving.

In the case that the RTS/CTS mechanism is used,each station follows the same procedure, except thatthe RTS/CTS handshake operations precede thedata/acknowledgment exchange phase (Fig. 1(b)).A station sends a RTS frame to the destinationbefore transmitting any MSDU. The destinationthen responds with a CTS frame once it hascorrectly received the RTS frame. The source canthen send the MSDU after receiving the corre-sponding CTS response. Both the RTS and CTSframes contain the source, the destination, and theduration it takes to transmit the MSDU/ACKframes. All the other nodes receiving either theRTS and/or CTS frames set their virtual carriersense indicator, called Network Allocation Vector

(NAV), to the duration given in the RTS and/orCTS frames, and use it together with the physicalcarrier sense in determining whether or not thechannel is idle. Even if a hidden station cannot hearthe RTS from the source station, it will be able toreceive the CTS response from the destination sta-tion and update its NAV accordingly. This mecha-nism guards transmission between stations againstunexpected transmission from hidden stations.

2.1.2. Point coordination function

In PCF, all the stations are coordinated throughthe use of a dedicated station, called Access Point

(AP). The AP sends periodically (at Target Beacon

Transmission Times (TBTTs)) beacon messages toinform the stations of the beginning of a new coor-dinated access period (Fig. 2). Each message alsocontains the next TBTT so that all the stationsknow when to expect for the next beacon message.During the interval between two consecutive beaconmessages, there are two non-overlapping sub-peri-ods: the Contention Free Period (CFP) and the Con-

tention Period (CP). During a CP all the stationsemploy DCF to access the medium. During aCFP, the AP plays the role of a coordinator andarbitrates medium access among the stations, fromthe time instant when it acquires the medium tothe time instant when it releases the medium. In

BeaconBeacon

Conteion Free Period Contention Period

Free Period

Contention

TBTT TBTT

Delaydue tobusy

Busy

TBTT interval

CF–END

Fig. 2. IEEE 802.11 PCF.


order for the AP to acquire the medium, it waits foran interval of PCF Inter-frame Space (PIFS) that isshorter than DIFS after detecting the medium to beidle. This prevents other stations from accessing themedium and interfering with the coordination of theAP. After acquiring the medium, the AP schedulestransmission among stations with a polling scheme,and supports time-constrained services.

In spite of the intention to support time-sensitiveapplications, PCF suffers from several limitations: (i)As depicted in Fig. 2, PCF cannot enforce the strictperiodicity of TBTT intervals, because the AP can-not interrupt an on-going data transmission whenit is supposed to send a beacon message. As a result,the temporal QoS requirement of a time-sensitiveapplication may not be fulfilled because of the delayin sending beacon messages; (ii) stations that do notreceive the previous beacon message cannot knowthe next TBTT. These stations include stations thatnewly arrive in the WLANs, stations that are hiddenfrom the AP at the previous beacon TBTT, and/orstations that operate in the power saving mode.These stations may interfere with the AP by contin-uously sending their frames based on DCF; and (iii)no management interface has been defined to set upand control PCF operations, or to communicateQoS requirements to the AP. Consequently, PCFin its current form cannot support service differenti-ation policies in the same manner as the Internet Int-Serv/DiffServ architectures would.

2.2. Network capacity improvement in IEEE 802.11

As mentioned in Section 1, the network capacitymismatch between the wireline and wireless net-works and the lack of QoS provisioning capabilitymotivate IEEE 802.11 to seek for enhancements toimprove the network capacity as well as to supportQoS. In this subsection, we summarize various stan-dards that have been devised for capacity improve-

ment. In the next subsection, we will give anoverview of IEEE 802.11 QoS extension.

The original IEEE 802.11 standard was approvedin 1997 and its second version was released in 1999.The standard mainly specifies the PHY and MAClayers. In the PHY layer, data are transmitted overinfrared signals or in the 2.4 GHz microwave band,modulated with Frequency Hopping Spread Spec-trum (FHSS) or Direct Sequence Spread Spectrum(DSSS). The target data rates are 1 Mbps or2 Mbps. Infrared signals in the PHY layer were laterexcluded since they are usually used for short rangedata communication in personal area networks(PANs). This original standard is usually termedas IEEE 802.11 legacy in order to differentiate fromother standards in the same family.

Most of the efforts improving the network capac-ity were made (by the IEEE 802.11 working group)in the PHY layer. In particular, IEEE 802.11b wasreleased in 1999, and is the first commercially suc-cessful IEEE 802.11 standard. It specifies the rangeof the channel rate {1, 2, 5.5, and 11 Mbps} in the2.4 GHz radio band with the DSSS based modula-tion. IEEE 802.11b has 11 channels of 22 MHz,three of which are non-overlapping channels andcan be used to prevent multiple WLANs from inter-fering with each other.

IEEE 802.11a was published in 2001. The PHYlayer operates in the 5.0 GHz radio band, allows amaximal channel rate up to 54 Mbps with the chan-nel rate range of {6, 9, 12, 18, 24, 36, 48, and54 Mbps}, and uses Orthogonal Frequency DivisionModulation (OFDM) as the modulation technique.IEEE 802.11a has not been as widely used as IEEE802.11b due to its short range of transmission.However, it is an attractive alternative as the5.0 GHz band is less crowded than the 2.4 GHzband.

IEEE 802.11g was approved in 2003, is backwardcompatible with IEEE 802.11b (as it also operates in


the 2.4 GHz radio band as IEEE 802.11b), and canprovide a maximal channel rate of 54 Mbps as IEEE802.11a. Its modulation technique is DSSS orOFDM. Although this standard is fully compatiblewith IEEE 802.11b, it has been shown that the over-all throughput of the IEEE 802.11g-compliantnetwork is reduced in the presence of IEEE802.11b-operated stations.

2.3. QoS extension to IEEE 802.11

Different from the aforementioned standardswhich mainly focus on improvement in the channelrate of the PHY layer, the IEEE 802.11e WorkingGroup has been working on the MAC extensionsto the IEEE 802.11 standard in order to supportQoS for multimedia applications, such as voice overIP or video streaming. It is expected that such exten-sions, in combination with recent improvements inthe PHY capabilities from IEEE 802.11a and IEEE802.11g, will increase the overall system perfor-mance, and expand the application spectrum forIEEE 802.11.

The MAC architecture in the IEEE 802.11e draftis depicted in Fig. 3(a). In addition to DCF andPCF, a new coordination function called the Hybrid

Coordination Function (HCF) is defined. HCF itselfincludes both a contention-based channel accessmethod, called the Enhanced Distributed Channel

Access (EDCA) mechanism for contention-baseddata transmission and a controlled channel access,referred to as the HCF Controlled Channel Access

(HCCA) mechanism, for contention-free datatransmission.

