Measuring Transmission Opportunities in 802.11 Links

1516 IEEE/ACM TRANSACTIONS ON NETWORKING, VOL. 18, NO. 5, OCTOBER 2010

Measuring Transmission Opportunitiesin 802.11 Links

Domenico Giustiniano, David Malone, Douglas J. Leith, and Konstantina Papagiannaki, Member, IEEE

Abstract—We propose a powerful MAC/PHY cross-layer ap-proach to measuring IEEE 802.11 transmission opportunities inWLAN networks on a per-link basis. Our estimator can operateat a single station and it is able to: 1) classify losses caused bynoise, collisions, and hidden nodes; and 2) distinguish betweenthese losses and the unfairness caused by both exposed nodes andchannel capture. Our estimator provides quantitative measures ofthe different causes of lost transmission opportunities, requiringonly local measures at the 802.11 transmitter and no modificationto the 802.11 protocol or in other stations. Our approach is suitedto implementation on commodity hardware, and we demonstrateour prototype implementation via experimental assessments. Wefinally show how our estimator can help the WLAN station toimprove its local performance.

Index Terms—802.11, channel measurement, carrier-sense mul-tiple access with collision avoidance (CSMA/CA).

I. INTRODUCTION

T HE practical performance of IEEE 802.11 depends onthe availability of transmission opportunities at the under-

lying 802.11 card. In all the cases when transmission opportu-nities are lost, 802.11 stations will be affected by throughputdrops, higher delay, and unfairness. Lost transmission opportu-nities, arise from the combined MAC and PHY environment atboth sender and receiver on each 802.11 link. Thus, their im-pact cannot be estimated purely on the basis of PHY measure-ments (see, e.g., [1]–[5]), such as signal-to-noise ratio (SNR)at the receiver. Alternative high-level characterizations, such asthroughput and delay statistics, can only determine the effectbut not the cause of a lost transmission opportunity. In fact, thenature and number of transmission opportunities are a combi-nation of MAC- and PHY-layer effects.The need for a cross-layer technique can be readily seen,

for example, from the fact that hidden nodes effects, exposednodes, capture effects, and so on are all associated with cross-layer issues. Other effects are instead due only to features ofthe MAC layer, such as the collisions, or to PHY layer, suchas a low SNR. Furthermore, tasks such as rate adaptation, con-tention window selection, power control, and carrier sense se-lection—all essential for improving and optimizing the networkperformance—are under the control of the transmitter.

Manuscript received April 02, 2008; revised May 01, 2009; accepted Feb-ruary 11, 2010; approved by IEEE/ACM TRANSACTIONS ON NETWORKING Ed-itor S. Das. Date of publication June 14, 2010; date of current version Oc-tober 15, 2010. This work was supported by Science Foundation Ireland grantsIN3/03/I346 and 07/IN.1/I901.D. Giustiniano is with Dipartimento di Ingegneria Elettronica, Università

degli Studi di Roma-Tor Vergata, 00133 Roma, Italy.D. Malone and D. J. Leith are with Hamilton Institute, National University

of Ireland, Maynooth, Ireland (e-mail: [email protected]).K. Papagiannaki is with Intel Research, Pittsburgh, PA 15213 USA.Digital Object Identifier 10.1109/TNET.2010.2051038

TABLE ITRANSMISSION IMPAIRMENTS AND AVAILABLE REMEDIES AT TRANSMITTER

Despite the difficulty of measuring and understanding trans-mission conditions, the potential benefits arising from the avail-ability of accurate and reliable data are considerable and de-pend crucially on the availability of suitable measurements ofthe transmission quality at each link. It is the current lack of suchmeasurements that underlies the poor performance of many ap-proaches currently implemented in commodity hardware. Forexample, at present, rate adaptation is commonly based on thenumber of transmission retries (e.g., a typical approach mightinvolve lowering the rate after retries and increasing the rateafter successful transmissions). However, since the numberof retries is affected not just by channel noise, but is also closelylinked to the number of contending stations (with associated col-lision related losses), this can easily lead to poor performance[6]. Analogous problems occur in the presence of hidden nodes,e.g., see [7]. The availability of a measure of the loss rate specif-ically induced by channel noise would potentially allow muchmore effective rate adaptation algorithms to be employed. Sim-ilarly, channel selection algorithms are fundamentally related tochannel impairments and typically depend upon the availabilityof an appropriate channel quality metric, which can then be op-timized by a suitable search over available channels. Effectivecarrier sense adjustment is also strongly dependent on link mea-surements. Causes of lost transmission opportunities and cor-responding remedies available at the transmitter are shown inTable I.Measurement of transmission opportunities is particularly

topical since the trend toward increasingly dense wirelessdeployments is creating a real need for effective approaches forchannel allocation/hopping, power control, interference mitiga-tion [8], [9], etc. New applications, such as mesh networks andin-home content distribution, are creating new quality-of-ser-vice demands that require more sophisticated approaches toradio resource allocation [10].In this paper, we propose a powerful new transmitter-side

