IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 53, NO. 4, JULY 2004 1235

A New Collision Resolution Mechanism to Enhancethe Performance of IEEE 802.11 DCF

Chonggang Wang, Bo Li, Senior Member, IEEE, and Lemin Li

Abstract—The medium-access control (MAC) protocol is oneof the key components in wireless local area networks (WLANs).The main features of a MAC protocol are high throughput, goodfairness, energy efficiency, and support priority guarantees,especially under distributed contention-based environment. Basedon the current standardized IEEE 802.11 distributed coordinationfunction (DCF) protocol, this paper proposes a new efficient col-lision resolution mechanism, called GDCF. Our main motivationis based on the observation that 802.11 DCF decreases the con-tention window to the initial value after each success transmission,which essentially assumes that each successful transmission is anindication that the system is under low traffic loading. GDCFtakes a more conservative measure by halving the contentionwindow size after consecutive successful transmissions. This“gentle” decrease can reduce the collision probability, especiallywhen the number of competing nodes is large. We compute theoptimal value for and the numerical results from both analysisand simulation demonstrate that GDCF significantly improve theperformance of 802.11 DCF, including throughput, fairness, andenergy efficiency. In addition, GDCF is flexible for supportingpriority access by selecting different values of for different traffictypes and is very easy to implement it, as it does not requires anychanges in control message structure and access procedures inDCF.

Index Terms—IEEE 802.11 DCF, wireless local area network(WLAN).

I. INTRODUCTION

RECENTLY, we have witnessed a rapid development anddeployment of wireless local area networks (WLANs),

which in return has fueled the development in the standardiza-tion organization, such as the IEEE 802.11 working group, toimprove its performance. One of the key components in WLANis a medium-access control (MAC) protocol that primarilydetermines its performance. MAC protocols are commonlyused in multiple-access environments, where multiple nodescompete for certain shared resources. The main functionality

Manuscript received November 9, 2003; revised January 9, 2004. B. Li’s re-search was support in part by grants from the Research Grant Council undercontracts HKUST6196/02E and HKUST6402/03E, an National Science FundsCouncil/RGC joint grant under contract N_HKUST605/02, and a grant fromMicrosoft Research under contract MCCL02/03.EG01.

C. Wang is with the the Special Research Centre for Optical Internet & Wire-less Information Networks (ICOIWIN), ChongQing University of Posts andTelecommunications (CQUPT), Chongqing 400065, P. R. China, on leave fromthe Department of Computer Science and Computer Engineering, University ofArkansas, Fayetteville, AR 72701 USA (e-mail: cgwang@cs.ust.hk).

B. Li is with the Department of Computer Science, The Hong Kong Univer-sity of Science and Technology, Hong Kong, P.R. China (e-mail: bli@cs.ust.hk).

L. Li is with the University of Electronic Science and Technology of China,Chengdu 400065, P. R. China (e-mail: lml@uestc.edu.cn).

Digital Object Identifier 10.1109/TVT.2004.830951

of MAC protocols is to arbitrate access for the shared transmis-sion medium [1]. The performance metrics of interest includethroughput, fairness, packet transmission delay, stability, andalso priority in an environment supporting multiservices. Inaddition, in a WLAN, the energy efficiency is also a majorperformance index of interest.

In IEEE 802.11 standard [2], channel access is controlled bythe use of interframe space (IFS) time between the frame trans-missions. Three IFS intervals that have been specified by 802.11standards include short IFS (SIFS), point coordination functionIFS (PIFS), and distributed coordination function (DCF)-IFS(DIFS). The SIFS is the smallest and the DIFS is the largest.

There are two access mechanisms including point coordina-tion function (PCF) and DCF. PCF is a centralized MAC algo-rithm used to provide contention-free service, while DCF uses acontention-based algorithm to provide access to all traffic. PCFis built on top of DCF and regulates transmission through a cen-tralized decision maker or point coordinator, which makes use ofPIFS when issuing polls. Because PIFS is smaller than DIFS, thepoint coordinator can seize the medium and lock out all asyn-chronous traffic (which uses DIFS to access channel) while itissues polls and receives responses. This paper focuses on DCFand we will give a brief introduction later.

In 802.11 DCF, a node starts its transmission if the mediumis sensed to be idle for an interval larger than the distributedinterframe space (DIFS). If the medium is busy, the node willdefer its transmission until a DIFS is detected and then generatea random backoff period (backoff timer) before retransmission.The backoff timer will be decreased as long as the channel issensed idle, frozen when the channel is sensed busy, and re-sumed when the channel is sensed idle again for more thana DIFS. A node can initiate a transmission when the backofftimer reaches zero. The backoff timer is uniformly chosen inthe range CW). CW is known as contention window, whichis an integer with the range determined by the PHY character-istics CW and CW . After each unsuccessful transmis-sion, CW will be doubled until reaching the maximum valueCW , where equals to (CW . Aftereach successful transmission, CW will reset to the minimumvalue CW . In 802.11 DCF for the DSSS physical channel,CW , CW , and .