In EDCA contention access, a total of eight userpriority levels are defined. Each priority is mappedto an access category (AC), which corresponds to

Distributed Coordination Function (DCF)

PointCoordination

Function(PCF)

HCFContention

Access(EDCA)

HCFControlled

Access(HCCA)

Required for ParamQoS Services

Used for ContenServices, basis fHCF

MACExtent

Hybrid Coordination Function (HCF)Required for Contension-Free

Services for non-QoS STA,optional otherwise

Required for PrioritizedQoS Services

Fig. 3. IEEE 802.11e MAC architecture and implementation mo

one of the four transmit queues. The mappingbetween user priorities and access categories isdepicted in Table 1. As shown in Fig. 3(b), an IEEE802.11e-compliant station allocates a separatequeue for each AC, and each queue is served byan independent channel access function. The chan-nel access function specifies the EDCA algorithmin terms of (1) the minimum channel idle delaybefore attempting for frame transmission AIFS

(note that the AIFS values except that at QoS accesspoint (QAP) are larger than or equal to the DIFSvalue), (2) the minimum and maximum contentionwindows (CWmin and CWmax), and (3) the transmis-sion opportunity limit TXOP—the right to use themedium and transmit multiple frames without back-off. If the DATA-ACK exchange sequence has beencompleted, and there is still time remaining in theTXOP, the station may transmit another frame inthe same access category, provided that the frameto be transmitted and its necessary acknowledgmentcan fit into the time remaining in the TXOP. Essen-tially data transmission from high-priority flows willbe favored by assigning smaller AIFS values, smal-ler contention window parameters, and largerTXOP values to increase both their probability ofgaining medium access and utilization of the med-ium. All the parameters can be dynamically updatedby the QAP through the EDCA parameter set, andare sent from the QAP as part of the beacon frames,and probe/re-association response frames. Thisadjustment allows stations in the WLAN to adaptto changing conditions, and gives the QAP the abil-ity to manage the overall QoS performance. Thedefault EDCA parameter set is depicted in Table 2.

There exist two types of contention in EDCA:one that occurs internally within one station, andone that occurs externally among all the stations.

eterized

tionor PCF and

Scheduling according to priority

Backoff

CW AIFS

Backoff

CW AIFS

Backoff

CW AIFS

Backoff

CW AIFS

Classification to Traffic Categories

Channel access functions

4 Priority Queues

del: (a) MAC architecture and (b) implementation model.

Table 1User priority to access category mappings

Priority User Priority 802.1D Designation Access category Designation

Lowest 1 BK AC_BK Background2 – AC_BK Background0 BE AC_BE Best Effort3 EE AC_BE Video4 CL AC_VI Video5 VI AC_VI Video6 VO AC_VO Voice

Highest 7 NC AC_VO Voice

Table 2Default EDCA parameter set

AC CWmin CWmax AIFS TXOP limit

DS_CCK Extended Rate/OFDM Other PHYS

AC_BK aCWmin aCWmax 7 0 0 0AC_BE aCWmin aCWmax 3 0 0 0

AC_VIðaCWmin� 1Þ

2aCWmax 2 6.016 ms 3.008 ms 0

AC_VOðaCWmin� 3Þ

4

ðaCWmin� 1Þ2

2 3.264 ms 1.504 ms 0


Internal contention is resolved by transmitting ahigher priority frame while having lower priorityframes to back off as if they encountered externalcollision. External collision is essentially resolvedin the same manner as DCF does, except one impor-tant difference: different access classes (queues)have different parameters that define the EDCAoperations.

HCF consists of a contention-free period and acontention period as PCF does, and has a central-ized coordinator called Hybrid Coordinator (HC)as PCF has the AP. Fig. 4 shows the timing struc-ture of HCF. The major difference between HCFand PCF is that HC can control some of the sub-

Conteion Free Period Contenti

TBTT interval

TBTT

Beacon CF–end

PCF HCCAEDCA

Polling by HC

Fig. 4. IEEE 802.11e H

intervals called HCF Controlled Channel Access

(HCCA) in CP in addition to CFP. That is, theHC can initiate a fast collision resolution periodeven after a CP starts, by polling each station witha shorter IFS called PIFS, allowing the station toask for transmission opportunities (TXOP) andthen allocating the requested TXOP to the station.In this manner, a HC can implement the exact trans-mission schedule for each AC at each station, andconsequently support delay-sensitive traffic even inCP.

In order for different entities (stations of differentACs and the HC) to acquire the medium, IEEE802.11e has specified different IFS values (Fig. 5).

Free Period

Contention

on Period

Beacon

TBTT

EDCA

CF Superframe.

DIFS/AIFS

Busy Medium Backoff Slots Next Frame

Slot Time

Select Slot and Decrement Backoff as longas medium is idle

Defer Access

SIFS

PIFS

DIFS

AIFS[i]

AIFS[j]

Contention Window

Immediate access whenMedium is free >= DIFS/AIFS[i]

Fig. 5. Different IFS values defined in IEEE 802.11e.


SIFS is the smallest IFS that separates frames of apredefined operational sequence (e.g., a RTS framefollowed by a CTS frame, followed by a data frame,and finally followed by an ACK frame). PIFS is thesecond smallest IFS which allows a HC to differen-tially acquire the medium. DIFS is the IFS forwhich stations have to wait after sensing an idlemedium in legacy IEEE 802.11, and defines thesmallest AIFS. Finally, AIFS is used by ACs asthe IFS for which stations with different ACs haveto wait after sensing the medium to be idle. ACsof different priorities use different values of AIFS,with the AC of the lowest priority using the largestvalue of AIFS.

2.4. Road map of this article

There have been a considerable number ofresearch works that (i) analytically model the oper-ational behavior of IEEE 802.11 MAC; (ii) proposeapproaches to improve the protocol capacity, pro-vide QoS support, or better arbitrate wirelessresources; and (iii) evaluate the various componentsof IEEE 802.11e. In this section, we categorize themaccording to several criteria:

• The major objective (analytic modeling and eval-uation of IEEE 802.11/IEEE 802.11e, devisingalgorithms for improving protocol capacity orfor providing service differentiation);

• The type of traffic considered (single-class trafficor multi-class traffic);

• Nature of the major solution approaches (analyt-ical-model-based, heuristic-based, or simulation-based).

Fig. 6 gives a taxonomy of approaches to be dis-cussed in this article. In Section 3, we will give anoverview of all the analytic models that characterizethe operational behavior of IEEE 802.11 MAC. Afterlaying the theoretical base, we will then discuss in Sec-tion 4 various IEEE 802.11-compliant approachesthat are proposed in the literature for capacityimprovement and for QoS provisioning. Followingthat, we present in Section 5 several performancestudies on the various IEEE 802.11e components.As radio resource allocation and management is clo-sely related to the protocol capacity that can be deliv-ered to users and the quality of data transmission, wesummarize in Section 6 resource allocation researchthat pertains to IEEE 802.11.

3. Performance modeling of IEEE 802.11-compliant

WLANs

As the first step, we present several analyticalmodels that analyze the throughput behavior ofIEEE 802.11. These models establish the theoreticalbase for subsequent research that focuses on capac-ity improvement and/or QoS provisioning.

3.1. Models that consider single traffic class

Bianchi [9] models the behavior of the binarybackoff counter at one tagged station as a discreteMarkov chain model. It determines the transmissionprobability (s) and analyzes the saturation through-put under the assumption that in each transmissionattempt, regardless of the number of retransmissions,each packet collides with constant and independentprobability p. It is intuitive that this assumption

single classsingle class

Evaluating IEEE 802.11e

single class

Imporving capacity orsupporting QoS for differentiated services

Throughput models

multiple classes multiple classes

Fine tuning parameters in IEEE 802.11 MACDevising new MAC algorithms

Resource allocation

QoS provisioning or capacity improving in IEEE 802.11

Model based Fair queuing based Heuristic Simulation based

multiple classes

Wu et al.Foh and ZukermanCarvalho et al.

BianchiCali et al.Kumar et al.

Kumar et al.Hui & DevetskiiotisGe & Hou

Robinson & RandhawaKong et al.

Xiao Kim and Hou BharghavanChao et al.Fang and Bensaow

Vaidya et al.Branchs and Perez Veres et al. Bianchi

Cali et al. Kwon et al.

