Location-Aware IEEE 802.11for Spatial Reuse Enhancement

Tamer Nadeem, Member, IEEE, and Lusheng Ji, Senior Member, IEEE

Abstract—In this paper, we propose an enhancement to the IEEE 802.11 Distributed Coordination Function (DCF). The enhancement

improves the level of channel spatial reuse; thus, it improves overall network data throughput in dense deployments. Our modification,

named the Location-Enhanced DCF (LED), incorporates location information in DCF frame exchange sequences so that stations

sharing the communication channel are able to make better interference predictions and blocking assessments. Hence, more

concurrent transmissions can be conducted in densely deployed Wireless LANs. The potential performance enhancement of LED is

studied both analytically and via ns-2 simulations. The results show that the LED method achieves significant throughput

improvements over the original DCF.

Index Terms—Wireless communication, network protocol, IEEE 802.11, MAC, DCF, carrier sense, capture effect, spatial reuse.

Ç

1 INTRODUCTION

DURING the past several years, the growth in popularityof the Wireless Local Area Network (WLAN) has been

phenomenal. Because the number of available WLANchannels is limited, as more and more WLAN AccessPoints (APs) are put into service, overlapped AP deploy-ments in both space and frequency have become morecommon. These APs, and their corresponding clients, mustshare the use of the communication channel. Efficientchannel sharing is critical to the success of other emergingapplications of WLAN technology, such as Wireless MeshNetworks and Wireless Ad Hoc Networks. The mechanismof WLAN channel sharing is specified by the IEEE 802.11 [2]standard or, more particularly, its Distributed CoordinationFunction (DCF).

However, the IEEE 802.11 DCF is not very efficient inshared channel use due to its overcautious approachtoward assessing the possibility of interference. In parti-cular, a station simply blocks its own transmission when itsenses the medium busy or it has received a channelreservation frame sent by any other station. In many cases,it has been observed that this channel-assessing station’sown transmission would not actually disturb the ongoingtransmission because such a transmission would notintroduce enough interference energy to the receiver ofthe ongoing transmission to actually corrupt its reception.

Finer channel assessment schemes which do consider theabove possibility are difficult to implement with informa-tion provided by the current IEEE 802.11 communicationprotocol. If more parameters regarding an ongoing trans-mission, such as the locations of the transmitter and receiver

and transmission power level, can be provided to surround-ing stations, it is then possible for the surrounding stationsto make better estimations on whether their own transmis-sions may indeed corrupt the reception of the ongoingtransmission. Hence, more concurrent transmissions can bescheduled and the communication channel can be sharedmore efficiently.

In this paper, we propose a novel contention-baseddistributed MAC scheme which achieves exactly the above.The use of location information gives the scheme the nameLocation Enhanced DCF or LED.

2 BACKGROUNDS AND RELATED WORKS

2.1 IEEE 802.11 DCF Mode

Historically, the design of the IEEE 802.11 DCF has beeninfluenced by several other protocols, among whichMACAW [6] is the most significant. MACAW, an extensionof its predecessor Multiple Access Collision Avoidance(MACA) [15], uses the Request-To-Send (RTS) and Clear-To-Send (CTS) message handshake prior to data frametransmission. Later on, the Floor Acquisition MultipleAccess (FAMA) is subsequently proposed in [12], whichemploys both local carrier sensing and RTS-CTS exchangefor data transmission.

The DCF is a Carrier Sense Multiple Access withCollision Avoidance (CSMA/CA) mechanism. Prior toany transmitting, if the carrier is sensed to be busy, thestation needs to wait for a random back-off interval. Forunicast data transmissions, the DCF includes a stop-and-goARQ process for improving data delivery reliability. That is,an ACK frame must be received after any unicast datatransmission. Otherwise, a retransmission of the data frameneeds to be scheduled. The DCF may also optionally start aRTS-CTS channel reservation process prior to a unicast datatransmission to reduce collision.

Because, in DCF, each frame delivery sequence consistsof multiple transmissions of both directions, either DATA-ACK or RTS-CTS-DATA-ACK, to prevent the sequence

IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 6, NO. 10, OCTOBER 2007 1171

. T. Nadeem is with Siemens Corporate Research, 755 College Road East,Princeton, NJ 08640. E-mail: tamer.nadeem@siemens.com.

. L. Ji is with AT&T Labs, Research, 180 Park Ave., Florham Park, NJ07932. E-mail: lji@research.att.com.

Manuscript received 2 Sept. 2005; revised 15 Apr. 2006; accepted 17 Dec.2006; published online 7 Feb. 2007.For information on obtaining reprints of this article, please send e-mail to:tmc@computer.org, and reference IEEECS Log Number TMC-0258-0905.Digital Object Identifier no. 10.1109/TMC.2007.1029.

1536-1233/07/$25.00 � 2007 IEEE Published by the IEEE CS, CASS, ComSoc, IES, & SPS

being interrupted by other stations transmissions, the DCFalso has a “soft” channel protection mechanism known asvirtual carrier sensing.

At each station, a timer called the Network AllocationVector (NAV) is maintained. This timer tracks the remain-ing time of any ongoing data delivery sequence. The NAVis set according to the “duration” field of received RTS,CTS, DATA, or ACK frames. The “duration” field containsthe frame sender’s estimation of the termination time of thedelivery sequence. Fig. 1 illustrates how stations set theirNAVs during a full RTS-CTS-DATA-ACK handshake.

Checking its NAV before a station attempting to transmitis also known as “virtual carrier sensing.” If the NAV is notzero, the station needs to block its own transmissions toyield to the ongoing delivery sequence.

2.2 Capture Effect

When a frequency modulation scheme, such as the 802.11’sDirect Sequence Spread Spectrum (DSSS), is used inwireless communication, an effect known as the “captureeffect” [4], [21], [22], [18], [13] may occur. Basically, if at anytime instance two transmissions arrive at the same receiver,the stronger signal captures the receiver modem and theweaker signal is rejected as noise.

From the results of different studies (e.g., [13], [9], [19],[25], [16]) that characterize the capture effect, we adopt asimple yet widely accepted model to describe the captureeffect. In this model, the capture effect occurs when

Pr > �Xn

i¼1;i 6¼rPi: ð1Þ

In this formula, it is assumed that there are n signals beingreceived by the receiver and their power levels are Pi. Therth signal is the one of interest and its power level is Pr. Theratio � is called the capture ratio.

We are interested in the capture effect because webelieve that it can help improve the channel sharingefficiency. As long as a receiver is still able to capture theintended transmission, new transmissions should be al-lowed to occur.

2.3 Related Works

Our observation that DCF is so overly pessimistic intransmission blocking decisions that it actually hurtschannel efficiency concurs with the views of other

researchers, who have also proposed modifications toDCF for the purpose of increasing the number ofconcurrent transmissions in the network. Our own mod-ification to the IEEE 802.11 DCF protocol is partly inspiredand motivated by these related works.