802.11 DCF defines two channel-access modes: basic and re-quest to send/clear to send (RTS/CTS) base access. In basic ac-cess mode [Fig. 1(a)], the destination node will wait for a SIFSinterval immediately following the successful reception of thedata frame and transmit a positive ACK back to the source nodeto indicate that the data packet has been received correctly. If thesource node does not receive an ACK, the data frame is assumedto be lost and the source node will schedule the retransmission

0018-9545/04$20.00 © 2004 IEEE

1236 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 53, NO. 4, JULY 2004

Fig. 1. IEEE 802.11 MAC mechanism.

with the doubled CW for backoff timer. When the data frameis being transmitted, all the other nodes hearing the data frameadjust their network-allocation vector (NAV), which is used forvirtual carrier sense at the MAC layer, correctly based on theduration field value in the data frame received. This includesthe SIFS and ACK frame transmission time following the dataframe.

In RTS/CTS-based access mode, nodes transmit data utilizingspecial short RTS and CTS frames prior to the transmission of anactual data frame in order to shorten the collided time interval.As shown in Fig. 1(b), the node that needs to transmit a packet is-sues a RTS frame. When the destination receives the RTS frame,it will transmit a CTS frame after the SIFS interval, immediatelyfollowing the reception of the RTS frame. The source node isallowed to transmit its packet if and only if it receives the CTScorrectly. At the same time, all the other nodes will update theNAVs based on the RTS from the source node and the CTS fromthe destination node, which helps to overcome the hidden ter-minal problem. In fact, the node that is able to receive the CTSframes correctly can avoid collisions even when it is unable tosense the data transmissions from the source node. If a collisionoccurs with two or more RTS frames, less bandwidth is wastedas compared to the situation where larger data frames can col-lide in the basic access mode.

The remainder of this paper is organized as follows. Section IIreviews the existing work and discusses the main features in ourproposed GDCF. Section III introduces the new collision-reso-lution mechanism called GDCF. Theoretical analysis of GDCF,including normalized throughput and some other metrics, willbe given in Section IV. In Section V, we present numerical re-sults of GDCF and compare with that those of the IEEE 802.11DCF protocol. Section VI concludes this paper.

II. RELATED WORK

This paper focuses on the contention-based MAC protocolsused in WLAN, specifically IEEE 802.11 DCF [2]. Theanalysis in [3] demonstrated that the throughput and fairness of802.11 DCF could significantly deteriorate when the numberof nodes increases. Several recent proposals have addressedthis issue [4]–[7]. Furthermore, given the need to supportmultimedia applications and to consider the energy efficiencyin mobile devices, there also are protocols to address a priorityscheme in [8] and [9] and the energy efficiency issue in [10].

Cali et al. [4] proposed a dynamic and distributed algorithm,IEEE 802.11 , which allows each node to estimate the numberof competing nodes and to tune its contention window to theoptimal value at run time. Results from simulations showed thatthe throughput of IEEE 802.11 is very close to the theoreticalupper bound. DCF , proposed in [5], is a new ACK-integratedmechanism that combines the TCP ACK with MAC level ACKand obtains the improved throughput. One of the limitationsis its ineffectiveness for other flows, such as UDP. It alsoviolates the layering principle that leads to the complicationin MAC ACK message structure. Peng [6] proposed a newmeasurement-based algorithm to adaptively configure theoptimal value of the initial CW value to improve the throughputand fairness. However, it also needs to compute currentchannel status at run time and adjusts the RTS/CTS messagestructure. The fast collision resolution (FCR) is another MACprotocol proposed in [7], which actively redistributes thebackoff timer for all competing nodes, thus allowing the morerecent successful nodes to use smaller contention window andallowing other nodes to reduce backoff timer exponentiallywhen they continuously meets some idle time slots, insteadof reducing backoff timer by 1 after each idle time slots, asin the original IEEE 802.11 DCF. FCR can resolve collisionsmore quickly than 802.11 DCF and obtains higher throughput,but FCR itself can inversely affect the fairness unless it iscombined with additional fair scheduling mechanism, as shownin [7]. Residual-energy-based tree splitting (REBS) [10] is anenergy-efficient collision-resolution algorithm that can be usedin the wireless ad hoc networks. REBS differentiates and splitsall the competing nodes according to their residual energy andassigns the node with the least residual energy to seize thechannel with the highest priority.