Weinmiller et al.

Qiao and ShinGe and Hou

Ada & CastellucciaBononi et al.Deng et al.Romdhani et al.Xiao et al.Zhu

Ge and HouHui & Devetskiiotis

Robinson & RandhawaKong et al.

Choi et al.Lindgren et al.Mangold et al.Pong and Moors

Pavaon and ChoiQiao et al.

Fig. 6. A taxonomy of performance modeling/capacity improvement/QoS provisioning work for IEEE 802.11 and 802.11e.


becomes more realistic when the number of stationsand the individual contention window sizes get lar-ger. Although the model does not consider the casein which the backoff counter freezes (at the currentvalue) when the medium is sensed busy due to thedata transmission activities (initiated by other sta-tions), it motivates a significant amount of subse-quent analysis work. Recently, Kumar et al. [39]present a simplification and generalization of Bian-chi’s analysis and give fixed-point solutions. In thecase of a large number of nodes, they give explicitexpressions for the collision probability, the aggre-gate attempt rate, and the aggregate throughput.

Calı́ et al. [16] derive a theoretical throughputbound by approximating IEEE 802.11 with a p-per-sistent model of IEEE 802.11. Essentially each sta-tion transmits its frame in a slot (after the mediumis sensed idle for an interval of DIFS) with probabil-ity p. Based on the analytical model, they observethat the system throughput only relies on the valueof p and the number of active stations. They alsoshow that with the current parameter settings ofIEEE 802.11, the maximal achievable systemthroughput falls far beneath the theoretical capacitybound. As such, they suggest to incorporate aparameter tuning method in IEEE 802.11 so as toon-line infer parameters (e.g., the number of activestations) needed for computing the best protocolparameters (e.g., the contention window size to beused) and achieve the capacity bound. They onlydeal with IEEE 802.11 DCF without the RTS/CTSmechanism under the assumptions that the data

frame size follows a geometric distribution, and thatall the stations always have packets ready for trans-mission (i.e., under the asymptotic condition).

Carvalho et al. [17] propose an analytical modelwhich computes the average service time and jitterper frame in a saturated IEEE 802.11-operated adhoc network. They show that the existing binarybackoff scheme is not appropriate for supportingdelay constraints, and that use of a large and con-stant contention window size is more efficient thanbinary backing off the window size. This suggeststhat the initial contention window size CWmin

should be set to a large enough value to avoid exces-sive backoff.

Foh and Zukerman [28] analyze, by leveraging thethroughput analysis by Bianchi [9], the saturationthroughput achievable under IEEE 802.11. Theyassume that the number of active stations increasesaccording to a Poisson process and decreases accord-ing to the state dependent service process. Wu et al.[62] also leverage Bianchi’s analysis to study the per-formance of reliable transport protocols over IEEE802.11-operated WLANs. They extend the Markovchain model in [9] and incorporate the frame retrans-mission limit, and hence the revised model achievesbetter accuracy in characterizing the transmissionactivities of IEEE 802.11 DCF.

3.2. Models that consider multiple traffic classes

Xiao [63] extends Bianchi’s model [9] to accom-modate the case of multiple traffic classes, and


incorporates three tunable parameters into themodel: the initial contention window size, the retrylimit, and the backoff window-increasing factor.However, the effects of AIFS and TXOP valuesare not figured in. With the use of the model, theperformance of IEEE 802.11e in terms of saturationthroughput, saturation delay and frame droppingprobability is analytically derived. Similarly, Konget al. [49] extend Bianchi’s model and develop athree-dimensional, discrete-time Markov chain thattakes into account of different AIFS values, conten-tion window sizes, and retransmission limits.Kumar et al. [55] extend their fixed-point analysisto study throughput differentiation provided by dif-ferent initial contention window sizes, persistencefactors, and AIFS values.

Ge and Hou [30], on the other hand, extend thework by Calı́ et al. [16] and devise an analyticalmodel for a multi-class, p-persistent version of IEEE802.11 DCF. Based on the devised analytical model,they then derive the optimal value of the probabil-ity, pi, with which a station with class-i trafficattempts for transmission in a slot under theasymptotic condition. By optimality, they meanthe protocol capacity is maximized, subject to therequirement that the ratio of the throughputattained by class-i traffic to that by class-1 trafficconforms to certain pre-determined value. As indi-cated in the analysis in [10], there is an one-on-onecorrespondence between the value of p in the p-per-sistent version of IEEE 802.11 DCF and the conten-tion window size, CW, in the legacy IEEE 802.11DCF. Hence the results derived in [30] can be read-ily applied to tune the contention window size in thelegacy IEEE 802.11 DCF, so as to optimize the pro-tocol capacity in the case of multiple traffic classes.

Motivated by the need to simplify and unify, Huiand Devetsikiotis [35] examine the foundations ofboth Bianchi’s Markov model and Cali’s p-persis-tent CSMA model and propose a new unified per-formance model and analysis method to study thesaturation throughput and delay performance ofEDCA. Robinson and Randhawa [56] investigatein detail activities in the post-collision period, andpropose and validate an analytical model for thesaturation throughput of EDCF.

4. Capacity improvement and QoS extension

in IEEE 802.11

Research that focuses on capacity improvementor service differentiation in IEEE 802.11 DCF can

be classified into two categories, with respect tothe approaches they use. The first category ofapproaches achieves the objective by either modify-ing the contention window backoff scheme, or fine-tuning the IFS values and the contention windowparameters, while the second category by devisingnew MAC algorithms (some of which can be incor-porated into IEEE 802.11 with modest modification).

4.1. Works that devise new MAC algorithms

Research in this category can be further groupedinto two sub-classes: those that focus on improvingthe protocol capacity in the case of a single trafficclass, and those that target to achieve service differ-entiation in the case of multiple traffic classes.

4.1.1. Consideration of single traffic class

Algorithms in this sub-class aim to improve theprotocol capacity by better detecting the channelstatus and reducing potential collision.

Bharghavan [8] proposes two MAC algorithms:CSMA/CA-based Dual Channel Collision Avoidance

(DCCA) and backoff-based Fair Collision Resolution

Algorithm (FRCA). DCCA employs two channels:one is a control channel for signaling and the otheris a data channel for data transmission. Since thecontrol range is tuned to be much larger than thedata transmission range, collisions in all the casesof hidden/exposed stations can be considerablyavoided. FRCA implements a collision resolutionmethod as follows: each station n keeps track ofthe number of RTS and CTS frames transmitteduntil a successful RTS/CTS handshake takes place,so as to correctly distinguish local collisions fromremote ones. Station n then determines its backofftimer only taking into account of local collisions.It also advertises its backoff timer value to neighbor-ing stations, by including the values in the header ofall non-RTS packets. A neighboring station canthen leverage the contention status experienced bystation n and use the advertised value as an estimateof the initial contention window size, rather thangrowing the contention window from CWmin.

Chao et al. [19] propose a simple load-awareMAC protocol. Observing that the contention-based IEEE 802.11 DCF scheme does not performwell and often renders excessive collisions (andsubsequent retransmissions) when the system loadis heavy, they propose to use IEEE 802.11 DCFwhen the overall system load is light, and a token-based, contention-free scheme otherwise. Fang and


Bensaow [20] study the issue of how to enforceamong competing stations the same probability ofsuccessfully transmitting a packet within an optimalfair interval, i.e., the interval in which all the sta-tions can have the chance of successfully sendingone packet. They devise a new binary backoff algo-rithm for IEEE 802.11 DCF, and prove its stabilityas well as fairness with game-theoretic methods.