Ye et al. [27] suggest that, by changing the timing of thesteps within the RTS-CTS-DATA-ACK frame sequence andsynchronizing the states among one hop neighbors, if thereceivers of two frames transmitted by two neighbors arefar enough apart, these two transmissions can be scheduledconcurrently. Acharya et al. [3] observe that, in an “over-active RTS-CTS” situation, in which the RTS-CTS exchangeaffects more surrounding stations than needed, hearing RTSor CTS but not both does not justify assessing the channel asbusy. Thus, a bystander to a pair of data transmitters andreceivers should only block its own transmission if itreceives both RTS and CTS.

The Interference-Aware (IA) method proposed byCesana et al. [8] and Maniezzo et al. [11] shares the samephilosophy as our proposal in the way that stations reportthe channel condition by piggybacking channel conditioninformation in the frame exchange sequence. In IA, thereceiver of an RTS frame embeds the Signal to InterferenceRatio (SIR) observed while receiving the RTS in its returningCTS frame. This way, other stations, also taking intoaccount the SIR they observed while receiving the sameCTS frame, are able to calculate if their own transmissionsmay cause enough interference to the RTS receiver inquestion. This mechanism works only with the RTS-CTSscheme and it requires stations to listen to both RTS andCTS frames.

We have noticed some rather common problems amongthese approaches. The first is that these proposals rely onthe RTS-CTS handshake. In reality, the RTS-CTS hand-shake is turned off in most deployments, which makesthese proposals inapplicable in such environments. Thenext issue is that these proposals do not take theaforementioned “capture frame” versus “capture signal”problem into consideration. As a result, many concurrenttransmissions will not be received by their intendedreceivers, not because the signals are not strong enough,but because the received bits are cast into the wrong frameand become incomprehensible.

More recent works [28], [26], [24] propose improvingWLAN channel spatial reuse via a more passive approachby tuning receivers’ physical carrier sense sensitivitysettings. Given a minimum required signal-to-noise ratioof a regular topology, Zhu et al. [28] are able to adjust thecarrier sense energy threshold to an optimal value thatmaximizes spatial reuse. Yang and Vaidya [26] extend thiswork by exploring the interactions between MAC and PHYlayers and studying the impact of MAC overhead on thechoice of optimal carrier sense range as well as on theaggregate throughput. Vasan et al. [24] introduce theECHOS architecture, which exploits the spatial heterogene-ity of users and data flows in WLAN hotspot environmentsin order to improve network capacity. They have derived analgorithm for AP to set appropriate carrier sense thresholdsfor itself as well as its clients so that more cochannel flowscan coexist without interfering with each other. A consider-able overhead is needed to apply such a mechanism in adistributed manner for ad hoc networks. In addition to the

1172 IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 6, NO. 10, OCTOBER 2007

Fig. 1. IEEE 802.11 DCF mechanism where EIFS is the Extended Inter

Frame-Space period for packets received incorrectly, R is the transmis-

sion range, and C is the carrier sense range.

complication of this mechanism, none of these worksconsidered the effect of capture phenomena.

3 DCF TRANSMISSION BLOCKING ASSESSMENT

ANALYSIS

Before going into the details of LED, in this section, weprovide an explanation for why the DCF carrier-sense-based blocking assessment algorithm is overly conservative.Then, we analyze the probability that a node can transmitwith the presence of a nearby transmission withoutcorrupting this transmission if frame capture is supportedby all the receivers. This probability quantifies how muchpotential throughput gain there is to improve over DCF.

3.1 Radio Propagation Model

Because it is the received signal power that determineswhich signal and frame captures the receiver, it is necessaryfor us to start our analysis with the radio propagationmodel. The radio propagation model describes howreceived power changes as the distance between thereceiver and transmitter changes.

Our approach to radio propagation separates thepropagation model into two components. The majorcomponent focuses on the relationship between transmit-ter-to-receiver distance and the long-term average receivedpower. The minor component focuses on modeling therandom variations caused by various types of short-termfading. The combined received power in decibels can thusbe described as

Pr;dB ¼ Pavg;dB þ xdB: ð2Þ

Pr;dB is often called the local mean power.For the long-term average received power, we adopt a

widely accepted omni-directional path loss model. In thismodel, the received signal power, Pavg, is calculated asfollows:

Pavg ¼k�2

ð4�Þ21

Dple; ð3Þ

where k is defined as k ¼ PtGtGr

L .In the above equation, Pt is the transmission power, L is

the system loss factor not related to propagation ðL � 1Þ,and Gt and Gr are the transmitter and receiver antennagains, respectively. Since these parameters do not changegiven a pair of transmitters and receivers, they are togethersummarized as k, which is a common constant factor thatappears in both submodels. Among the rest of the para-meters, D is the distance between the transmitter and thereceiver and � is the wavelength. The ple is the path lossexponent, which is usually between 2 and 4. In our analysis,we use ple ¼ 2 if we are without additional specification.In this case, this model is often called the “free-spacepropagation model.”

We model the random variation around the long-termaverage received power, the minor component, as a randomvariable xdB with normal distribution centered at the Pavg;dB.The probability density function of xdB is, hence,

fðxdBÞ ¼e�

x2dB

2�2ffiffiffiffiffiffiffiffiffiffi2��2p ; ð4Þ

where � is the standard deviation of the distribution. Intypical wireless systems, � is usually between 6 dB and10 dB. In our experiments, we assume that � is 6 dB.

It is worth noting that both components may be modeleddifferently in different environments. Although, in thispaper, we only deal with this particular pair of models, thisapproach can be easily extended to other models as well.

Combining the above radio propagation model and thecapture model, it is easy to illustrate why the 802.11 DCF’scarrier sense blocking assessment approach is overlypessimistic. Here, we only consider the major componentof the propagation model.

In the example shown in Fig. 2, node 1 is transmitting tonode 2. The three subfigures illustrate three scenarios ofdifferent transmitter-to-receiver distances. Under DCF, allnodes within radius C of node 1 will sense the carrier asbusy and block their own transmissions, if any. C is calledthe carrier sense range and the area within radius C ofnode 1 is called the carrier sense zone. Here, we do notworry about the carrier sense zone around node 2 as muchbecause the reverse direction transmissions are usuallyvery short compared to the actual data transmissions fromnode 1.

Assuming r is the distance between nodes 1 and 2, from(3) and (1), it is easy to derive that node 2 captures thetransmitted signal from node 1 correctly as long as there isno other node transmitting within a certain range of node 2.This “quiet range” is called the interference range I.

I ¼ rffiffiffiffi�plep

; ð5Þ

where ple is the path loss exponent.Similarly, capturing must work for the reverse direction

for node 1 to receive node 2’s frames, i.e., ACK. The regionthat is within radius I of either node 1 or 2 is the actualinterference zone within which any transmission mayactually interfere with the data delivery sequence betweennode 1 and 2.

We denote the carrier sense zone as area AC and theinterference zone as area AI . Under DCF, nodes inside ofðAC �AIÞ, which is the area that is inside of the carriersense zone but outside of the interference zone, areunnecessarily blocked from transmitting. This area isshown as the shaded areas in Fig. 2.

With a little geometry, it is easy to calculate thepercentage of the shaded area ðAC �AIÞ over the carriersense zone AC . As an example, Fig. 3 plots such apercentage versus the distance between node 1 andnode 2, given that the transmission range (R) is 250 m,for C ¼ 550 m and � ¼ 5 with a path loss exponent of 2.