We propose a new collision-resolution mechanism calledGDCF, which is a simple variation of 802.11 DCF, yet cansignificantly improve throughput and fairness. GDCF enablesthe priority support for multimedia application and obtainsbetter energy efficiency than DCF itself. There are severalunique advantages in the proposed GDCF. Comparingit toIEEE 802.11 in [4] and self-adapt DCF in [6], GDCF issimpler in that it does not need to estimate network parameterssuch as competing node number in [4] and channel statusin [6], although there is a Kalman filter-based algorithm tomeasure the number of competing nodes in [11]. Comparing itto DCF in [5], GDCF can support any upper protocols (TCP

WANG et al.: A NEW COLLISION RESOLUTION MECHANISM TO ENHANCE THE PERFORMANCE OF IEEE 802.11 DCF 1237

Fig. 2. Collision-resolution stage evolution in GDCF and 802.11 DCF.

or UDP) and does not need to change the RTS/CTS messagestructure. Comparing it to the FCR algorithm in [7], GDCFachieves better fairness and simplicity and can easily supportpriority or quality-of-service (QoS) differentiation effectively.GDCF maintains excellent compatibility with the original IEEE802.11 DCF. In summary, the proposed GDCF achieves betterthroughput, fairness, and energy efficiency and enables prioritysupport. In addition, it does not need to estimate the competingnode and channel status; thus, it is simple for implementation.

III. PROPOSED GDCF ALGORITHM

From the discussions in the Section II, we can see that802.11 DCF resolves collision through CW and backoff stage[Fig. 2(a)]. In the initial backoff stage (stage 0), the value ofCW has the minimal value CW . After each transmissioncollision, the backoff stage will be increased by 1 and the CWwill be doubled until it reaches the maximum, CW . Aftereach successful transmission, the backoff stage will resume toinitial stage 0 and the CW will be reset to CW , regardlessof network conditions such as the number of competing nodes.In this method, we refer to heavy decrease, which tends towork well when there are only a few competing nodes. Whenthe number of competing nodes increases, it will be shown tobe ineffective, since new collisions can potentially occur andcause significant performance degradation.

For example, assuming that the current backoff stage iswith contention window CW and that there isa successful transmission, the next backoff stage will be stage 0with CW , according to 802.11 DCF specifications forDSSS PHY [1]. But if the number of current competing nodesis large enough, , the new collision will likely occur atthe backoff stage 0. The main argument is that since the currentbackoff stage is , some collisions must have occurred recently.Now if the number of current competing nodes is larger thanor close to CW and if the backoff stage is reset to 0 after asuccessful transmission, there is a high probability that somenew collision(s) will happen. Certainly, the number of currentcompeting nodes may be smaller than CW if there are sev-eral consecutive successful transmissions at the backoff stage .Under this case, we can effectively begin to decrease the CW.This is the primary principle used in GDCF.

GDCF attempts to avoid useless collisions through the“gentle” decrease of contention window, referred as gentleDCF or GDCF. The collision-resolution stage evolution inGDCF is presented in Fig. 2(b). The difference between GDCF

and DCF is that GDCF will halve CW value if there areconsecutive successful transmissions. On the contrary, DCFwill reset CW once there is a successful transmission or theretry count overruns the threshold , so GDCF needs tomaintain a counter for recording the number of continuoussuccessful transmissions up to now. This counter will reset tozero after each collision, because what it records is the numberof continuous successful transmissions, not the number of totalsuccessful transmissions. According to the channel status, thedetailed collision resolution process in GDCF is as follows.

• Collision: Similar to the operations in DCF, GDCF willdouble the contention window and select a backoff timervalue uniformly from CW]. But GDCF also needs toreset the counter for recording the number of consecutivesuccessful transmissions.

• Successful transmission: If there are consecutive suc-cessful transmissions, GDCF will halve the CW and selecta backoff timer value uniformly from CW]. Then, thecounter for recording the number of continuous successfultransmissions is reset to zero. Otherwise, GDCF increasescounter for the number of consecutive successful trans-mission and keeps the contention window unchanged.

• Idle: If the channel is idle, GDCF also reduces the backofftimer by 1, the same as in DCF.

In another word, CW in GDCF is gently and gradually de-creased after consecutive successful transmissions. If there area few competing nodes, many consecutive successful transmis-sions can appear and the backoff stage gradually goes downto the initial stage 0. If there are many competing nodes, theprobability that the backoff stage will be down to initial stage 0are very small and the backoff stage will oscillate between twolarge stages and with a high probability. This behaviorbrings two advantages. First, it will decrease the collision prob-ability and improve the system throughput. Second, it will ob-tain better fairness because GDCF maintains all the nodes inthe same stage (with the same CW) even if after several con-secutive successful transmissions , especially under largenode number. However, nodes in DCF will stay in a differentstage (with different CW) after successful transmission, sinceit is reset to initial stage 0 after each successful transmission.GDCF can easily be extended to support priority applicationsor QoS differentiation through configuring te value for dif-ferent type of applications. A simple method is to let high-pri-ority applications choose smaller , while low-priority applica-tions with larger . Evidently, the nodes with smaller can seize

1238 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 53, NO. 4, JULY 2004

the channel more quickly and result in lower access delay. Thisis particularly important for some real-time multimedia appli-cations. We will exploit and evaluate this capability of GDCF inSections IV and V through simulations.

One issue in GDCF is how to set the parameter . The intu-ition is that in the environment with many (or a few) competingnodes, it requires large (or small) value of . If the number ofcurrent competing node number can be obtained, we can intelli-gently adjust the parameter , but it usually is expensive to pre-cisely obtain the number of competing nodes in a distributed dy-namic environment, where nodes consistently move or/and fre-quently switches on or off. However, we will show that the pa-rameter is not too sensitive to the number of competing nodesin Section IV. In addition, we prove that the short range 4 8is the optimal value for if the number of competing nodes islarger than 10.