Kim and Hou [38] develop a model-based frame

scheduling (MFS) scheme that is laid as a thin layerbetween the Link Layer Control (LLC) and MAClayers to improve the capacity in wireless LANs.In MFS, each station estimates the current networkstatus by keeping track of the number of collisions itencounters between its two consecutive successfulframe transmissions. With the on-line measuredparameters the station then estimates the numberof active stations that attempt to transmit frames,and computes the current network utilization withthe use of a rigorous fluid model. (In order to accu-rately calculate the current utilization in WLANs,they develop an analytical fluid model that charac-terizes data transmission activities in IEEE 802.11-operated WLANs with/without the RTS/CTSmechanism, and figures in all the control overheadincurred in the PHY and MAC layers and the othersystem parameters specified in IEEE 802.11.) Thecalculated result is then used to determine an (artifi-cial) delay to be introduced before a station passesthe frame down to IEEE 802.11 MAC. As long asthe measured network status sustains, the delayintroduced can reduce the likelihood of potentialcollisions. MFS does not require any change inIEEE 802.11 MAC (as it is implemented as a thinlayer between the LLC and MAC layers) and is thusbackward compatible with IEEE 802.11.

4.1.2. Consideration of multiple traffic classes

Algorithms in this sub-class aim to provideweighted fairness (in terms of the throughput attainedby different stations) and differentiated services.

Based on the notion of weighted fair queuing,Vaidya et al. [59] propose the Distributed Fair

Scheduling (DFS) algorithm. In DFS, each nodedetermines the finish tag of each packet by leverag-ing the Self-Clocked Fair Queuing algorithm [32]. Itthen determines the packet with the smallest finishtag in a distributed manner, and assigns the backoffvalues to the head-of-queue packets in proportionto the finish tags of those packets.

Banchs and Perez [6] also propose a Distributed

Weighted Fair Queuing (DWFQ) algorithm. DWFQ

aims to allocate the channel bandwidth, ri, for aflow i, according to the weight, Wi, of the flow,i.e.,

rj

W j¼ ri

W i8i; 8j. Every time a new packet is

transmitted, ri can be estimated with

rnewi ¼ ð1� e�ti=KÞ � li

tiþ e�ti=K � rold

i ;

where li and ti are, respectively, the size and inter-arrival time of transmitted packets, and K is aconstant of 100 ms. Each station i maintains a labelLi, which is calculated as Li ¼ ri

W i, and a contention

window scaling coefficient coeff. Each sending sta-tion includes its label in the header of its packet.For each observed packet, if the received label Lrcv

in the header of the packet is smaller than the labelof the station Lown, the station increases its scalingcoefficient coeff by a small amount, while in theopposite case it decreases coeff by a small amount.Each station maintains its contention window,CW802.11, following the rules in the standard IEEE802.11 MAC protocol. However, when the stationcalculates the backoff window size, the actual con-tention window size used, CW, is derived by scalingCW802.11 with the coefficient coeff. This solution re-quires that all the stations in the BSS run the samefair scheduling algorithm, and the performance iscontingent upon how to determine whether thechannel is overloaded. More importantly, it is notclear whether the scheme shall always converge toa normal equilibrium state rather than an abnormalone (e.g. extremely low aggregated throughputbeing fairly shard among all flows). In addition toDWFQ, Banchs and Perez also propose anotherscheme that is similar to DWFQ in [7]. The pro-posed scheme uses token buckets to regulate flowrates and attempts to control the number of tokensin the bucket so that desired flow rates can beachieved.

Veres et al. [60] present a delay model for IEEE802.11 DCF to analyze the expected delay experi-enced by a station. Based on the model, they showthat service differentiation can be achieved by usingdifferent contention window values, {CWmin,CWmax}, for each service class. However, they donot discuss how to select appropriate values forCWmin and CWmax. In addition, they propose twoMAC algorithms: (i) Virtual MAC (VMAC) thatestimates the MAC-level service qualities, such asthe delay, collision, and losses by emulating theoperational behaviors of IEEE 802.11-compliantMAC, and (ii) Virtual Source (VS) that estimatesthe application-level delays caused by packetizing,


encoding, and queuing on top of VMAC. They thenpropose a distributed admission control algorithmthat exploits the estimates obtained in VMAC andVS, and show that the resulting MAC equipped withthe admission control scheme can guarantee the per-formance required by each service class. The VMACalgorithm bears some similarity to the internal con-tention resolution algorithm in the IEEE 802.11edraft. However, VMAC is intended to be used asan estimation module in admission control ratherthan that for resolving frame collision.

4.2. Works that fine-tune parameters in IEEE 802.11

MAC

Another category of approaches aims to tune theMAC parameters in IEEE 802.11 legacy standard orIEEE 802.11e draft to improve the protocol capacityor to provide differentiated services. Research effortsin this category can also be classified into two sub-classes, according to whether or not they deal withsingle-class traffic or multi-class traffic.

4.2.1. Consideration of single traffic class

Algorithms in this sub-class aim to improve theprotocol capacity or to provide fairness (in termsof the throughput attained by different stations).The objective is achieved by turning the contentionwindow size CW. Algorithms can be further classi-fied into model-based and heuristic-based.

4.2.1.1. Model-based approaches. All the models inSection 3.1 can essentially be used to tune the conten-tion window size (which in turns determines theattempt probability), in order to improve the protocolcapacity. In what follows, we discuss several represen-tative algorithms that leverage the analytic models.

Based on the observation that the systemthroughput achievable by IEEE 802.11 DCF heav-ily depends on the number of active stations, Bian-chi et al. [11] propose a method that on-lineestimates the number of active stations under IEEE802.11 MAC. They present that if the conditionalcollision probability p is estimated by an AutoRegressive Moving Average (ARMA) filter, thenthe number of active stations n can be estimated as

N ¼ 1þ logð1� pÞlog 1� 2�ð1�2pÞ

ð1�2pÞðWþ1ÞþpW ð1�ð2pÞmÞ

� � ;

where W = CWmin and m = log2(CWmax/CWmin).Therefore, based on the estimated number of active

stations, one can dynamically determine the conten-tion window size to avoid potential collisions.

As discussed in Section 3.1, Calı́ et al. show in [15]that the system throughput relies on the transmissionprobability, p, and the number of active stations.They also show that the average number of idle slotsin a virtual transmission time (i.e., the time intervalbetween two consecutive successful frame transmis-sions) can be expressed as a function of N and p.Based on these observations, Calı́ et al. [15] proposea p-persistent version of IEEE 802.11 MAC protocolwith an adaptive backoff mechanism. In theproposed protocol, a frame is transmitted with aprobability p, and is deferred transmission with aprobability 1 � p, where the value of p is dynamicallyadjusted according to the channel status. Specifically,the average idle period between two consecutivetransmissions, E(Tidle), can be expressed as

EðT idleÞ ¼ð1� pÞN

1� ð1� pÞN� tslot;

where tslot is the slot time (= 20 ls in IEEE 802.11).When the attempt probability p used in the mea-surement period is known, one can infer the numberof active stations by on-line measuring the idle per-iod. With the on-line inferred parameter N, a stationcomputes the optimal value of p using the analyticmodel. Calı́ et al. show that the computational over-head incurred in on-line measurement is not signifi-cant, and that with the value of p being on-lineadjusted, the proposed protocol can achieve systemthroughput that is close to the theoretical protocolcapacity limit derived in [16].