NADEEM AND JI: LOCATION-AWARE IEEE 802.11 FOR SPATIAL REUSE ENHANCEMENT 1173

Fig. 2. Carrier sense zone and interference zone.

3.2 Probability of Noninterfering Transmission

We now study in what probability a transmission thatoccurs despite the presence of another ongoing transmis-sion will actually not interfere with the ongoing transmis-sion. This probability is the motivation behind the design ofLED because it indicates the potential space of improve-ment from the DCF.

We assume that stations are uniformly distributed overan area with a density of �. Each station has a transmissionrange R and a carrier sense range C. For the ease of analysis,we assume that all stations have the same traffic model andall data packets are of the same length. Each packet requirestransmission time � and is randomly destined to one of thesender’s 1-hop neighbors. One data packet is generated at arandomly selected time within every time interval T , whereT > � . We also assume that all transmitters use the sametransmission power and transmitter and receiver antennagains are the same. In this section, we deal with the majorpropagation component only, as described in (2). The effectof the minor component will be studied in the next section.

We are concerned about the scenarios where a station vmay cause interference to another station r which isreceiving a data frame delivery from station s as shown inFig. 4. Station v may transmit only if its transmission doesnot affect the reception of the DATA frame at r and theACK frame at s. Using the previously described propaga-tion model and capture model, to allow stations s and r tocorrectly capture each other’s frames in the presence of anytransmission from station v, the following should hold:

ðv:s >ffiffiffiffi�p

s:rÞ AND ðv:r >ffiffiffiffi�p

s:rÞ; ð6Þ

where s:r is the distance between station s and station rand � is the capture ratio.

Fig. 4 illustrates the situations for both r capturing s’s

transmissions (DATA) and for s capturing r’s transmis-

sions (ACK) in the presence of v’s transmission. For r to

capture s’s transmissions, given that m is the distance

between s and r, the distance between v and r must be

greater thanffiffiffiffi�p

m. For s to capture r’s transmissions, given

that x is the distance between v and s, r must be within a

circle of radius minðR; xffiffiffi�p Þ. Considering both conditions, r

must be located within the shaded area AðxÞ in the figure.

Hence, the probability that v’s transmission does not corrupt

the communication between s and r is

P ðBjxÞ ¼ AðxÞ�R2

; ð7Þ

where the area AðxÞ is calculated as follows:

AðxÞ ¼Z minðR; xffiffi

�p Þ

0

2 �� arccosx� x2�m2þð

ffiffiffi�p

mÞ22x

m

! !m dm:

ð8Þ

Since we only worry about potential interferers withinthe carrier sensing range, by unconditioning x, we obtain

P ðBÞ ¼Z C

0

AðxÞ�R2

2x

C2dx: ð9Þ

Based on the traffic model, the probability that none ofthe stations within the carrier sensing range of a station willtransmit is obtained by

P1 ¼ 1� �

T

h i��C2

ð10Þ

and the probability that v’s transmission will not interferewith other transmissions (if any) in the carrier sense range is

P2 ¼ 1� �

Tþ �

TP ðBÞ

h i��C2

: ð11Þ

Therefore, the probability Pb that v can transmit with thepresence of a nearby transmission without corrupting thistransmission is given by

Pb ¼ P2 � P1: ð12Þ

In the analysis above, the derivation of Pb is ratherconservative. The first reason is that we have been usingpath loss exponent 2 in the process. A higher path lossexponent means that the received power is even lower atthe same distance. This translates to less interference and,consequently, increased Pb. The second reason is that, forsimplicity, we assume that all stations in the vicinity of vhave the freedom of transmission. We do not take intoaccount that some of these stations may block because thereare other ongoing transmissions not from v in theirvicinities. Accounting for these blocked stations would alsoincrease Pb.

The above analytical results can be verified bysimulation, by generating random network topologies

1174 IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 6, NO. 10, OCTOBER 2007

Fig. 3. Unnecessary blocking based on carrier sensing.

Fig. 4. Capture analysis where x0 ¼ xffiffiffi�p and m0 ¼ ffiffiffiffi

�p

m.

and traffic patterns and studying the interference situation

in each case.For constructing each random network, we place the v

station at the center of an area of 1,000 � 1,000. Transmitter

stations are distributed uniformly in this area. Each

transmitter is paired with a corresponding receiver whose

location is randomly picked within a circular area which is

centered at the transmitter and with radius R. Then, each

transmitter starts transmitting, following the traffic model

described before: all packets require transmission time �

and they are generated randomly at a constant rate: one

packet every time interval T , where T > � . When v has a

frame to send, we study whether it will be blocked under

the current IEEE 802.11 DCF mechanism and, when

blocked, we will verify whether v’s transmission will

indeed harm other communications. The number of situa-

tions where unnecessary blocking is suggested by DCF is

then divided over the total number of simulated situations

to derive the probability of unnecessary blocking, which is

then compared to the analytical result provided above.Fig. 5 plots both the analytical and simulated values of

P1, P2, and Pb for R ¼ 250, C ¼ 550, � ¼ 5, �=T ¼ 0:01, and

different numbers of stations (thus varying the station

density �). As we can see, the simulation results closely

match the analytical results.We have also made simulation runs without using the

same assumptions as the above analysis. Fig. 6 plots the

simulation of Pb with more realistic assumptions. Here,

stations in the vicinity of v may actually be blocked by other

ongoing transmissions, not from v, in their vicinities. This

figure contains plots of Pb under different packet loads.The bottom curve in the figure is not a result of this set of

simulations but directly copied from Fig. 5. It shows that, in

real networks, the probability that v can transmit with the

presence of a nearby transmission without corrupting this

transmission is likely even higher than our conservative

analytical model shows.

3.3 The Effect of Random Variation in PropagationLoss

So far, we have been primarily using the major component

of the radio propagation model described in (3) in our

analysis and ignoring the minor component. We now

account for the minor random component by studying

how much of an error margin the minor component adds to

the analysis.Previously in our analysis, we have only considered far

field average power Pavg. Now, we apply local mean power

Pr instead in the capture condition. Cases that are able to be

captured using Pavg but not able to be captured using Pr are

erroneous. Recalling the previously described capture

condition, the error probability of the previous analysis is

defined as a conditional probability

P ððPr1 � Pr2Þ < 10 log�jðPavg1 � Pavg2Þ > 10 log�Þ; ð13Þ

where all power terms are in decibels.Since Pr is the combination of major component Pavg and

minor component x, we have

Pr1 � Pr2 ¼ ðPavg1 � Pavg2Þ þ ðx1 � x2Þ ð14Þ

¼ 10 logd2

d1

� �2

þy; ð15Þ

where y ¼ x1 � x2. Again, all power terms are in decibels.When both x1 and x2 follow normal distribution and are

independent of each other, the probability density function

for y is also a normal distribution,

fðyÞ ¼ e� y2

2�2yffiffiffiffiffiffiffiffiffiffi

2��2y

q ; ð16Þ

where �2y ¼ 2�2.