MAC/PHY cross-layer approach to measuring the transmissionopportunities in 802.11 WLANs. Unlike previous approaches,we explicitly classify lost transmission opportunities into noise-related losses, collision induced losses, and hidden-node lossesand distinguish these losses from the unfairness caused by ex-posed nodes and capture effects. Our approach distinguishesamong these different types of impairments on a per-link basis

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GIUSTINIANO et al.: MEASURING TRANSMISSION OPPORTUNITIES IN 802.11 LINKS 1517

and provides separate quantitative measures of the severity ofeach. We thus make available new measures that we expect tobe of direct use for rate adaptation, channel allocation, etc. Wetake advantage of the native characteristics of the 802.11 pro-tocol (such as timing constraints, channel busy detection, and soon) without requiring any modification to the 802.11 standard.Our approach is suited to implementation on commodity hard-ware, and we demonstrate both a prototype implementation andexperimental measurements. It is vital to demonstrate operationin a real radio environment, not only because of the difficulty ofdeveloping realistic radio propagation models, but also becauseimportant impairments such as hidden nodes and capture effectsare affected by low-level issues (e.g., interactions between am-plifier and antenna design as well as radio propagation) that aredifficult to model in simulations. We note that many of the mea-surements presented are new and of interest in their own right.The paper is organized as follows. In Section II, we review

related work and show how our estimator differs from the stateof the art. In Section III, we briefly review the 802.11 MACand then categorize the main causes of lost transmission oppor-tunities. In Sections IV and V, we introduce our estimation ap-proach. We describe our test bed setup in Section VI and presentextensive experimental measurements in both Sections VII andVIII, evaluating this approach in a wide range of real radio envi-ronments. Finally, we summarize our conclusions in Section IX.

II. RELATED WORK

Previous work on estimating 802.11 channel conditionscan be classified into three categories. First, PHY link-levelapproaches use SNR and bit error rate (BER). Second, MACapproaches rely on throughput and delay statistics, or frameloss statistics derived from transmitted frames that are not ac-knowleged (ACKed) and/or from signaling messages. Finally,cross-layer MAC/PHY approaches aim to combine informationat both MAC and PHY layers.Most work on PHY-layer approaches is based on SNR mea-

surements, e.g., [2] and [5]. The basic idea is to a priori mapSNR measures into link quality estimates. However, there arelimitations of this approach. First, frames are usually lost at thereceiver (802.11 data packet) and not at the transmitter (802.11ACK packet). Consequently, SNR measurements at the receiverside must be passed to the transmitter, which is “responsible”for improving the link performance via carrier sense, rate, trans-mission power, and channel adaptations. This requires the useof a modified 802.11 protocol to send feedbacks to the trans-mitter. Second, the mapping between measured SNR and de-livery probability rate is generally specific to each link [1] andmay be time-varying, thus requiring a continuous update of thestatistics from the receiver to the transmitter. Finally, the corre-lation between SNR and actual packet delivery rate can be weakin some situations [3].To bypass problems related to SNR-only measurements, [16]

performs loss diagnosis by examining the error pattern within aphysical-layer symbol in order to expose statistical differencesbetween collision and weak signal-based losses. In the proposedsystem, the AP relays the entire packet, received in error, backto the client for analysis, with a consequent overhead for thenetwork. Furthermore, the analysis is limited to collision andnoise-related losses.