4.2.1.2. Heuristic-based approaches. Kwon et al. [40]propose to use a minimum contention window sizeCWmin that is smaller than what is specified in IEEE802.11 and a maximum contention window sizeCWmax that is larger than what is specified in IEEE802.11. Each station increases (doubles) the conten-tion window size up to CWmax when it detects abusy medium or when it experiences collisions inits transmission attempt, and decreases (halves) itscurrent backoff timer value when it detects a fixednumber of consecutive idle slots during the backoffprocedure. The contention window size is reset toCWmin when it successfully transmits a frame. Toachieve fairness, the self-clocked fair queuing(SCFQ) algorithm [32] is used to track the servicereceived by each station. When the service receivedby a station exceeds its fair share by a threshold,


the station gives up its capture of the channel by set-ting its backoff timer to a value randomly generatedfrom [0,CWmax]. As compared with the other fine-tuning algorithms, this approach does not requireestimates of the number of active stations and doesnot make any assumption on the traffic pattern (e.g.the asymptotic condition). However it is not clearwhether or not the approach provides deterministicperformance bounds in terms of system throughputand frame delays.

Weinmiller et al. [61] study the difference in thebackoff values between waiting stations and newlyarrived stations after one successful transmission.They show that the attempt probability system wideis not as uniform as expected. They then propose twoschemes that improve the performance by havingnewly arrived stations choose larger backoff values:(i) the weighted slot selection scheme allows each slotto have an additional weight according to its posi-tion within the current contention window; (ii) theload adaptive scheme restricts newly arrived stationsto set their backoff timer in [CW � CWselected,CW],while allowing existing stations to set their backofftimer in [0,CW � CWselected], where CW is the cur-rent contention window size and CWselected is the slotat which the successful transmission occurs in theprevious competition cycle.

4.2.2. Consideration of multiple traffic classes

Algorithms in this sub-class aim to provide QoSin terms of the ratio of throughput attained by traf-fic of different classes. There are several controlknobs that can be used to effect the design: (1)IFS values (that determines when a station can startto count down its backoff timer after sensing an idlemedium), (2) the minimum and maximum conten-tion windows (CWmin and CWmax, between whichthe backoff timer value will be randomly selected),(3) the retry limit (up to which retransmissions canbe attempted), (4) the backoff scaling factor (thataffects how fast the contention window increasesafter collision), (5) the maximal frame size, and (6)the transmission opportunity limit TXOP (thatdetermines for how long a station can transmit onceit grasps the medium). Different approaches haveexplored use of one or more control knobs andassigned different parameter values to different traf-fic classes so as to favor high-priority traffic withbetter opportunity to gain access to the medium.(For example, (1), (2) and (6) have been defined inthe IEEE 802.11e draft.) They can also be furtherclassified into heuristic-based and model-based.

4.2.2.1. Heuristic-based approaches. Aad et al. [2]propose three mechanisms to provide differentiatedservices in IEEE 802.11-compliant networks. Inthe first mechanism, when a station encounters col-lision and has to increase its current contention win-dow size ðdCW Þ, instead of exercising the binaryexponential backoff algorithm and doubling the cur-rent contention window size it uses a different back-off scaling factor per traffic class. In the secondmechanism, instead of using a single value of DIFS,stations of priority j wait for an interval of DIFSj

after sensing the medium to be idle. The values ofDIFSj are so chosen that DIFSj < DIFSj�1, as prior-ity j is higher than priority j � 1. Moreover, the con-tention window backoff mechanism is modified toensure stations of priority j will always acquire themedium earlier than stations of priority j � 1: themaximum contention window size of a station ofpriority j is set to (DIFSj�1 � DIFSj), so that thetime a priority-j station has to back off (with itsDIFS value included) is always less than or equalto DIFSj�1. This prevents frames of the same prior-ity from colliding each other, while ensuring thatframes are served according to their priorities. Thelast mechanism specifies different maximal framesizes to different priority levels. A station discardsframes that are larger than the maximum frame sizeor fragments them. This method is grounded on theintuition that the maximal frame size has a linearrelationship with the data rate. Clearly, the firsttwo methods bear similarity with the mechanismsproposed in the IEEE 802.11e draft.

Bononi et al. [13] propose a contention reductionmechanism, called Distributed Contention Control

(DCC). During a backoff interval, a station keepstrack of the number of busy slots, Sb (no matterwhether the medium is busy for transmission or forcollision), and the total number of slots availablefor transmission, Si. The station then computes theslot utilization (l) by l = Sb/Si. In addition, the sta-tion also keeps track of the number of transmissionattempts (n) already performed for the currentlypending frame, and computes the transmission prob-ability p with which a station transmits its frame in aslot after sensing an idle medium for DIFS as p =(1 � ln). With this probability, each station attemptsto transmit its pending frame. In order to incorporatetraffic priority, the transmission probability can beadapted to include the traffic priority (‘) and favorhigh-priority traffic, i.e., p = (1 � lnÆ‘).

Bononi et al. [14] also propose another mecha-nism, called Asymptotically Optimal Backoff (AOB),


which combine the mechanisms in [13] and [15,16], todynamically tune the backoff window size to achievethe theoretical capacity limit of the IEEE 802.11 pro-tocol. Essentially the same equation for the transmis-sion probability is used, but the optimal channelutilization (lopt) is figured in the equation, in theform of p ¼ 1�minð1; l

loptÞn in order to enable the

channel utilization to reach the optimal value. Theoptimal channel utilization is approximated withlopt = N Æ pmin, where N is the number of active sta-tions and pmin is the optimal p-value in Calı́’s p-persis-

tent model [15,16]. Similar to the work reported in[13], a station can also take into account of traffic pri-orities (‘), and computes the transmission probabilityas p ¼ 1�minð1; l

loptÞn�‘.

Deng et al. [24] propose to provide service differ-entiation with the use of different backoff intervalsand inter-frame space (IFS) values. The backofftime generation function for high-priority flows ischanged to branf() Æ 2i+1c, and that for low priorityflows is changed to 2i+1 + branf() Æ 2i+1c, where i isthe number of times a station attempts to transmitthe same frame. Combined with the use of two dif-ferent IFS values, DIFS and PIFS for high and lowpriority flows, the proposed scheme can support atotal of four different priority classes.

Romdhani et al. [57] argue that the current con-tention window size is closer to the ‘‘optimal’’ valueand hence it may not be desirable to discard suchvaluable information and reset the contention win-dow to CWmin after each successful transmission.Instead it is more preferable to decrease the currentCW value at a slower pace by a moderate backoffmultiplicator factor (MF) after each successfultransmission. Based on this argument, they proposea scheme called Adaptive EDCF (AEDCF). Eachclass i uses a different MF value q(i) that is propor-tional to the class priority. After each successfultransmission of a class i frame, CW(i) is updatedas follows: CW(i) max(CWmin(i), CWold(i) · q(i)).Similarly, after each failed transmission of a frameof class i, the new contention window of this classis increased with a backoff scaling factor r(i), i.e.,CW(i) min(CWmax(i), CWold(i) · r(i)).