Given any two distances d1 and d2, if

Pavg1 � Pavg2 ¼ 10 logd2

d1

� �2

> 10 log�; ð17Þ

d2

d1>

ffiffiffiffi�p

; ð18Þ

the error probability is the probability of the following

event:

Pr1 � Pr2 < 10log�; ð19Þ

10 logd2

d1

� �2

þy < 10 log�; ð20Þ

y < 10 log �d2

1

d22

� �: ð21Þ

NADEEM AND JI: LOCATION-AWARE IEEE 802.11 FOR SPATIAL REUSE ENHANCEMENT 1175

Fig. 5. Analytical versus simulated results for P1, P2, and Pb. Fig. 6. Pb with different load values t0 ¼ �=T .

Because y follows normal distribution, for any given d1

and d2, the above probability can be calculated. For

example, if d1 ¼ 25 m, d2 ¼ 100 m, and � ¼ 5, the above

probability is equal to 0.07.To show how much the random variation in received

power may affect Pb, we again construct simulations just

like those described in the previous section. The only

difference is that, now, when determining if the capture

condition can be satisfied, Pr (2) is used instead of Pavg (3).Fig. 7 shows, for two different traffic load levels t0 ¼ 0:01

and t0 ¼ 0:02, how Pb differs when a different received

power model is used. As expected, because of the error in

received power estimation caused by shadowing, Pb is

reduced.

4 LOCATION-ENHANCED DCF

In this section, we describe our Location-Enhanced DCF

(LED) for the IEEE 802.11 by first giving an overview of the

LED mechanism. Then, we provide the design of the

needed physical layer. Finally, we present the proposed

modifications to IEEE 802.11 MAC with the details of the

LED mechanism. Before we introduce our approach of

using location information and capture effect to improve

channel efficiency, several terms which will be used during

the description need to be clarified to avoid confusion.In our description, we use the term “delivery” for the

whole handshake procedure for delivering a unicast data

frame. Depending on frame size and network configura-

tion, a “delivery” may involve a full RTS-CTS-DATA-

ACK 4-way frame exchange sequence or just a DATA-

ACK 2-way exchange. A “source” is the station having

data to send during a delivery. The “destination” of a

delivery is the station to whom the source wishes to send

data. While “source” and the “destination” regard data

frame and, hence, the whole delivery, the terms “sender,”

“transmitter,” and “receiver” correspond to any indivi-

dual frame, RTS, CTS, DATA, or ACK. So, for instance, a

sender of a CTS or ACK frame is actually the destination

of the delivery. In addition to the above, “connection” is

used to refer to both the source and destination stations

collectively.

4.1 Protocol Overview

Our approach is simple: to include more information abouteach transmission in the transmission itself so that any otherstations overhearing the transmission are able to betterassess whether their own transmissions may harm thisongoing delivery. Among various relevant parameters, thelocations of the transmitters and receivers are the mostimportant. We assume that each station is capable ofacquiring its own location, e.g., by configuration, GPS, orother RF-based localization methods [5], [17]. A station hasaccess to other communication parameters regarding itsown transmitter/receiver as they are typically configurationparameters.

When the above parameters are included in eachtransmission, an overhearing station of a data deliverycan compute the received energy level of the framesbelonging to the same data delivery at their receivers,using a propagation model suited for the surroundingenvironment. Then, if the capture ratio of the receiver is alsoknown, knowing its own location, antenna gain, andtransmission power, this station can make a predictionabout whether its own transmission may affect this ongoingdata delivery. If the result is negative, this station shouldnot block its own transmissions, if any, despite the presenceof the ongoing data delivery. When LED’s predictions arecorrect, the “unblocked” transmission will not affect thecorrect receptions of others at their corresponding receiversif these receivers are capable of frame capture.

Because LED permits additional concurrent transmis-sions, applying LED in wireless mesh networks or denselydeployed infrastructure WLANs has the benefit of improv-ing overall channel throughput.

The use of a propagation model to predict interferencemay introduce certain limitations on how LED can beapplied in real world applications. For instance, as Lu andRutledge [20] point out, path-loss in indoor environmentstends to be very dependent on building structure andconstruction. However, the protocol operations of LED arenot affected by the choice of underlying propagation model.Thus, an LED-based system design may wish to build in theflexibility of plugging in different propagation modelsunder different operation environments. Additional mea-surement-based control mechanisms may also be includedin such a system in an open-loop fashion so that theprediction model can be better “tuned” for non-distance-induced fading conditions.

LED uses a simple estimation model. In particular, eachchannel assessing station only considers the effects from itsown potential transmission. It may occur that severalstations simultaneously yet independently predict that theirown transmissions will not cause collision to the ongoingdelivery. But, the aggregated energy from all these sidetransmissions may actually change the result of the captureeffect and interfere with the ongoing delivery. We postponefurther studies for this issue to future works.

4.2 Physical Layer Design

To take advantage of capture effect, the LED requires aspecial kind of receiver design. The reason lies in thedifference between capturing a signal versus capturing aframe.

1176 IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 6, NO. 10, OCTOBER 2007

Fig. 7. Pb with shadowing versus without shadowing.

The capture effect only produces the momentary resultwhen multiple signals are competing for a receiver.However, a receiver being able to capture a strong signaldoes not necessarily mean it can successfully capture thestrong frame. This also depends on several other factors,such as the arrival moment of the beginning of the strongframe, the current receiving state of the receiver, thecapability of the receiver to realize that it is seeing thebeginning of a new (strong) frame, and the capability for thereceiver to jump to the appropriate receiving state forbeginning to process the new frame. For instance, if thereceiver is in the middle of receiving the service dataportion of a frame and a stronger frame arrives, the receiveris not able to realize that it has just been captured by a newframe and reset its receiving state machine accordingly.Therefore, received bits, which belong to the new frame, areinterpreted as bits of the current (weak) frame whichtypically result in check sum failure and frame rejection.Receivers that support the capture of frames require specialdesign consideration.

Fortunately, receiver designs which do support thecapture of a new frame after the receiver has already begunto receive another frame do exist. One example of such areceiver physical layer (PHY) design is Lucent’s PHYdesign with “Message-In-A-Message” (MIM) support [7].In this design, the newly arrived frame is referred to as the“(new) message in the (current) message.”

A MIM receiver is very similar to normal IEEE 802.11PHY designs except that it continues to monitor thereceived signal strength after the PHY transits from receivertraining state to data reception state. If the received signalstrength increases significantly during the reception of aframe, as shown in Fig. 8, the receiver considers that it mayhave detected the beginning of a MIM frame and, hence,switches to a special MIM state to handle the new frame.

While under the MIM state, the receiver tries to detect acarrier for a new frame. If the carrier signal is detected, thereceiver begins to decode the initial portion of the newframe and retrains to synchronize with the new transmis-sion. If no carrier, preamble, or frame delimiter is detected,which indicates that the energy increase is likely caused bynoise, the PHY will remain in this MIM state until either acarrier is detected or the scheduled reception terminationtime for the first frame is reached.

With a MIM-capable design, a receiver is able to correctlydetect and capture a strong frame regardless of the currentstate of the receiver, unlike regular IEEE 802.11 PHYdesigns, where the strong frame can only be correctlycaptured while the PHY is under certain (i.e., receivertraining) states during its reception of a weak frame.