IV. GDCF PERFORMANCE ANALYSIS

In this section, we will analyze the performance of GDCF,discuss how to choose parameter , and investigate the perfor-mance of GDCF when supporting priority traffics.

A. Saturation Throughput

First, we will deduce the normalized system throughput ofGDCF, which is equal to the ratio between “average payloadduration in a slot time” and “average length of slot time,” usingsome similar procedures and symbols in [3] and [5]. Let bethe probability that a transmitted packet collides, be the prob-ability that a node transmits in a randomly chosen slot time, bethe backoff stage, be the maximal backoff stage [Fig. 2(b)],

be the backoff time slot, be the bidimensional stateof each node, be the stable probability of state , and

be the one-step transition probability fromstate to state . For convenience, we use CWand interchangeably in the following discussions.

After every transmission collision, GDCF will back off(increase the stage ) and double the contention window, so

.The backoff timer will decrease by 1 if the channel is sensedidle, so .If there are continuous successful transmissions, GDCFwill decrease the backoff stage and halve the contentionwindow; otherwise, the node will stay at the current backoffstage and keeps the contention window unchanged. Wecan approximate this transition probability as follows.

and, where and let . Then, we can

easily construct corresponding transition equations of GDCFsMarkov model (see Fig. 4) according to its collision-resolutionprocess in Fig. 3.

Fig. 3. Collision-resolution process in GDCF.

The nonnull one-step transition probabilities can be com-puted as

,

,

,

, .(1)

Let and we can aggregate thestate into a single state , so it is easyto get that

(2)

For each , also has the relationship shownin (3) at the bottom of the page.

With (2) and , (3) can be simplified as

(4)

Because the sum of stationary distribution for all states mustbe equal to 1, therefore

(5)

.(3)

WANG et al.: A NEW COLLISION RESOLUTION MECHANISM TO ENHANCE THE PERFORMANCE OF IEEE 802.11 DCF 1239

Fig. 4. Markov chain model of GDCF.

In (5), can be computed using (2) and is standardizedin 802.11b as follows (for DSSS PHY in 802.11, ):

.(6)

Replacing (5) with (2) and (6), we can get the value of in(7) as

(7)

Then, the probability that a node transmits in a randomlychosen slot time can be expressed as

(8)

For convenience of the following discussions, we write theMarkov modeling results of (8) into the following function :

(9)

In the stationary state, a node transmits a packet with prob-ability , so the probability that the transmission is collidedconditioned that there are transmissions can be computed asfollows, because collision must happen if there are at least twonodes to transmit simultaneously:

(10)

where is number of competing nodes. Equations (9) and(10) can be solved by numerical computing methods to obtainthe value of or . Then, we can get the normalized systemthroughput as

(11)

where is the probability that there is at least one transmissionin the considered slot time. is the probability that a transmis-sion is successful. and are the average time the channel issensed busy because of a successful transmission or collision,respectively. The represents the average packet length and

is the duration of an empty slot time. and can be com-puted as

(12)

and can be computed for basic access mode and RTS/CTSaccess mode, respectively, as shown in (13) and (14) at thebottom of the next page where isthe packet header, is the propagation delay, and is theaverage length of the longest packet payload involved in a col-lision. In this paper, all the packets have the same fixed size, so

.We have calculated the normalized system throughput of

GDCF and DCF according to (11). The results are presentedin Figs. 5 and 6, respectively for basic access and RTS/CTSaccess modes. It can be easily observed that the influences onthroughput resulted from factors such as access mode, packetlength, and value in GDCF. First, for both DCF and GDCF,RTS/CTS access mode and/or large packet size will bring

1240 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 53, NO. 4, JULY 2004

(a) (b)

Fig. 5. Normalized throughput of basic access mode. (a) Packet length 500 B and (b) packet length 1500 B.

(a) (b)

Fig. 6. Normalized throughput of RTS/CTS access mode. (a) Packet length 500 B and (b) packet length 1500 B.

higher throughput. GDCF can obtain improved performancefor both access modes, but the improved performance under thebasic access mode is much larger. Under the basic access mode,the improved performance will increase with the increasingof the packet length, because the effect resulted from loweredcollision probability will be more apparent under long packetlength. On the contrary, under RTS/CTS access mode, whenthe packet length is small, the collided slot is more comparable,so the improved performance will decrease with the increasingof packet length. As the results in Fig. 5, GDCF withobtains higher throughput than under the basic accessmode. However, both 4 and 8 obtained nearly the samethroughput under the RTS/CTS mode, so the problem is howto choose the optimal for different competing node numbers.This problem will be further analyzed in Section IV-B.