Xiao et al. [64] propose a two-level protection andguarantee mechanism for voice and video traffic. Atthe first level, QAP measures the amount of timeused by each traffic class in each beacon interval,determines the additional amount of time availablefor each class during the next beacon interval andbroadcasts such information to all stations. A newstation determines whether it can start a new flow

by checking whether there is sufficient remainingtransmission time budget in the system for the corre-sponding traffic class. At the second level, the servicerate differentiation for the voice and video flows isguaranteed by dynamically controlling EDCF chan-nel access parameters for (best-effort) data trafficaccording to the following three rules: (1) the backoffscaling factor used in the ith retry, ri, varies with i,where 1 6 i 6 L and L is the retransmission retrylimit. (Note that ri is defined differently from r[i]defined in AEDCF.) Instead of having ri = 2 asspecified in 802.11e, the mechanism enforces2 6 r1 < � � � < rL so that the contention window sizeincreases faster after the collision occurs; (2) When aframe is attempted for transmission for L times andis dropped, both CWmin and AIFS are increased by amultiplicative factor, until a pre-determined upperlimit is reached; (3) When a station successfullytransmits m consecutive frames, both CWmin andAIFS are decreased by a multiplicative factor, untila pre-determined lower limit is reached.

Zhu et al. [66] propose the Enhanced Distributed

Coordination Function with Dual-Measurement

(EDCF-DM) that attempts to reduce the number ofidle slots by dynamically adjusting the contentionwindow parameters according to the current trafficstatus of each traffic category. In EDCF-DM, eachstation divides the time domain into measurementwindows (MWs), and determines the network condi-tion indicator in the jth MW with aðjÞ ¼

#ðcollisionsÞ#ðframes successfully sentÞ, where #(event) is the number

of the parameterized event that occurred at the sta-tion in the jth MW. The network condition indicatora is then calculated as a moving average of a(j)’s, andused to determine the backoff scaling factor r(i) fortraffic class i. Specifically, if no collision occurs dur-ing the last MW and no higher priority flow hasframes to transmit or receive, the backoff scalingfactor for traffic class i is set to r(i) =min{(1 + 2i) · a,rmin}; otherwise, it is set to r(i) =min{(1 + 2i) · a,rmax}. After each successful trans-mission, the contention window size CWi is adjustedas CWi = max{CWi,min,CWi · r(i)}. On the otherhand, upon occurrence of collision, CWi is adjustedas CWi = min{CWi,max,CWi · r(i)}.

4.2.2.2. Model-based approaches. A major problemwith heuristic-based approaches is that no rigorousanalysis is provided to reason about the optimalityor robustness of these approaches. Model-basedapproaches, on the other hand, are devised based


on analytical models, and hence their performancecan be better bounded or inferred.

Qiao and Shin [54] extend Bianchi’s Markovchain model in [10] to the case of multiple priorityclasses and propose a priority-based fair mediumaccess control protocol P-MAC. P-MAC requiresthat each station keeps track of the activities onthe wireless medium. Based on the measurementsof the average number of consecutive idle slots onthe wireless medium, avg_idle, and the average num-ber of time slots between two consecutive successfulclass-i frame transmissions, each station can esti-mate the number, fi, of active stations of class-i,and approximately calculate the optimal contentionwindow size, CW �

i , of class i.Ge et al. [29,31] exploit their analytical model in

[30] (which in turns is derived based on Calı́’smodel), and devise a multi-class, p-persistent versionof IEEE 802.11 to achieve throughput differentia-tion among different traffic classes. Given the desir-able ratio, ri1, of the throughput attained by class i

traffic to that attained by class 1 traffic, they derivethe relationship between the optimal values ofattempt probabilities in a slot, pi and p1 (or equiva-lently the optimal window sizes, CW �

i and CW �1), for

classes i and 1. The protocol capacity can then beoptimized by finding the optimal value of p1, subjectto the constraint of the relation between pi an p1.They also propose an on-line measurement mecha-nism to measure and infer the number of active sta-tions of each class so as to calculate CW �

i and copewith network traffic dynamics.

5. Performance evaluation of IEEE 802.11e

After the IEEE 802.11e draft was released, sev-eral research efforts have been made to evaluate itsperformance (with respect to the capability of QoSprovisioning). Most of the work, perhaps except[31], are simulation based.

Choi et al. [22] evaluate IEEE 802.11e EDCF,and compare it against IEEE 802.11 legacy DCF,with respect to the throughput, packet loss rate,and delay. As expected, IEEE 802.11e EDCF ismore capable of supporting differentiated channelaccess among traffic of different classes. They alsoleverage the notion of contention-free bursts (CFBs)(the idea of which originates from Mangold et al.[46]), and allow a station to transmit multipleframes separated by SIFS within its TXOP limit.They show that CFBs improve the performance sys-tem wide in terms of throughput, packet loss rate,

and delay, but at the expense of increasing thedelays for other traffic classes.

Lindgren et al. [43,44] evaluate, via simulation,four mechanisms for supporting service differentia-tion in IEEE 802.11 wireless LANs: IEEE 802.11legacy PCF, IEEE 802.11e EDCF, DFS [59] (Sec-tion 4.1), and Blaskburst [58]. As indicated by thesimulation results, all the schemes can provide ser-vice differentiation to some extent, but their perfor-mances degrade as the number of high-prioritystations increases. Also, both Blackburst and EDCFmay starve low priority traffic in the presence ofheavy high-priority traffic. They conclude thatDFS is the best scheme as far as the relative differ-entiation is concerned.

Mangold et al. [47] give an overview of new fea-tures in IEEE 802.11e to support QoS in WLANs,and evaluate, via simulation, IEEE 802.11e EDCFand HCF. Pong and Moors [52] give several guide-lines (obtained through simulation studies) on howto select the transmission opportunities, TXOP,and the contention window parameter, CWmin,according to traffic priorities. They find that thevalue of CWmin affects both the delay and band-width characteristics.

With the use of the analytic model developed in[30], Ge and Hou [31] show that by assigning appro-priate different attempt probabilities (or contentionwindow sizes) to stations of different classes, it isfeasible to provide deterministic (proportional)service differentiation and achieve pre-specifiedtargeted throughput ratios among different classes,while at the same time, maximizing the total systemcapacity. The analysis is first made under theassumption that after a busy period, all stationswait for the same interval of DIFS after sensingthe channel to be idle. They then relax the assump-tion and investigate the effect of assigning differentAIFS and TXOP values to different traffic classes.They derive, under the case of asymptotic condition,the ratio of average per flow throughput betweendifferent traffic classes as a (closed-form) functionof both the contention window size and the AIFSvalues. Their analysis reveals that indeed AIFSplays an important role in QoS differentiation; how-ever, simultaneously tuning both the contentionwindows and the AIFS values in order to meet allthe throughput ratio constraints (i.e., the ratio, ri1,of the throughput attained by class i traffic to thatattained by class 1 traffic) lead to sub-optimal per-formance. The dimension of the design freedomshould be reduced by either fixing the AIFS values


of all access categories to the same value (e.g, 2, thatis, AIFS = DIFS) and then deriving the optimal val-ues of contention window sizes (as was done in theirfirst analysis) or vice versa.

6. Resource allocation techniques in IEEE 802.11

In addition to devising new MAC protocols orfine-tuning their parameters, QoS provisioning canalso be achieved by judiciously allocating wirelessresources, i.e., radio bandwidth and power, amongwireless stations. Resource allocation in IEEE802.11 is made possible by the fact that IEEE802.11a and b/g support multiple physical ratesand IEEE 802.11h enables transmission power con-trol (TPC) in IEEE 802.11-compliant devices. Mostof the prior work in this area assumes that theunderlying network is a cellular network (that ismade up of one base station and multiple wirelessstations), and focuses on how to optimally distrib-ute resources over all the wireless stations.