4.3 MAC Layer Design

Our enhanced design for a DCF MAC stands atop a MIM-capable PHY. Fig. 9 illustrates the layered structure of therelevant entities. The IEEE 802.11 Physical MediumDependent (PMD) layer performs wireless medium trans-mission and receiving services. The Physical Layer Con-vergence Protocol (PLCP) layer adapts the raw services ofPMD to PHY-MAC data and control interface. The newLED is a part of the MAC layer function. Fig. 10 shows theframe format to support the enhanced functionalities ofthe new MAC.

We propose inserting a block of information called ENH(“Enhanced”) to provide the additional information neededfor LED. There are two places that the ENH block can beinserted: between the current PLCP header and the PLCPService Data Unit (PSDU) or right after the 802.11 MACheader. Either has its pros and cons.

For the end of PLCP header solution, the ENH block isreceived very early. Also, all stations within the service setcan understand the ENH block since the PLCP header istransmitted at a base rate. However, putting the ENH blockinto the PLCP header will result in incompatibility withnon-LED stations. The immediately-after-MAC-header so-lution has better compatibility because non-LED stationscan still receive the PLCP and MAC headers correctly.However, it takes longer for an LED station to make LEDdecisions. Also, not all stations in the BSS may be able todecode the ENH block since that part of the frame (MACdata) is not transmitted at the base rate. In addition, theENH should carry its own data integrity verification sincethe MAC data checksum is calculated at the end of thewhole MAC frame. In this paper, we have the ENH as partof the PLCP header mainly for performance reasons. Tocope with legacy nodes, the reserved bits in the Subtypefield of the Frame Control field of the frame header could beused as an indication of the LED-compatible nodes/frames.An LED-compatible node could disable the LED mechan-ism once it detects a legacy node in its neighborhood toavoid medium access unfairness. Details of compatibilityissues are out of the scope of this paper.

The ENH block is further divided into six fields. TheLOCT field contains the location of the frame transmitter,the PWRT field describes the transmission power of thetransmitter, and the GAINT field specifies the transmission

NADEEM AND JI: LOCATION-AWARE IEEE 802.11 FOR SPATIAL REUSE ENHANCEMENT 1177

Fig. 8. Message-in-a-message.

Fig. 9. PHY-MAC layer structure.

Fig. 10. Frame structure.

antenna gain. The LOCR, PWRR, and GAINR fields containthe same pieces of information for the receiver.

If RTS-CTS exchange is needed for a data delivery, asource starts its unicast data delivery by sending out an RTSframe to reserve the channel. In the ENH block of thisframe, the source fills the LOCT, PWRT, and GAINT fieldswith its own parameters and the LOCR, PWRR, and GAINRwith the destination’s parameters, if known. Any unknownparameters are set to NULL. Upon receiving the RTS, thedestination of the data delivery copies the LOCT, PWRT,and GAINT fields into the corresponding fields of its CTSframe. It also fills or updates the LOCR, PWRR, and GAINRfields of the CTS frame with its own parameters. Insubsequent DATA and ACK frames, full descriptions ofboth the source and the destination are included. In case ofthe frame size being less than the RTS-CTS threshold and noRTS-CTS handshake being conducted, the DATA frame willhave its fields set in the same fashion as the RTS frame andthe ACK frame is filled in the same way as the CTS frame.

A parameter cache may be maintained by stations tostore the location, power, and antenna information ofalready known stations. This way, when sending data to astation in cache, the cached parameters may be used in thecorresponding fields of the ENH block instead of NULLvalues. Cache entries are updated if newer information isreceived from their corresponding stations. Cache entriesare removed after the expiration time.

In the standard IEEE 802.11, the PHY (PLCP inparticular) will normally signal three events to the MAClayer during frame reception: carrier busy, begin receivingPSDU, and end receiving PSDU. It does not deliver any databits to the MAC layer until the PSDU reception has begun.Then, the receiver will proceed until the end of the frame(unless interrupted by carrier loss in the middle of thereception). Received bits are passed to the MAC layer asthey are decoded and assembled into the MAC frame. Atthe end of the PSDU is a forward error detection CRC blockcalled Frame Check Sequence (FCS). If the MAC framepasses the CRC check, it is accepted and passed up forfurther 802.11 MAC processing. If the CRC fails, the frameis dropped.

In addition to the above interactions, LED defines twonew mechanisms for the PLCP layer to interact with LED.They are illustrated by Fig. 11. The first is an indicator

called PHY_NEWPLCP. This indicator is turned on by thePLCP layer after it finishes receiving the Start FrameDelimiter (SFD) field of a frame’s Preamble section. Themeaning of this indicator is that the PHY is affirmative thatit has begun receiving a new frame, and the next thing itexpects is the PLCP header of the frame. Upon receivingthis indicator, LED needs to block transmission so thePLCP header can be received without interruption. ThePHY_NEWPLCP indicator will be turned off by the PLCPlayer after it finishes receiving the CRC field of the PLCPheader. The second mechanism is for the PLCP layer topass up the PLCP header contents to LED as soon as thePLCP is verified to be correct by CRC checking. Afterreceiving the PLCP header from the PLCP layer, LED willmake a decision as to whether the physical layer shouldblock its own transmission.

During the blocking decision making process, a non-receiver station (denoted as station i) of the frame calculateswhether its own transmissions will cause enough inter-ference to interrupt the data delivery to which the justreceived frame belongs. The station needs to calculate thepower level of its own transmission at both the source,denoted as Ps

i , and the destination, denoted as Pdi , of the

ongoing data delivery using an appropriate propagationmodel (i.e., (3)). The station also needs to calculate thereceived power level of the destination station’s transmis-sion at the source, denoted as Ps

d , and that of the sourcetransmission measured at the destination, Pd

s . If ðPsd > �Ps

i Þand ðPd

s > �Pdi Þ, the station should not block its own

transmissions. Otherwise, it should block its transmissions.In the case that the communication parameters of either thesource or the destination are unknown, the assessing stationassumes the worst and blocks its own transmission.

If the station decides to block its own transmission due toworries that the transmission may affect the correctreception of some frames of the ongoing data delivery, itremains in the receiving state and continues the receivingprocedure as specified by the standard. It disables anytransmission request from an upper layer and sets its NAVvalue according to the Duration field of the frame, which isset to the time required for the full data delivery frameexchange sequence to finish. One thing to note is that, onthe intended receiver of the frame, the blocking estimationwill implicitly always produce positive result.

On the other hand, if the station decides not to block,the receiving may still continue but upper layer transmis-sion requests are not disabled. No NAV is set in this caseeither. If there is indeed any outgoing frame ready, themodem can accept the request by switching to thetransmission state and starting the transmission. A PHYreset signal is needed in this case to force the PHY to leavethe receiving state and enable the PHY_TXSTART signalwhen the MAC has a frame to send.

If LED decides not to block, the handling of the physicalcarrier sensing mechanism, i.e., the Clear Channel Assess-ment (CCA) indicator produced by the physical layer,requires careful consideration. CCA is set to busy whenthere is a carrier being detected. Since the frame is still beingtransmitted in the air, the CCA will remain busy. It needs tobe temporarily ignored. The overriding of CCA in the LEDlayer is accomplished by proposing a new vector, called the

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Fig. 11. PHY-MAC interactions.