In contrast, in this paper we provide a local methodology toexplicitly classify noise-related losses, collision-induced losses,hidden-node losses, and impairments caused by exposed nodesand capture effects. As part of our validation, we compare ourscheme to a receiver-side estimator for the loss diagnosis. Thisreceiver-based scheme cannot only differentiate between colli-sion and weak signal-based losses, but in general, among noise,collisions and hidden-node losses.Regarding MAC approaches, RTS/CTS signals can be used

to distinguish collisions from channel noise losses, e.g., [22] and[23]. However, such approaches can perform poorly in the pres-ence of hidden nodes as we demonstrate in Section VII. [24]considers an approximate MAC-layer approach for detectingthe presence of hidden nodes, but does not consider other typesof transmission impairments.With regard to combined MAC/PHY approaches, early work

related to the present paper is presented in [18] and [19]. How-ever, this uses a channel busy/idle approach that is confined todistinguishing between collision and noise-related losses anddoes not allow consideration of hidden nodes or exposed nodeand capture effects. In [20], techniques to separate noise fromcollision losses are considered, but hidden node techniques areleft as future work. In [21], the importance of root-cause anal-ysis of transmission failures is considered, and a monitoringsystem based on multiple sniffers is designed.

III. CSMA/CA PROTOCOL AND RELATED IMPAIRMENTS

A. CSMA/CA Protocol

In 802.11 WLANs, the basic mechanism controlling mediumaccess is the Distributed Coordination Function (DCF) [11].This is a random access scheme, based on carrier-sense multipleaccess with collision avoidance (CSMA/CA). In the DCF BasicAccess mode, a station with a new packet to transmit selects arandom backoff counter. Time is slotted, and if the channel issensed idle, the station first waits for a distributed interframespace (DIFS), then decrements the backoff counter each PHYtime slot. If the channel is detected busy, the countdown is haltedand only resumed after the channel is detected idle again for aDIFS. Channel idle/busy status is sensed via the following.• Clear channel assessment (CCA) at the physical level,which is based on a carrier-sense threshold for energydetection, e.g., 80 dBm. CCA is expected to be updatedevery physical slot time. It aims to detect transmissionswithin the interference range.

• Network Allocation Vector (NAV) timer at MAC level,which is encapsulated in the MAC header of each 802.11frame and is used to predict the end of a received frameon air. It is naturally updated once per packet and canonly gather information from stations within the decodingrange. This method is also called virtual carrier sense.

The channel is detected as idle if the CCA detects the channel asidle and the NAV is zero. Otherwise, the channel is detected asbusy. A station transmits when the backoff counter reaches zero.The countdown process is illustrated schematically in Fig. 1.The 802.11 handshake imposes a half-duplex process wherebyan acknowledgment (ACK) is always sent by the receiver uponthe successful receipt of a unicast frame. The ACK is sent aftera period of time called the short interframe space (SIFS). As the


Fig. 1. DCF protocol summary.

SIFS is shorter than a DIFS, no other station is able to detect thechannel idle for a DIFS until the end of the ACK transmission.In addition to the foregoing Basic Access mode, an optional

four-way handshaking technique, known as Request-To-Send/Clear-To-Send (RTS/CTS) mode, is available. Before transmit-ting a packet, a station operating in RTS/CTS mode reservesthe channel by sending a special Request-To-Send short frame.The destination station acknowledges the receipt of an RTS bysending back a CTS frame, after which normal packet transmis-sion and ACK response occurs.The DCF allows the fragmentation of packets into smaller

units. Each fragment is sent as an ordinary 802.11 frame, whichthe sender expects to be ACKed. However, the fragmentsmay be sent as a burst. That is, the first fragment contends formedium access as usual. When the first fragment is successfullysent, subsequent fragments are sent after a SIFS, so no colli-sions are possible. In addition, the medium is reserved usingvirtual carrier sense for the next fragment both at the sender(by setting the 802.11 NAV field in the fragment) and at thereceiver (by updating the NAV in the ACK). Burst transmissionis halted after the last fragment has been sent or when loss isdetected.

B. Losses of Transmission OpportunitiesIn this section, we categorize the main impairments that can

affect transmissions between an 802.11 sender and receiver. Be-fore proceeding, it is important to emphasize that a two-way (orfour-way with RTS-CTS) handshake is used in 802.11. Hence,the quality of a link is determined by conditions at both thesender and the receiver stations. For example, low link qualityat the receiver can mean that data packets transmitted by thesender cannot be decoded at the receiver. Similarly, low linkquality at the sender can mean that ACK packets transmitted bythe receiver cannot be decoded at the sender. These conditionsimmediate follow.• Measuring the SNR (or other local properties) at eitherthe sender or receiver alone is insufficient to determine itsquality. Instead, it is necessary to recognize the intrinsi-cally two-way nature of each link.