Pavaon and Choi [23] propose a link adaptationmethod to improve the network throughput bydynamically adjusting transmission rates accordingto the current link condition. The link condition isestimated by the received signal strength (RSS) ofreceived frames, and the transmission rate is deter-mined as the maximally allowable rate given in thecurrent link condition. Several states are used todescribe the link condition, each of which is delin-eated by a pair of threshold values of RSS. Thethreshold values are dynamically changed over timeaccording to the success/failure status of frametransmission and the number of retransmissions.The current state which a station is in is also contin-uously adjusted (according to its RSS), every timeeach station receives the frames.

Qiao et al. [53] introduce an energy-efficientscheme, called MiSer, that controls both the trans-mission power and the transmission rate to optimizeresource usage in IEEE 802.11 a/h-compliant wire-less networks. When a station equipped with MiSertransmits a frame, it uses the most energy-efficientpair of power and rate. For this purpose, an optimalrate-power combination table is established offline,and a station looks up the table for every frametransmission. The rate-power table is built uponan energy consumption model that specifies theamount of energy consumed for each protocol oper-ation (e.g., the energy incurred in frame/RTS/CTS/ACK transmission, in the backoff state, in the fro-zen state, and in frame retransmission). In order

to mitigate interference MiSer transmits a CTSframe with a higher power level (strong CTS).Through simulation, they show that combined rateand power allocation outperforms either of thecomponent scheme (power control without rateadaptation or rate adaption without power control),and that rate adaptation is more effective thanpower control within MiSer.

7. Application of QoS-enhanced IEEE 802.11 to new

domains

Targeting primarily for solving the well knownlast mile problem for broadband access [48,50], wire-

less mesh networks (a.k.a. community wireless net-

works) have emerged as a new network architecturein which IEEE 802.11 and its QoS extension canbe used as the underlying MAC protocols [12]. Asshown in Fig. 7, in wireless mesh networks most ofthe nodes are stationary. Only a fraction of nodeshave direct access, and will serve as gateways, tothe Internet. Several nodes serve as relays forward-ing traffic from other nodes (as well as their owntraffic) and maintain network-wide Internet connec-tivity, while the remaining nodes send packets alongdynamically selected ad-hoc paths to gateway nodeswith Internet access. In this section, we use thisnewly emerged network architecture as an example,and outline several QoS-related IEEE 802.11 MACresearch issues that need to be resolved in thisdomain.

In order to adapt IEEE 802.11 and its QoS exten-sion to wireless mesh networks, one has to consider,in addition to arbitrating medium access within thecoverage area of one access point, the challenges ofhow to mitigate interference among neighboringwireless mesh nodes or radio devices that employdifferent PHY/MAC technologies but operate inthe same radio frequency band, how to increasethe network capacity based on channel and spatialdiversity, and how to ensure QoS and fairnessamong users in the presence of interference andvarying channel conditions. As part of the MITroofnet project, Aguayo et al. [3] and Bicket et al.[12] has identified that the channel behavior is quiteunpredictable and packet losses exhibit intermittentpatterns. This is rooted in the fact that the wirelessmedium is ‘‘shared’’ among wireless mesh nodes,and the ‘‘sharing range’’ is determined by eachnode’s transmit power and carrier sense threshold.This is further complicated by physical characteris-tics, such as multi-path fading, interference of other

Internet

AceessPoint

Fig. 7. Wireless mesh network.


non-IEEE 802.11 wireless activities in the 2.4 GHzband, temperature and humidity variation, and exis-tence of objects in between. Nevertheless, as most ofthe connections will carry delay-sensitive, audio/video traffic (that originates from the Internet)toward smart home environments, transport of dataframes of different access categories with certain lev-els of QoS becomes a must, rather than a desirablefeature. While some of the approaches discussed inthis article can be adapted for the purpose in wirelessmesh networks, research along this direction is stillin its infancy. In what follows, we will several recentworks that pertain to IEEE 802.11 MAC. (Althoughdynamic routing is an important issue in wirelessmesh networks, and Draves et al. [25,26] have intro-duced several routing metrics for this purpose, we donot present their work here, due to the fact that it isnot directly related to IEEE 802.11.)

7.1. Capacity improvement through power control,

carrier sense threshold tuning, channel diversity,

and spatial diversity

There are several control knobs that one canexplore to control the sharing range of the wirelessmedium (and ultimately the degree of spatial reuse):the transmit power each node uses for communica-tions, the carrier sense threshold each node uses todetermine if the shared medium is idle, the channelon which the node transmits, the time intervals inwhich each node gain access to the channel.

The first two control knobs have not been exten-sively explored in the context of wireless mesh net-works, except in [65]. One research avenue that canbe pursued is how each node determines its transmitpower and carrier sense throughput (in a distributedand self-adjusting manner) so that (1) network con-nectivity of each mesh node to gateway nodes ismaintained; (2) the MAC-level interference is miti-gated; and (3) the spatial reuse is fully utilized (i.e.,as many concurrent connections as possible areenabled, subject to maintaining necessary SINR fordecoding at certain data rates). Several relatedresearch issues are—what is the relation between thetransmit power and the carrier sense threshold? Willtuning one parameter implies the other? What is thetrade-off between (i) increasing the level of spatialreuse by using smaller power or a larger carrier sensethreshold and (ii) decreasing individual data rateseach node can afford (because of the decrease in theSINR as a result of using smaller power/larger carriersense threshold)? What is the minimal informationthat needs to be exchanged among mesh nodes inorder to realize a (sub-)optimal solution (if any)?

Several researchers have explored the use of mul-tiple radios and multiple channels to improve net-work capacity [4,27,41]. Essentially each wirelessmesh node is equipped with one or more radios thatcan switch among multiple channels. (Recall thatIEEE 802.11b has 11 channels of 22 MHz, threeof which are non-overlapping channels, and IEEE802.11a has 13 non-overlapping channels.) The

2 In AFH, a station identifies ‘‘bad’’ frequencies by measuringthe received signal strength index (RSSI) in each of the narrowfrequency bands. A large value of RSSI serves as an indicationthat other (IEEE 802.11) wireless devices are transmitting in thevicinity of that frequency band. The bluetooth master thendetermines a frequency hopping sequence without using identified‘‘bad’’ frequencies.


number of concurrent connections can be increasedby enabling mesh nodes that transmit at the sametime to use different channels, subject to the con-straint that each sender and receiver pair on thepath to the gateway has to tune to the same channelat the time of communications.

Bahl et al. [5] propose a new link layer protocol,called Slotted Seeded Channel Hopping (SSCH), toimprove the network capacity with the notion ofchannelization. The underlying idea is to exploit fre-quency diversity and non-overlapping channels inIEEE 802.11-compliant networks. SSCH firstdivides the frequency domain into non-overlappingchannels, and then transmit frames over a dynami-cally scheduled channel in a predefined time interval(slot), with the objective of allowing two or morestations within the interference range of each otherto transmit their frames at the same time. Sincethe scheme does not use a centralized controller tocoordinate the usage of channels among stations,each station should maintain a list of channel sched-ules for each of its neighboring stations. This listdetermines the plan of channel switching in futureslots and is collected by periodic broadcast fromneighboring stations.