CCA-Suppression Vector (CSV), which is a suppressiontimer. CSV is set to the end of reception of the currentframe, calculated based on the length field contained in thereceived PLCP header of the frame.

During the reception of a frame, if a new stronger framearrives and captures the receiver, the PHY will again passup the PLCP header to LED upon successfully verifying theCRC. The LED will estimate interference again using thenew PLCP header. If LED decides to block transmission forthis new data delivery, NAV is set to the end of this newdelivery if it is later than the current NAV expiration time.Start-to-transmit requests are disabled as well. If LEDdecides not to block for this new delivery, the NAV value isnot changed but the CSV expiration time remains or is set tothe end of the new frame, whichever is later.

At the source or the destination station of the ongoingdelivery, according to the IEEE 802.11 standard, the NAV isnot set for the duration of the delivery. In LED, thisspecification is still followed. However, in LED, the sourceand the destination stations of a data delivery do need to settheir CSVs to the estimated end of the delivery. The reasonis as follows: LED permits concurrent transmissions byother stations as long as they do not produce enoughinterference to disturb the ongoing delivery. If any otherstation indeed decides to transmit, the energy of thetransmission may cause the source and the destination ofthe ongoing data delivery to sense that CCA is busy and,thus, abort the data delivery frame sequence. Hence, theCCA should be suppressed on the source and destinationstations till the end of the data delivery.

In total, an LED station has four indicators related to thetransmission blocking estimation. The CCA is the physicalcarrier indicator. It is “TRUE” when the PHY layer detectsthe carrier (or energy exceeding threshold, or both,depending on equipment vendor implementation). TheNAV indicator is the virtual carrier indicator. It is “TRUE”when there is a channel reservation which needs to behonored. That is, if this station transmits, then thetransmission will interfere with the ongoing delivery. ThePHY_NEWPLCP indicator is on while a PLCP header isbeing received. Finally, the CSV indicator tells the station ifit should ignore the physical layer CCA. It is “TRUE” whenthe suppression timer is running. More precisely, a stationonly blocks its own transmission if the following is true:

PHY NEWPLCP k ððCCA && CSV Þ k NAV Þ: ð22Þ

Another issue occurs if a channel-assessing station onlydetects the carrier but cannot decode the frame. In this case,a station is not able to estimate whether its transmission willaffect this ongoing transmission. Either an aggressiveapproach or a conservative approach can be taken. In theaggressive approach, this station will not block its owntransmission in the event of “detecting a carrier but notbeing able to decode the frame,” while, in the conservativeapproach, this station will block its own transmission.

Another note is that the LED mechanism requires thatnodes to block during the RTS-CTS periods of otherconnections will also benefit network throughput whenunder RTS-CTS mode. The reason is the following: Duringa RTS-CTS-DATA-ACK handshake after the CTS, the

destination is expecting a DATA frame from its correspond-ing source. If a different frame is transmitted by a nearbynode during this gap, this connection may be forced torestart the RTS-CTS handshake again regardless of whetherthe destination can still capture the DATA frame from itssource. Such a situation is rare under DCF, but it would befairly common for LED. Thus, to avoid the connections torestart RTS-CTS, nodes should be blocked during theperiod from the beginning of RTS to the EBH header ofthe DATA frame.

5 PERFORMANCE EVALUATION

In this section, we present extensive simulation-basedstudies on the performance of the LED. The performancecomparisons are done using the ns-2 simulator [1]. The LEDprotocol is implemented as a modification of the IEEE802.11 MAC.

We have also made several modifications to the im-plementation of ns-2. The radio propagation model of ns-2 isa simple 2-slope path loss model, which is only suitable forlong-term average behavior. We enhanced this propagationmodel by including the shadowing model as described inSection 3 to better reflect the short-term randomness natureof radio propagation. Another simplification of ns-2 is that,after a receiver begins to receive a frame and a new framearrives, the simulator only compares the signal strengthsbetween these two frames to determine if the ongoingreception is interfered with. As we pointed out earlier, thecomparison is actually the signal of the intended frameagainst the aggregated signal energy of all other transmis-sions. We extended the PHY layer in ns-2 to allow eachstation to keep track of all frames that it can detect and theaggregated background signals to make the capture model-ing more realistic. Finally, we emulated the design of theMIM PHY design as discussed before so that a receiver cancorrectly receive the stronger frame even when it hasalready engaged in receiving a weaker frame.

5.1 Simulation Configurations

Each of our simulated networks consists of a set ofconnections which are constructed as pairs of stationarysender and receiver stations. The senders and receivers arerandomly placed in a 1; 000 m� 1; 000 m. We assume thateach node knows its own location. Other parameters, suchas transmission power levels and antenna gains, are alsoassumed to be fixed and known to all stations therefore notincluded in simulation. In simulation, the ENH header onlycontains LOCT and LOCR fields of 32 bits each.

With such a model, the transmission power Pt is set to0.282W while RXThresh 1 and CSThresh are set toconfigure the transmission radius R of a station to 250 mand the carrier sense radius to 550 m. Each connection is aflow of UDP packets which are 1,000 bytes in size andtransmitted at 11 Mbps. To simplify the simulationimplementation, the base rate is also set to 11 Mbps. Eachsimulation is run for a fixed duration of 200 seconds. Eachpoint on the curves to be presented is an averaged result of50 simulation runs.

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1. RXThresh_ is the power value of a transmitted signal measured at theboundary of its transmission range R.

Since we could not find any reported data for captureratio, the capture ratio used in the simulations is derived bythe following method: For 11 Mbps, according to calcula-tions described by [23], it can be determined that 18 dB ofSNR is needed to achieve 10�8 BER. The processing gain of802.11’s 11 Mbps modulation is 11 dB. Thus, when onlyconsidering signals before receiver processing, the SNRrequirement is 7 dB. Roughly, this maps to five times thesignal power over interference. We adopt the same numberas the capture ratio.

We have modeled various scenarios of different stationdensities, data loads, errors in location estimation, etc. Inthese scenarios, we compare our LED with both the originalIEEE 802.11 DCF. As described in Section 4, we experimentwith two different flavors of LED: LED_CS and LED_RX.The LED_CS mechanism is an aggressive version of theLED mechanism, in which, when a station receives a frameit cannot decode,2 it simply assumes that its transmissionwill not interfere with that ongoing data delivery andtherefore should not block. On the other hand, LED_RX is aconservative version of LED in which a station assumes itstransmission will interfere with the ongoing data deliveryunder the same situation. The performances of LED_CS andLED_RX are compared against the original DCF. Whenappropriate, the mechanisms are also compared with theMACAW protocol.

During the simulation runs, we take the followingmeasurements:

1. Throughput: This counts the total number of bits persecond received network-wide by all the receivers.

2. Collision Packets: This counts the total number ofobserved collisions that involve data and ACKpackets by all the attempted deliveries per second.

3. Fairness Index: To measure the bandwidth sharingof the connections under different mechanisms, weuse Jain’s fairness index [10], [14] defined as follows:

F ¼ ðPN

i¼1 �iÞ2

NPN

i¼1 �2i

; ð23Þ

where N is the number of connections and �i is thenumber of received packets for connection i.