• Links are directional as data packets, and ACKs may havedifferent properties, e.g., coding rate, duration, NAV pro-tection. Collisions and interference with transmissions byother stations can affect each end of a link differently.

• Since each station is typically located in a different phys-ical position, its local radio environment is generallydifferent from that of other stations. Hence, we need tomeasure the transmission opportunities between eachsender–receiver pair individually. In particular, we cannotreliably infer the properties of one link frommeasurementstaken on another link, even if the links share a common

sender, e.g., the access point (AP) in an infrastructuremode WLAN. Furthermore, due to the directional natureof link quality, we need to measure quality in each direc-tion separately and generally cannot use measurementsfrom one direction to reliably infer the quality in theopposite direction. An example illustrating this is shownlater in Section VIII-B.

As we will see, the manner in which link impairments aremanifested is closely linked to the interaction between MACand PHY operation. We distinguish five main types of link im-pairment when using the 802.11 DCF.1) Collisions: Collisions are part of the correct operation ofCSMA/CA. Collisions occur when two or more stationshave simultaneously decremented their backoff counter to0 and then transmit. Note that collisions can only occuron data packet transmissions. The level of collision-in-duced packet losses is strongly load-dependent. For ex-ample, 802.11b with four saturated nodes has a collisionprobability of around 14%, while 20 saturated have a col-lision probability of around 40% (numbers from the modelin [15]). We denote by the probability that a transmitteddata frame is lost due to a collision.

2) Hidden nodes: Frame corruption due to concurrent trans-missions other than collisions are referred to as hiddennode interference.We denote by the probability thata data transmission fails to be received correctly due tohidden node interference. Similarly, we denote bythe probability that an ACK transmission is lost due tohidden node interference. A lost data packet or a lost ACKboth lead to a failed transmission, and so we combinedata and ACK losses into an overall hidden node errorprobability .

3) Noise errors: Frame corruption due to sources other thantransmissions by other 802.11 stations are referred to asnoise losses. We denote by (respectively, )the probability that a data (respectively, ACK) frame is lostdue to noise-related errors. Since data and ACK losses bothlead to a failed transmission, we lump these together intoa combined noise loss probability .

4) Exposed nodes: The inability to transmit 802.11 frames isnot just due to link losses. In particular, the carrier sensemechanism used in 802.11 to sense channel busy condi-tions may incorrectly classify the conditions. We denoteby the probability that a slot is erroneously detectedas busy when in fact a successful transmission could havebeen made. Such errors lead to an unnecessary pause inthe backoff countdown and so to a reduction in achievablethroughput.

5) Capture effect: A second impairment that does not causelosses is the so-called physical layer capture (PLC).Specifically, we denote by the probability of suc-cessful reception of a frame when a collision occurs. Thiscan occur, for example, when the colliding transmissionshave different received signal power—the receiver maythen be able to decode the higher power frame. For ex-ample, [17] shows that for 802.11b PLC can occur whena frame with higher received power arrives within thephysical layer preamble of a lower power frame. Ourmeasurements—not shown here for lack of space—haveconfirmed this finding and found similar behavior for


TABLE IISUMMARY OF MEASUREMENTS USED AND PROPOSED ESTIMATORS

802.11g. Differences in received power can easily occurdue to differences in the physical location of the transmit-ters (one station may be closer to the receiver than others),differences in antenna gain, etc. The PLC effect can leadto severe imbalance of the network resource and hence inthe throughputs achieved by contending stations (and soto unfairness).

IV. ESTIMATING LOSSES OF TRANSMISSION OPPORTUNITIESOur aim is to develop a sender-side estimator capable of dis-

tinguishing the different types of impairments and providingquantitative measurements of the related losses. To do this, wemake use of the key observation that these impairments are inti-mately related to MAC operation. We therefore exploit the flex-ibility already present in the 802.11 MAC to enable us to dis-tinguish the impact of the different impairments. The techniquecan use live traffic, and so can naturally estimate each activelink being used at a station. If measurements of inactive linksare required, this could be achieved by using probe traffic.We make use of the following properties of the 802.11 MAC.• Time is slotted, with well-defined boundaries at whichframe transmissions by a station are permitted.

• The standard data-ACK handshake is affected by all typesof link impairment considered, and a sender-side analysiscan reveal any loss.