B. Optimal Value for

It can be seen that the value will heavily influence thethroughput performance. The problem is of which value of

is the most optimal for throughput conditioned that the systemparameters, such as in Table I, are given. We can use thefollowing method to determine the optimal value of . Let uswrite (11) to the following form:

(15)

In order to maximize , must be minimal, so letand we can get the optimal value of as

When is too large, we can use the approximation

(13)

(14)

WANG et al.: A NEW COLLISION RESOLUTION MECHANISM TO ENHANCE THE PERFORMANCE OF IEEE 802.11 DCF 1241

TABLE ISYSTEM PARAMETERS (802.11 DSSS)

Let [ can be computed according to (13) and(14) for basic access and RTS/CTS access mode, respectively]be the normalized average collision length in the number of slottimes; then, we can finally obtain the optimal value of as

(16)

After obtaining the optimal value of , we can easily obtainthe optimal value of according to (9) and (10). Because theoptimal value of is dependent on node number and the nor-malized average collision length [see (16)], so the optimalvalue of is also dependent on and . However, we can seefrom (13) that is related to packet length under basic accessmode, so the optimal value of under the basic access modeis also dependent on packet length. When the packet length islarge, the collision will cause heavy influence on throughput,so it is required to choose large value of . Under the RTS/CTSaccess mode, because the normalized average collision length

is independent of packet length, the optimal value of isalso irrelevant to it, so we only present the results of RTS/CTSaccess mode in Fig. 7, which also presents the minimal andmaximal value of to make the GDCF throughput higher thanDCF, where the case of means that DCF obtains higherthroughput at this time (for example, when node number is equalto 2 and 4). It can be observed that: 1) When , GDCFcan improve throughput performance (see the dashed curve la-beled with “minimal ”), even through the original purpose ofGDCF is to improve performance for large node number and2) when , the optimal value of for any node number

is smaller than the minimum of maximal value of , sowhen , GDCF with any value of in the range of 18 will obtain higher throughput than DCF. Fig. 6 conclusivelydemonstrates that the optimal can be obtained in the narrowrange 4 8 and nearly independent of node number when thenode number is larger than 10. This implies that even thoughone of the original arguments in GDCF is to improve perfor-mance for large node number, to our pleasant surprise, this es-sentially shows that GDCF obtains better performance even ifthe node number is small when is chosen properly. We willfurther verify this result through simulations in Section V.

C. Performance Under Priority Traffics

Assume that total competing nodes can be divided intopriorities or groups. Each group has competing nodes

and is configured with parameter . It is obvious that the groupwith small value of will has the large probability and thatthe node belonging to this group will transmit in a slot timeand have large probability that the transmissions from thisgroup is collided. Let the normalized throughput obtained bygroup be . Thus, we can describe each group using thefive-tuple . In the following, we will calculatethe throughput ratio between any two groups and .

We can write the results from Markov-modeling into the fol-lowing function :

(17)

When the transmission is issued by only a node of groupand all other nodes in this group and other group keep idle, thistransmission will succeed. Otherwise, the transmission will becollided. So the probability can be computed as

(18)

For given parameters and , the numerical results ofand can be obtained through solving (17) and (18). Then,the throughput ratio between any two groups and can becalculated as

(19)

Without loss of generality, it is assumed that. According to the principle of GDCF, the node with small

has the large probability that it will transmit in a slot time, so. From (18) we can easily get the following

conclusion:

(20)

Through selecting different combination of for competingnodes, GDCF can make the nodes with smaller obtain lowerMAC access delay (because of lower probability ) and largerthroughput (and corresponding total queuing delay if the trafficarrival rate is limited). This interesting property in GDCF canbe directly utilized to support differentiated QoS in the MAClayer. For example, if some node needs supporting real-time ap-plications , has better channel quality, or has lower energy, wecan let it choose a small value of to obtain a lower delay ofreal-time application, optimal throughput, or higher energy ef-ficiency and longer system alive time. Although there are someother QoS-supporting approaches, such as fair queuing in theMAC layer, GDCF is very simple and flexible. We will furtherexploit this property through simulations in Section V.

Although is assumed to be an integral up to now, can bealso configured to be a real number. Then, GDCF needs only aminor change, as presented in Fig. 8. However, this change willnot influence the simplicity of GDCF.

In Fig. 8, conSuccNum is the continuous successful transmis-sions and deficit is the additional parameters used to count theaccumulated deficit quantum in each node. Through using pa-rameter deficit, GDCF can guarantee that node will halve CW

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Fig. 7. Optimal c value under the RTS/CTS access mode (when c = 0, which means that DCF is the best choice at this time).

Fig. 8. GDCF algorithm when c is a real number.

once every continuous successful time, on average, even ifis a real number.