In our studies, we use the RTS-CTS mode in moststudies.

5.2 Impact of Node Density

Figs. 12, 13, 14, and 15 show various measurementsunder different numbers of connections. Since the area isfixed, the more connections there are, the higher nodedensity becomes. Each connection carries a constant bitrate (CBR) UDP flow at a rate of 20 packets per second.The plots in Fig. 12 also show 95 percent confidenceintervals for each point.3 All studies in this section aredone with RTS-CTS enabled.

As shown in Fig. 12, the LED_CS, LED_RX, andMACAW all have higher network throughput than the

1180 IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 6, NO. 10, OCTOBER 2007

Fig. 12. Throughput versus number of connections.

Fig. 13. Throughput enhancement versus number of connections.

Fig. 14. Packet collisions versus number of connections.

Fig. 15. Fairness index versus number of connections.

2. In ns-2 this is the situation where the received signal level is lowerthan the RXThresh_.

3. We have studied the confidence intervals for the rest of the experimentas well, which display similar levels of confidence concentration. For thesake of figure clarity, we usually do not include the confidence intervals inthe plots.

original DCF. Fig. 13 better illustrates the throughputimprovements of these mechanisms over the DCF by onlyshowing the percentage of throughput increase. At theirpeaks, LED_CS could achieve about 18 percent morethroughput than the DCF and the LED_RX could reach ashigh as 20 percent. Both are higher than MACAW’s too.

Fig. 14 shows the total number of collisions that occur inthe network occurred as an indication of the level oftransmission concurrency within the network. SinceLED_CS is more aggressive than LED_RX, as expected, ithas the highest collision count. However, simply tryingharder may not help in cases of high node density becausemore transmissions may result in more collisions at framereceivers, which actually bring the throughput down. Thisis why, in Fig. 12, we actually see that the conservationLED_RX has an edge over LED_CS in terms of networkthroughput.

Lacking more detailed knowledge regarding the ongoingtransmissions, the MACAW does not spatially reuse thechannel as intelligently as LED mechanisms. A station usingthe MACAW blocks it transmission only if it overhears CTSframes. As the simulations show, oftentimes, such anassessment is incorrect. Although the MACAW tries veryhard, as indicated by the high number of collisions inFig. 14, its throughput does not increase as hoped. As thestation density increases, the MACAW performance ap-proaches that of the Original DCF since the CTS frames willcover most of the network area, just like the RTS and CTSframes of the Original.

Fig. 15 compares the fairness of the mechanisms. Themechanisms of LED actually have slightly better fairnessthan the original DCF. An explanation for this is that LEDmechanisms reduce the well-known “exposed node” pro-blem in the original DCF, which is one of the major sourcesfor unfairness.

5.3 Impact of Network Load

Next, we experiment with different packet loads on theconnections to see their effects on LED performances. Thenetwork consists of 50 connections. All sources generatepackets at the same rate. This rate varies between 10 and400 packets per second.

Fig. 16 shows the overall network throughput. As shown,different from the previous results, LED_CS actually has thehighest throughput. LED_RX’s performance is not as goodas LED_CS and MACAW under high packet loads. At highpacket loads, the chance that there are some frames being

transmitted nearby increases. Thus, it is more likely forLED_RX to decide to block. This is opposite of LED_CS,which takes advantage of its aggressive mechanism tosqueeze in more transmissions.

The packet collisions for different mechanisms areshown in Fig. 17. The MACAW mechanism has the highestnumber of packet collisions because of its high aggressive-ness. Compared to LED_RX, LED_CS experiences morepacket collisions. However, overall, LED_CS has a sig-nificantly larger number of successful transmissions thanthe number of collisions. This is why we see the highthroughput of LED_CS.

Fig. 18 shows the fairness index of all the mechanismsunder different packet loads. LED_CS, LED_RX, andMACAW have similar fairness index measurements whichare higher than the original DCF mechanism. The overalltrend of the fairness index under different packet loads isvery similar to the one under different node densities. Afterall, increasing node density while each source still generatesthe same amount of traffic effectively increases the overallnetwork packet load.

We have also included scenarios using the basic channelaccess mode (no RTS-CTS) in this study. Fig. 19 and Fig. 20show the overall network throughput and fairness indexunder different network packet loads. The results are quitesimilar to the ones obtained under the RTS-CTS mode.

5.4 Impact of Capture Factor

As pointed out earlier, it may occur that several stationssimultaneously predict that their own transmissions willnot cause interference to the ongoing delivery and, hence,start their own transmissions. In this event, the aggregated

NADEEM AND JI: LOCATION-AWARE IEEE 802.11 FOR SPATIAL REUSE ENHANCEMENT 1181

Fig. 16. Throughput versus packet load. Fig. 17. Collision packets versus packet load.

Fig. 18. Fairness index versus packet load.

energy from all these side transmissions may change theresult of the capture effect and cause interference with theongoing delivery. Instead of modeling the details, whichwill undoubtedly be too complex, we approach thissituation by simply adjusting the capture ratio � used in(1) by a multiplier called the “capture factor” . Byincreasing the value over 1, we require the receivercapturing signal to be even stronger, thus decreasing thechance that the aggregated energy from side transmissionsinterfere.

The parameter has another purpose. In our approach,received power consists of two components, the averagereceived power component corresponding to distance and arandom variation corresponding to various types of short-term fading. The factor, when greater than 1, also servesas a safe margin in the received power comparison. Forinstance, with ¼ 1:1, even if the received power of atransmission, which is assessed to be safe to ongoingtransmission, fluctuates up by 10 percent, the ongoingtransmission is still able to capture the receiver.

The simulation network consists of 50 connections thateach carry a CBR traffic of 100 packets per second. We donot include MACAW in this set of experiments as this is notrelevant to MACAW. Fig. 21 shows both LED_CS andLED_RX throughput under different values of . Asexpected, when is small, a decreasing degrades theperformance of both mechanisms since there are morechances for channel competing stations to decide totransmit and result in frame collision at receiver. When is significantly large, further increasing also reducesthroughput since this underutilizes the capture effect. Veryinteresting is that, for the experiment configuration, ¼ 1:2

results in the optimal performance. We will study the effectof further and mechanisms for calculating its optimumvalue in our future work.

5.5 Impact of Errors in Node Locations

Quite often, nodes cannot acquire accurate location infor-mation. In this section, we study how location error affectsthe performance of LED. We do this set of experimentsusing a network configuration of 50 connections with CBRtraffic of 100 packets per second. Again, relevance reasonexcludes MACAW from this set of experiments. Thelocation errors are simulated with each station adding arandom offset within the range of ½�Err;Err� to its X andY position. We vary the error margin Err to see how overnetwork throughput changes for different error levels. Theresults are shown in Fig. 22.

Overall, LED performance is not very sensitive towardlocation estimation errors. The interesting result is that thebest performance is achieved when there exist smalllocation estimation errors. Apparently, location errors haveeffects similar to the capture factor , which helps reducinginterference when it is small. Of course, with high locationerrors, the performance of LED mechanisms degrade.Although both LED_CS and LED_RX suffer, it still pays tobe aggressive. The LED_CS still outperforms the LED_RX.