• When fragmentation is enabled, second and subsequentfragment transmissions are protected from collisions andhidden nodes by the NAV values in the fragments andACKs. We treat hidden nodes that are unable to decodeeither NAV value as channel noise. Instead of using frag-ments, we could use TXOP packet bursting, although thisis only available in 802.11e [12] and would require theNAV value set in the MAC ACK. RTS/CTS might also beused, but in practice can perform poorly, as we will demon-strate later in the paper.

• Transmissions occurring before a DIFS are protected fromcollisions. This is used, for example, to protect ACK trans-missions, which are transmitted after a SIFS interval. The802.11 DCF also permits transmissions after a PIFS in-terval (with ).While the full 802.11point coordination function (PCF) is rarely fully imple-mented, the ability to transmit after a PIFS is widely avail-able; if not as part of the PCF, it is available as part WME(wireless multimedia extensions), the subset of 802.11e,commonly available on commodity hardware.

In the following sections, we consider in more detail howthese properties can be exploited to obtain new powerful mea-surements of the quality of each link. A summary of our estima-tors and notation is shown in Table II.

A. Estimating Noise ErrorsConsider a station sending fragmented packets to a given re-

ceiver. Each fragment is immediately ACKed by the receiverwhen it arrives, allowing detection of loss. Fragments are sentback-to-back with a SIFS interval between them. Hence, secondand subsequent packets are protected from collisions. Impor-tantly, fragment ACK frames update the NAV, and so the frag-ment-ACK handshake is akin to an RTS-CTS exchange from thepoint of view of hidden nodes.1 Hence, second and subsequentfragments are also protected from hidden node collisions. Thatis, while the first fragment will be subject to collisions, noise,and hidden node errors, subsequent fragments are only subjectto noise errors, and we have that

(1)

where the station transmits second and subsequent dataframes and of these are successful because an ACK isreceived. We can therefore directly estimate the probabilityof noise errors from the fraction of second and subsequentfragments with no ACK

(2)

Since the impact of noise losses may depend on the frame length(longer frames typically having higher probability of experi-encing bit errors), we equalize the length of the fragments wesend and transmit fragments of length equal to the packet sizeused for regular data transmissions. The frame loss rate esti-mated from fragment measurements can then be reliably appliedto estimate the loss rate for other transmissions.

B. Estimating Hidden Node InterferenceWe now distinguish frame losses due to hidden node interfer-

ence. To achieve this, we exploit the fact that frames transmittedafter a PIFS are protected from collisions since other transmis-sions must defer for a DIFS interval after sensing the channel1As mentioned, we do not rely on RTS/CTS since it can perform poorly; see

Section VII.


Fig. 2. MAC slot boundaries at which transmissions are permitted. Different types of MAC slot are possible: idle slots (corresponding to PHY slots), busy slotsdue to transmissions by other stations (marked “Other”), and busy slots due to transmissions the station of interest (marked “Tx_”). “Other” transmissions includeboth successful and unsuccessful transmissions.

to be idle, with DIFS PIFS. Losses on PIFS frames are dueeither to noise or hidden node interference. That is

(3)

where the station transmits data frames after a PIFS, and,of these, are successful because an ACK is received. Wecan now use our estimate of [based on fragment loss mea-surements; see (2)] to allow estimation of the probability ofhidden node losses as

(4)

C. Estimating Collision RateConsider a station sending ordinary data packets (i.e., sent

after DIFS and not fragmented) to a given receiver. Supposethat, over some time period, the station contends and transmitsdata frames times, and, of these, are successful because anACK is received. As discussed previously, the possible sourcesof frame loss are collisions, hidden nodes, and noise errors. As-suming that these sources of frame loss are independent, if thestation transmits, the probability of success over the link is

(5)

Finally, can be estimated from (5) and (3)

(6)

V. IMPAIRMENTS TO TRANSMISSION OPPORTUNITIES THATDO NOT LEAD TO FRAME LOSS

Section IV presents a straightforward approach for esti-mating the magnitude of link impairments that lead to frameloss, namely collisions, hidden nodes, and noise. The estimatesrequire only very simple measurements that are readily avail-able on commodity hardware. In this section, we now considermethods for estimating capture and exposed node effects.These impairments do not lead directly to frame losses, but cannevertheless lead to unfairness in throughput/delay.In order to estimate capture and exposed node effects, we

make use of additional measurements. In particular, measure-ments of channel idle and busy periods. Here, idle/busy refersto time as measured in MAC slots rather than in PHY slots. Inthe next section, we discuss MAC slots in more detail. Then, wediscuss estimating capture and exposed node effects. Note thatwhile these additional measurements offer further insight intothe wireless environment, they are not necessary to estimate thebasic quantities , , and .