Another dimension of improving the networkcapacity is through joint temporal and spatial diver-

sity. Specifically, the overall capacity can beincreased by exploiting spatial diversity that existsamong a number of multi-hop paths. Connectionsthat are routed along these paths can be scheduledto take place simultaneously if their transmissionsdo not interfere with each other (significantly). Inthis manner, even if only single channels are avail-able (e.g., without multi-radios or multi-channels)it is possible that the achievable throughput on amulti-hop wireless path is only limited by intra-flowinterference. There are two issues that must beaddressed in order to realize spatial diversity [42].First, the set of paths along which transmissionscan take place with the least inter-flow interferencemust be identified, perhaps with received signalstrength measurements. To this end, one maychoose to use geographic node locations as the ref-erences. Although this information can be readilyobtained by GPS or as part of the static networkconfiguration, it is sometimes quite misleading.For example, even though two next-hop nodes aregeographically close to each other, the interferencemay not be significant if there is an obstacle betweenthem. Lim et al. [42], on the other hand, exploitreceived signal strength (RSS) measurements among

neighbors (to be defined below) as the referencesand construct, based on principal component anal-ysis, a virtual coordinate system. Second, based onthe set of non-interfering paths, a wireless meshnode then has to determine the order with whichit schedules packets of different connections to betransmitted.

7.2. Interference mitigation

In wireless mesh networks, interference mayoccur either among concurrent connections origi-nating from neighboring mesh nodes or in the pres-ence of other non-IEEE 802.11 radio devices thatoperate in the 2.4 GHz band. The first type of inter-ference can be mitigated by addressing the afore-mentioned research issues. The second type ofinterference was termed as the device coexistence

problem in smart home environments and reincar-nates in wireless mesh networks (although at differ-ent scales –access points at the rooftop versuswireless-enabled home appliances).

While mitigation of the second type of interfer-ence is still in its infancy in wireless mesh networks,there has been several R&D efforts in smart homeenvironments. The WiFi Alliance Coexistence Task

Group has been launched to develop spectrum eti-

quette rules [34,45] for sharing unlicensed radiobands (i.e., both the 2.4 GHz and 5 GHz frequencybands). Simultaneously, the IEEE 802.15.2 Coexis-

tence Task Group and the Bluetooth Special Interest

Group (SIG) are also looking into techniques thatmitigate mutual interference between IEEE 802.11and Bluetooth devices when they exist in the closeproximity of one other. For example, Bluetoothspecification V 1.2 has included adaptive frequencyhopping2 [33] as part of its standard. Chiasseriniand Rao [21] propose two Overlap Avoidance

Schemes (OLA), under the assumption that IEEE802.11 and Bluetooth devices can detect interfer-ences caused by the other type of devices. Whetheror not, and how, the approaches proposed abovecan be adapted in wireless mesh networks is worthyof investigation.


7.3. QoS provisioning in the presence of interference

In spite of its importance, there has no MACwork reported that provides (deterministic) servicedifferentiation in the presence of interference. Thecombination of frequency and spatial diversity inthe neighboring area and HCF in IEEE 802.11e(with analytically tuned parameters to ensure deter-ministic QoS) seems to be a viable solution and isworthy of further investigation.

8. Conclusion

We have witnessed the success of IEEE 802.11-based WLANs. Many IEEE 802.11-compliantdevices and services are available in the market bya variety of vendors owing to the availability oflicense-exempt bands and IEEE 802.11 standards.In this article, we provide a comprehensive overviewof techniques for capacity improvement and QoSextension in the IEEE 802.11 protocol family, high-lighting approaches reported in the literature andefforts made in the 802.11 Working Groups. Wesummarize the operations of IEEE 802.11 legacyas well as its extension, introduce several protocolmodeling techniques, and categorize variousapproaches to improve protocol capacity, to pro-vide QoS (by either devising new MAC protocolcomponents or fine-tuning protocol parameters inIEEE 802.11), and to judiciously arbitrate radioresources (e.g., transmission rate and power).

The success of IEEE 802.11-based networks willnot stop here, and various QoS extensions to theIEEE 802.11 protocol family are expected to beimplemented and deployed, as new applicationsemerge and demand for new features. Wireless meshnetworks, for example, have recently emerged andwill likely leverage QoS-enhanced IEEE 802.11 forservice differentiation among traffic of differentaccess categories. As part of the new prospectivesfor IEEE 802.11, we summarize recent work in thisdomain that pertains to IEEE 802.11 MAC, andoutline several MAC research issues that need tobe resolved, in order to make wireless mesh net-works a reality.

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Hwangnam Kim received the B.S.E.degree from the Department of Com-puter Engineering at Pusan NationalUniversity, Pusan, Korea, in 1992, theM.S.E degree from the Department ofComputer Engineering at Seoul NationalUniversity, Seoul, Korea, in 1994, andthe Ph.D. degree from the Departmentof Computer Science at the University ofIllinois at Urbana-Champaign (UIUC)in 2004. He is currently an assistant

Professor in the School of Electrical Engineering at Korea Uni-versity, Korea. He was a post-doctorate fellow in the Department
of Computer Science at UIUC in 2004–2005, a software engineerat Bytemobile, Inc., CA in 2000–2001, and at LG Electronics,
Ltd., Seoul, Korea, in 1994–1999. His research interests are in thearea of network modeling, analysis, and performance evaluationfor wireline/wireless networks.

Jennifer C. Hou received her Ph.D. degreefrom the Department of Electrical Engi-neering and Computer Science The Uni-versity of Michigan, Ann Arbor in 1993and is currently a professor in theDepartment of Computer Science at Uni-versity of Illinois at Urbana Champaign.

She has authored/co-authored morethan 150 archived journal papers and peer-reviewed conference papers. She has beenon the TPC of several major networking,

real-time, and distributed systems conferences/symposiums, and wasthe Technical Program Co-chair of ACM Mobicom 2007, IEEE
MASS 2006, IEEE IPSN 2004, and IEEE RTAS 2000, a ProgramVice Chair of IEEE ICDCS 2002, IEEE ICPADS 2004, IEEE RTSS2004, and the General Co-Chair of IEEE RTAS 2001. She has beenon the editorial board of IEEE Trans. on Wireless Communications,IEEE Wireless Communications Magazine, IEEE Trans. on Paralleland Distributed Systems, ACM/Kluwer Wireless Networks, Com-puter Networks, and ACM Trans. on Sensor Networks. Herresearch focus is in the areas of network modeling and simulation,wireless sensor networks, and cyber physical computing with theemphasis on its use in healthcare. She was a recipient of the ServiceAppreciation Award from ACM in 2004, the Lumley ResearchAward from Ohio State University in 2001, the NSF CAREERaward from NSF in 1996–2000 and the Women in Science InitiativeAward from The University of Wisconsin – Madison in 1993–1995.She is a senior member of IEEE and a member of ACM.
Chunyu Hu received the B.S.E. andM.S.E. degrees from the Department ofAutomation at University of Science andTechnology of in 1997 and 2000, respec-tively, and the Ph.D. degree from theDepartment of Electrical and ComputerEngineering at the University of Illinois atUrbana-Champaign in 2006. She is cur-rently a Research Scientist at Broadcom,Inc., Santa Clara, CA. She was a recipientof Motorola Center for Communications

Fellowship in 2005–2006. Her research interests are in the areas ofresource (power and spectrum) management in wireless networks.

Ye Ge received the B.S.E. degree fromthe Department of Automation at Tsing-Hua University, China in 1997, and thePh.D. degree from the Department ofElectrical and Computer Engineering atthe Ohio State University in 2004. He iscurrently an IT consultant. His researchinterests are in the area of QoS provi-sioning for IEEE 802.11-based networks.

Documents

QoS provisioning in IEEE 802.11-compliant networks: Past, present, and future