5.6 Impact of Network Degree

We experiment with the network degree to study theireffect on the protocol performance. We measure thenetwork degree by the average number of ongoing andoutgoing links per node. For example, when the parallelism

1182 IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 6, NO. 10, OCTOBER 2007

Fig. 19. Throughput versus packet load (basic access mode).

Fig. 20. Fairness index versus packet load (basic access mode).

Fig. 21. Throughput versus capture factor ðÞ.

Fig. 22. Throughput versus location error range.

degree is 1, it means that each node has one link eitheroutgoing (sender) or ingoing (receiver). We use 50 connec-tion pairs in a network of 100 nodes as the basicconfiguration with a parallelism degree of 1. For a higherparallelism degree, we add additional connections to theoriginal connections. To add a new connection, a node isselected randomly as the sender side of the connectionwhile the receiver side node is selected randomly from theneighbor node set of the sender node. In this experiment,we fix the packet transmission rate on each connection to be150 packets per second. Fig. 23 shows the throughput ofLED over DCF. As shown, LED_CS has the highestthroughput over LED_RX. LED_RX’s performance is notas good as LED_CS since it is a conservative mechanismand, with a small number of nodes as in our experiment(100 nodes), a node will block long periods of time while itcan transmit within such a period with no interference fromother transmissions. This is the opposite of LED_CS, whichtakes advantage of its aggressive mechanism and avoidssuch blocking periods.

5.7 Impact of Node Mobility

One great benefit of wireless communication is that itenables user mobility. In this section, we study theperformance of the LED mechanisms under different nodemobility levels.

The mobility model we use is a modified Random Way-Point model with the restriction that all connections arealways maintained. We vary the node’s maximum speedfrom 0 m/s, which is corresponding to stationary scenario,to 40 m/s.

Fig. 24 and Fig. 25 show the throughput of the network,with a 95 percent confidence interval displayed, atdifferent node mobility levels. The first figure enablesRTS/CTS exchange for a network of 50 connections thateach carry a CBR stream of 100 packets per second whilethe second figure disables RTS/CTS and uses a basicaccess scheme for a network of 50 connections that eachcarry a CBR stream of 200 packets per second. From thefigures, we can see that node mobility has no significanteffect on the relative performance of LED mechanismswith respect to the DCF.

When LED is used under mobility scenarios, an im-portant issue is how freshly a node learns its peer node’slocation. This directly affects how accurate the locationinformation is. For the RTS-CTS case, nodes can have fairlyrecent location information for their peer nodes because theRTS-CTS frames have just exchanged location information.For the no RTS-CTS case, the location information exchangeoccurs at each DATA-ACK exchange. Thus, how fresh apeer node’s location information is depends on data rate.Table 1 shows the mean times between consecutivesuccessful data transmissions over a connection at differentdata rates and their corresponding mean distances of travelwhere the maximum speed for the nodes is 40 m/s. Fromthis table, we can see how the results of this study concurwith the results of the location error study reported earlier.

6 CONCLUSION AND FUTURE WORKS

In this paper, we begin with showing that the 802.11 DCF isconservative in terms of collision estimation, with as muchas 30 percent of unnecessary blocking assessments. Then,we introduce an enhancement of the IEEE 802.11 DCF. Theenhancement, named the Location-Enhanced DCF (LED),piggybacks communication parameters, especially the loca-tions of transmitters and receivers with each frame. These

NADEEM AND JI: LOCATION-AWARE IEEE 802.11 FOR SPATIAL REUSE ENHANCEMENT 1183

Fig. 23. Throughput versus network degree.

Fig. 24. Throughput versus mobility.

Fig. 25. Throughput versus mobility (basic access mode).

TABLE 1Location Error Caused by Mobility

parameters may assist stations to better assess the channelcondition and make interference and capture estimations.We have also conducted extensive simulation studies onLED’s behavior and performance. The simulation resultsshow that LED may improve overall network throughputby as much as 20 percent over DCF with better fairness.LED consistently outperforms the DCF in most of thestudied scenarios.

It should be noted that, although LED can achieve betterthroughput, it is at the cost of trying harder with moretransmissions. This is indicated by higher collision countscompared to the original IEEE 802.11 DCF. Although theLED can achieve higher throughput despite the highcollision counts, this issue may limit the use of LED inenergy-constrained WLAN applications.

These excessive collisions are mostly the result of two

simplifications in LED design. They are: 1) a station is only

concerned if its own transmission will affect an ongoing

delivery and, consequently, it neither considers if its own

transmission can be received correctly by its destination nor

if its destination will be able to send ACK back, and 2) the

aggregated interference energy changes receiver capture

results, caused by transmissions from multiple stations,

simultaneously making negative collision estimation be-

cause each station only considers how its own transmission

may affect the ongoing delivery and not leave space for

other channel assessing stations to make the same decision.

For future work, we plan to investigate the issues further

and enhance the LED mechanism to address the problem.

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[24] A. Vasan, R. Ramjee, and T. Woo, “ECHOS: Enhanced Capacity802.11 Hotspots,” Proc. INFOCOM, Mar. 2005.

[25] C. Ware, J.F. Chicharo, and T. Wysocki, “Modelling of CaptureBehaviour in IEEE 802.11 Radio Modems,” Proc. IEEE Int’l Conf.Telecomm., June 2001.

[26] X. Yang and N. Vaidya, “On the Physical Carrier Sense in WirelessAd-Hoc Networks,” Proc. INFOCOM, Mar. 2005.

[27] F. Ye, S. Yi, and B. Sikdar, “Improving Spatial Reuse of IEEE802.11 Based Ad Hoc Networks,” Proc. IEEE Global Telecomm. Conf.(GLOBECOM ’03), Dec. 2003.

[28] J. Zhu, X. Guo, L. Yang, and W. Conner, “Leveraging SpatialReuse in 802.11 Mesh Networks with Enhanced Physical CarrierSensing,” Proc. IEEE Int’l Conf. Comm. (ICC ’04), June 2004.

Tamer Nadeem received the PhD degree fromthe Department of Computer Science, Universityof Maryland, College Park, in 2006. He was amember of the Maryland Information and Net-work Dynamics Laboratory (MIND) at the Uni-versity of Maryland. He is currently a researchscientist at Siemens Corporate Research. Hisresearch interests include wireless communica-tions, mobile ad hoc networks, energy efficientprotocols, sensor networks, peer-to-peer sys-

tems, pervasive computing, and location determination systems. He is amember of the IEEE, the IEEE Computer Society, and the ACM, anelected member for the Phi Kappa Phi honor society, an electedmember for the Sigma Xi honor society, and a member of theAssociation of Egyptian American Scholars.

Lusheng Ji received the PhD degree incomputer science from the University of Mary-land, where he developed multicast protocols forad hoc networks. He was a research scientist atthe Fujitsu Laboratories of America in CollegePark, Maryland. He is currently a senior techni-cal specialist at AT&T Labs Research. Hisresearch interests include ad hoc networks,routing protocols, m-commerce, and wirelesssecurity. He is a senior member of the IEEE.

. For more information on this or any other computing topic,please visit our Digital Library at www.computer.org/publications/dlib.

1184 IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 6, NO. 10, OCTOBER 2007