A. MAC SlotsThe slotted CSMA/CA process creates well-defined bound-

aries at which frame transmissions by a station are permitted.The time between these boundaries we call MAC slots (as

distinct from PHY slots). Considering operation from theviewpoint of a station, say station 1, we have the followingpossibilities.1) Station 1 has transmitted and received an ACK. We callthese slots successful transmissions.

2) Station 1 has transmitted, timed out while waiting for anACK, and is about to resume its backoff.We call these slotsunsuccessful transmissions.

3) Station 1 has seen the medium as idle and, if backoff isin progress, has decremented its backoff counter. We callthese idle slots.

4) Station 1 has detected the medium as busy due to oneor more other nodes transmitting and has suspended itsbackoff until backoff can resume. We call these slotsother transmissions and include both successful and un-successful transmissions of other stations. Note that eachbusy period is counted as a single slot, so these busy slotsare closer to the MAC’s view than the PHY’s.

These events are illustrated (not to scale) in Fig. 2. Transmis-sions by station 1 are only permitted at event boundaries.We also make the following assumptions.Assumption 1: The probability that at least one other station

transmits in an arbitrary slot does not depend on whether sta-tion 1 transmits or not.Assumption 2: The collision probability is independent of the

backoff stage of station 1.With these assumptions, the probability of a collision is then

precisely the probability that at a slot boundary the channel isbusy due to a transmission by one or more other stations. Wenote that our assumptions are reasonable in a distributed randomaccess MAC scheme such as CSMA/CA and, indeed, these as-sumptions are central to well-established models of 802.11 op-eration such as that of Bianchi [15] and others (e.g., the nonsat-urated heterogeneous model in [27]).

B. Capture and Exposed NodesSuppose there are MAC slots in which our station does

not transmit, and that of these are idle slots and areother transmissions slots. These quantities can be measured byappropriate sensing of the channel idle/busy status (using bothCCA and NAV). Under ideal conditions, the ratio isa measure of the probability of collision [18], [19]. However,this carrier-sense-based measurement is biased by impairmentsthat do not generate packet losses. These are the slots whereeither the channel is busy when, in fact, a transmission wouldbe successful (exposed nodes) or the slots where we expect acollision loss but we do not collide (capture effect).We thereforehave that

(7)

where is the collision probability, the probability that thechannel is sensed busy due to exposed node behavior, and


TABLE IIIFAIRNESS WITH POWER TUNING

the probability that the channel is sensed busy due to capture ef-fects. Combining our estimate of from (6) with the additionalinformation in (7), we can estimate

(8)

In effect, we are estimating the number of collisions losses thatwe expect based on the carrier sense environment and com-paring it to the actual collision rate. The discrepancy, if any,provides a measure of exposed node and capture effects—bothof which are associated with apparently busy slots during whicha successful transmission can in fact take place.Note that these idle/busy measurements can also be used to

estimate the collision probability in the absence of exposed nodeor capture effects (see [18] and [19]), but this is not possible inthe more general setting considered here.

VI. IMPLEMENTATION ON COMMODITY HARDWARE ANDTESTBED SETUP

A. ImplementationWe have implemented the foregoing estimators using a com-

bination of driver and firmware modifications to commoditynetwork cards using the Atheros AR5212/AR5213 and Intel2915ABG chipsets. The proposed estimators are summarized inTable III. The TX counters would be maintained per link (i.e.,for each active physical destination). In the case of a station ina traditional infrastructure mode network, all traffic is sent viathe AP, so only one set of counters is required.The estimators of collision rate, hidden node, and noise

errors described in Section IV can be implemented via straight-forward driver modifications. In our work, they have beenmainly tested on Atheros cards and the widely used MADWiFidriver. To transmit frames after a PIFS interval, we made useof the wireless multimedia enhancements (WME) features,which allow dynamic adjustment of the TXOP, CWmin, andAIFS parameters for each Access Category of 802.11e. Inparticular, we created an access category with MAC settings