V. SIMULATION RESULTS

This section presents the extensive simulation results ofGDCF. Since the collision probability has more influenceon the basic access mechanism than on the RTS/CTS-basedaccess mechanism, GDCF is sure to obtain better performanceimprovement under the nasic access mechanism than underthe RTS/CTS-based access mechanism as that shown inSection IV. Therefore, here we only present results using theRTS/CTS-base access mechanism to observe the performanceimprovement in GDCF. The same parameters in Table I are alsoused in simulations. The nodes are uniformly distributed in the100 m 100 m two-dimensional (2-D) square spaces. We usethe shadowing propagation model in order to match the realenvironment and assume that the 90% packet can be correctlyreceived within the distance of 150 m. The main performancemetrics of interest are system throughput, fairness index, RTSfailure ratio, and QoS-supporting capability. The throughputis used to quantify the throughput gain obtained by GDCF.We adopt the use of fairness index defined in [12], as it is acommonly accepted metric. The RTS failure ratio (RFR) canbe used to evaluate the energy cost to transmit packets. If RFRis large, then the energy cost will be high because more RTSmessages are collided and more energy will be wasted. Thesimulation time is selected to be 100 s and all the followingresults are the average values obtained in ten simulations. In allthe figures of this section, we use to represent the 802.11DCF algorithm, for convenience of comparison with the GDCFalgorithm.

A. Saturation Traffic

In this section, the traffic is configured to saturate the system,so there are always some competing nodes attempting to

transmit packets. We will investigate the throughput, fairness,and RTS failure ration in GDCF and DCF, respectively. Theoptimal value of is obtained and compared to the analyzedresults in Section IV.

Experiment A.1—TCP Traffic: In this case, all traffic sourcesare TCP (NewReno) flows, whose packet size and window sizeare 1460 B and 40 packets, respectively. We collect the RTSfailure ratio, saturation throughput, and fairness for a differentnumber of competing node.

As analyzed in Section IV, GDCF obtains higher throughputthan DCF and keeps good fairness simultaneously. The resultsare presented in Table II and Fig. 9, respectively, for a smallernode number and large node number. Observing from Fig. 9 andTable II, GDCF obtains higher throughput and better fairnessthan DCF. When is between 4 8, GDCF improves throughputby about 15%–20% for large node number [Fig. 9(a)] and a bitfor small node number (Table II). GDCF also maintains goodfairness property while obtaining higher throughput. Underlarge node number, GDCF has much better fairness than DCF,especially when [Fig. 9(b)]. The larger value of (forexample, ), the better fairness will be obtained becauselarge value of will make all competing nodes stay in thesame backoff stage with high probability. Although the fairnessof GDCF can be worse than DCF when the node number issmall (Table II), GDCF still has good fairness ( 0.9) and thisdeterioration is only slight. If the fairness index is smaller than1, the bandwidth obtained in some nodes will be smaller thanthe average bandwidth by . According tothe definition of fairness index in [11], if the fairness index isthe same, the decreased bandwidth will be decreased whenthe total node number is small. So the fairness deteriorationin GDCF under the small node number will cause slighterinfluence on bandwidth sharing than the fairness deteriorationin GDCF under a large node number. Moreover, the totalsystem throughput and the average bandwidth of each nodeunder small node is large than that under large node, whichwill make the influence resulted from fairness deterioration inGDCF under small node number more slight.

Fig. 10 presents the results of RTS failure ratio, from whichit can be seen that: 1) GDCF has smaller RTS failure ratio thanDCF for any value; 2) the RTS failure ratio of GDCF will de-crease when value increases, but it will keep at a certain level(for example, the two curves for and are nearly

WANG et al.: A NEW COLLISION RESOLUTION MECHANISM TO ENHANCE THE PERFORMANCE OF IEEE 802.11 DCF 1243

TABLE IITHROUGHPUT AND FAIRNESS: SMALL NODE NUMBER (c = 0 IS FOR DCF)

Fig. 9. Throughput and fairness-large node number (c = 0 is for DCF).

Fig. 10. RTS failure ratio-saturation traffic.

overlapped); and 3) the gain of the RTS failure ratio obtained byGDCF is more apparent when the node number is large; 4) theRTS failure ratio will increase when the node number increasesand GDCF decreases the backoff stage by only 1 if and only ifthere are continuous transmissions, so it will keep larger con-tention window with high probability than DCF and has largerRTS success ratio independent of node number, as in Fig. 10.Also, it is reasonable that RTS failure ratio will increase when

decreases or node number increases. The results do show thanGDCF has lower RTS failure ratio than DCF. This means thatnodes in GDCF issue fewer RTS message than DCF for trans-mitting the same information volume.

Fig. 11. Packet dropping in the MAC layer.

In simulations, we also collect the packet drops in the MAClayer throughout the whole simulation time and Fig. 11 presentsthe results for different number of competing node. It can beobserved that there are few packet drops in the MAC level underGDCF. On the contrary, DCF cause many packet drops in MAClevel, especially when there are many competing nodes.

Experiment A.2—UDP Traffic: We also change the inputtraffic from TCP into UDP and vary the total traffic densityfrom 0.1 to 1.0. GDCF also obtains the improved throughput.Because the fairness under each case is close to 0.99 and haslittle difference under GDCF and DCF, we only present theresults of system throughput (see Fig. 12). When UDP traffic

1244 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 53, NO. 4, JULY 2004

Fig. 12. System throughput—UDP traffic.

density is smaller than the system saturation throughput, thesystem throughput of DCF and GDCF will increase with theUDP density. After the UPD density overruns the saturationthroughput, the system of DCF and GDCF will nearly keepconstant. Similar to the results in Fig. 10, GDCF also obtains abit higher throughput than DCF under this case. It can also beobserved that the throughput under UDP is somewhat smallerthan that under TCP, especially when the node number is equalto 50. The reason is may be that the factually competing nodenumber may smaller than the total node number because ofTCP ACK-based flow and congestion-control mechanism.

In summary, GDCF obtains higher throughput and better fair-ness at the same time. On the selection of the value, it canbe seen from the above discussions (Table II and Fig. 9) thatGDCF with 1 4 has better performance when the node numberis very small (between 2 and 6) and 4 8 is more suitablefor cases with a large node number. Considering the tradeoffbetween throughput decrease (under very small node number)and throughput and fairness improvement (under large nodenumber), 4 8 is the better choice if the number of competingnode cannot be known. Recall that the optimal value (4 8)from theoretical in Section IV; we can conclude that the simu-lation results are very consistent with the theoretical results inFig. 7, so we can choose value from 4 8 in practical deploy-ment.

B. QOS Supporting

Recently, it was shown in 802.11e and enhanced DCF(EDCF) [8] that it can provide QoS differentiation by con-figuring small (or large) and and DIFS forhigh- (or low-) priority traffic. In a DCF environment, however,it is hard to deploy admission control for the high-prioritytraffic, so EDCF will cause performance deterioration if mostof applications are high-priority traffics with small and

value. One of the properties in GDCF is that the nodewith smaller will get the access chances more quickly. We canuse this property directly to support QoS differentiation. In thissection, we investigate the priority-supporting ability in GDCF.

Fig. 13. Experiment B.1—throughput ratio.

Experiment B.1: In this experiment, we divided the totalnodes into two groups with the same node number. Thevalue in the first group is fixed at , and the for thesecond group is varied from 1.1 to 10. UDP traffic is used insimulations. According to the analysis in Section V-A, the firstgroup will obtain higher throughput than the second group,which has larger value of . We calculate the throughput ratiobetween the two groups using (19) and compare it with thesimulation results (see Fig. 13). When , there is a largethroughput decrease in the second group. When , thethroughput ratio curve is gradually flat and the increase of

has no apparent effect. Although there is some mismatchbetween analysis and simulation results, the first group withsmaller does obtain higher throughput. On the contrary, thesecond group obtains lower throughput. The throughput ratiobetween the two groups will decrease with the increasing of ,so we can adjust the throughput ratio between different typesof traffic in deployment through configuring .

As for node number , we also collected instanta-neous throughput and delay for each group. Fig. 14(a) presentsthe instantaneous throughput of group 1 (G1) and group 2 (G2)and Fig. 14(b) illustrates the instantaneous delay of randomlychosen nodes from these two groups, respectively. It can be seenthat group 1 with a smaller value of obtains higher throughputand lower delay, so GDCF provides a simple and flexible ap-proach to supporting differentiated QoS.

Experiment B.2: In this experiment, two types of traffic areconfigured: 1) high-priority traffic -UDP sourceand 2) low-priority traffic -TCP source. Theparameters used in TCP are the same as in Section V-A. Thepacket size of UDP is equal to 1500 B. The traffic density ofthe UDP source is varied from 0.05 to 0.6. In simulations, thenode number is set to 20 and 40, respectively, and one half ofnodes support UDP and the other half support TCP. We collectthe UDP delay, UDP delay jitter, TCP delay, and total systemthroughput. Fig. 15 shows that: 1) the average delay and delayjitter of UDP source in DCF quickly increase with its trafficdensity; 2) the average delay and delay jitter of UDP sourcein GDCF is not sensitive to the competing node number andtraffic density of UDP source; and 3) UDP source has verylower delay (0.02–0.035 s) and delay jitter under GDCF thanthat under DCF. At the same time GDCF achieves higher system

WANG et al.: A NEW COLLISION RESOLUTION MECHANISM TO ENHANCE THE PERFORMANCE OF IEEE 802.11 DCF 1245

Fig. 14. Experiment B.1—instantaneous throughput and delay.

Fig. 15. Experiment B.2—UDP delay and delay jitter.

Fig. 16. Experiment B.2—Total throughput and TCP delay.

throughput (Fig. 16), but slightly bigger TCP delay than DCF(Fig. 16).

We have evaluated GDCF through such thee types ofsimulations: TCP traffic, UDP traffic, and combined traffic.The conclusions that GDCF improve better performance than

DCF can be drawn from the extensive simulations. GDCF firstacquires about 15%–20% higher saturation throughput thanDCF. Second, GDCF simultaneously maintains good fairness.When the number of competing node is some large, GDCF hasbetter fairness than DCF. Third, GDCF has lower RTS failure

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ratio, which means that GDCF will issue smaller RTS messageand consumes fewer energy than DCF in order to transmit thesame number of packets. GDCF also drops fewer packets at theMAC level, while DCF drops many packets in the MAC level,so GDCF realize better integration of the high-protocol layerand the low-MAC layer. Finally, GDCF can effectively supportpriority traffic. Through simple parameter configuration, GDCFcan make high-priority traffics get much lower delay and delayjitter and provide differentiated QoS, and keep total higherthroughput at the same time. GDCF needs neither changesof RTS/CTS structure nor measures of node number; thus, itis very easy to deploy. The GDCF proposed in this paper isdeterministic approach. It is certain that the behavior of GDCFcan be realized through some probability-based approaches.For example, we can decrease backoff stage by 1 and halvethe contention window with probability after each successfultransmission. We also investigate the performance of thisapproach and find out that it realizes similar performance withGDCF and that the optimal value of is about 0.2. But the fair-ness of this approach is a bit worse than GDCF because of itsprobabilistic behavior in contention resolutions. In future work,we will plan to evaluate the priority–guarantee mechanism inGDCF and its performance under multihop environments.

VI. CONCLUSION

This paper investigates the MAC protocol for WLAN andthe corresponding collision-resolution algorithm and proposesan effective algorithm, GDCF, based on 802.11 DCF proto-cols. Theoretical analysis and simulations are carried out, whichshow that the proposed GDCF brings several benefits: 1) it ob-tains higher throughput than traditional DCF, especially with alarge number of competing nodes; 2) it maintains a good fair-ness property; 3) GDCF has a lower RTS failure ratio and issuesless RTS messages than DCF in order to transmit the same in-formation volume, so it is more energy efficient; 4) GDCF dropsfewer packets in the MAC level and can easily extend to supportpriority application with the flexibility of selecting differentvalues; and 5) GDCF is very easy to be deployed, as it does notneed to estimate a competing node number or change the con-trol message structure and access procedures in DCF.

In our future work, we will investigate the performance ofGDCF when the number of nodes vary frequently and furtherstudy comparison between the performance of GDCF when sup-porting QoS and 802.11e [8].

REFERENCES

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[9] Y. Xiao, “A simple and effective priority scheme for IEEE 802.11,”IEEE Commun. Lett., vol. 7, pp. 70–72, Feb. 2003.

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Chonggang Wang received the B.Sc. (Hons.) degreefrom Northwestern Polytechnic University, Xi’an,China, in 1996 and the M.S. and Ph.D. degrees incommunication and information systems from theUniversity of Electrical Science and Technology,Chengdu, China and Beijing University of Posts andTelecommunications, Beijing, China, in 1999 and2002, respectively.

From September 2002 to Novemeber 2003, hewas an Associate Researcher with the Departmentof Computer Science, The Hong Kong University of

Science and Technology, Hong Kong, P. R. China. He currently is a VisitingProfessor with the Special Research Centre for Optical Internet & WirelessInformation Networks (ICOIWIN), ChongQing University of Posts andTelecommunications (CQUPT), Chongqing, P. R. China, and a PostdoctoralResearch Fellow with the University of Arkansas, Fayetteville.

Bo Li (S’93–M’95–SM’99) received the B.S.(summa cum laude) and M.S. degrees in computerscience from Tsinghua University, Beijing, P. R.China, in 1987 and 1989, respectively, and the Ph.D.degree in electrical and computer engineering fromthe University of Massachusetts, Amherst, in 1993.

From 1994 to 1996, he worked on high-perfor-mance routers and asynchronous transfer mode(ATM) switches with the IBM Networking SystemDivision, Research Triangle Park, NC. SinceJanuary 1996, he has been with Computer Science

Department, the Hong Kong University of Science and Technology, wherehe is an Associated Professor and Codirector for the ATM/Internet protocol(IP) cooperate research center, a government-sponsored research center. Since1999, he has also been an Adjunct Researcher with Microsoft Research Asia(MSRA), Beijing, China. He has been an Editor or Guest Editor for 16 journalsand is involved in the organization of about 40 conferences. His current researchinterests include wireless mobile networking supporting multimedia, videomulticast, and all optical networks using WDM, in which he has publishedover 150 technical papers in referred journals and conference proceedings.

Dr. Li was the Co-TPC Chair for IEEE INFOCOM’04 and is a Member ofthe Association for Computing Machinery (ACM).

Lemin Li graduated from Jiaotong University,Shanghai, China, in 1952, majoring in electricalengineering.

From 1952 to 1956, he was with the Departmentof Electrical Communications, Jiaotong University,Shanghai, P. R. China. Since 1956, he has been withChengdu Institute of Radio Engineering (currentlythe University of Electronic Science and Technologyof China), Chengdu, P. R. China. From August1980 to August 1982, he was a Visiting Scholarwith the Department of Electrical Engineering and

Computer Science, University of California, San Diego, where he did researchon digital and spread-spectrum communications. He currently is a Professor ofCommunication and Information Engineering. His research work is in the areaof communication networks, including broad-band and wireless networks.

Mr. Li is a Member of the Chinese Academy of Engineering.