. All trafficsent via the queue associated with this access category is thentransmitted using PIFS. A second access category and queueis defined for normal traffic. On this queue, data packets arefragmented in two fragments, which is sufficient for assessingour estimator.2 By appropriately directing packets to thesetwo queues, we can collect statistics for the overall number2Other traffic configurations are possible, e.g., to fragment only the PIFS

traffic.

of transmissions , , and and number of successfultransmissions , , and (transmissions for which a MACACK is received). In our implementation, packets are allocatedbetween queues at the driver level, although other solutions arepossible.The estimators in Section V require measurement of the

number of and busy and idle MAC slots. This requirescarrier sense information. We modified the card firmwareand microcode on cards using the Intel 2915ABG chipset toperform the necessary measurements and to expose these tothe driver. Recent versions of Atheros driver provide access tohardware registers that allow us to infer the number of idle andbusy slots (e.g., see [25]), however we have conducted thesetests with the Intel chipset. Our implementation implicitly usesthe same carrier sense threshold as the rest of the MAC.We will also cross-validate a number of our results based on

the number of CRC errors, , observed at a receivingSTA. This counter has also been retrieved from the microcodein Intel cards and driver code in Atheros cards. This cross vali-dation is described in detail in Section VI-C.

B. Testbed Setup

To evaluate the estimators, we performed experimental mea-surements over a wide range of network conditions, of which wepresent a subset here. Our testbed consists of Soekris net4801devices running Linux and configured in infrastructure mode.Stations transmit 1400-byte UDP packets to an AP equippedwith a NIC using the Intel 2915ABG chipset or Atheros AR5213chipset, according to the specific test. As the impact of impair-ments can be high in even unsaturated conditions [27], we haveconsidered a range of traffic loads. When not explicitly stated,nodes can be assumed to be saturated. Unless otherwise speci-fied, the physical rate is set to 6 Mb/s in each station, time slotsare set to 20 s on both Intel and Atheros NICs, and the car-rier sense threshold for the Intel NICs was set to 80 dBm,while the carrier sense level used with the Atheros NICs isthe default value (set in the binary component—HAL—of theAtheros MADWiFi driver). In all experiments, automatic rateselection and the RTS/CTS mechanism are disabled unless oth-erwise stated. Antenna diversity functionality is also disabled(see [4] and therein), together with any proprietary mechanismsat MAC level. External interference levels are measured using aspectrum analyzer. Link impairments are generated as follows.• Noise errors: In the test bed, we modify the SNR of a linkby a combination of adjusting the physical separation ofstations and/or adjustment of the transmit power used. In


Fig. 3. Topology for hidden node tests.

Fig. 4. Topology for exposed node tests.

Fig. 5. Topology for physical layer capture tests.

this way, we can roughly control conditions to allow inves-tigation of the ability of the proposed estimator to measurethe level of frame losses due to noise errors on a link.

• Collisions: The level of collision-induced losses is adjustedby varying the number of contending stations and their of-fered traffic load.

• Hidden nodes: Hidden node effects are evaluated usingscenarios based on the setup illustrated in Fig. 3. We havea number of transmitting nodes and a receiver. The hiddennode transmits to an independent receiver. We ensure thatthe following conditions hold: the link from the transmitterto our receiver is of high quality in isolation; the link fromthe hidden node to the hidden receiver is of high quality inisolation; a link cannot be established from the transmitterto the hidden node; losses occur when the hidden node op-erates at the same time as the transmitter.

• Exposed nodes: Exposed nodes are investigated via asetup with up to two interfering WLANs, as depicted inFig. 4. In more detail, and are associated to

(WLAN 1), while and are associated to(WLAN 2). In WLAN 1, we verify that: 1)

receives the signals from WLAN 2 ( and ) athigher strength than the carrier sense threshold; 2) the

link3 is of higher signal quality than theand links, so that may

successfully decode any signal from , despite theinterference from WLAN 2.

• Capture effects: Capture effects are studied using the setupillustrated in Fig. 5. Two stations and are asso-ciated to . We verify that the link is ofhigher signal quality than the link such thattransmissions by are successfully received ateven when they collide with transmissions by , i.e.,

can capture the channel.3We denote by a link with data sent from A to B.

C. Cross Validation of Frame Loss Impairments

To help validate the sender-side measurements obtainedusing the estimator in the previous section, in our experi-mental tests we also perform loss diagnosis at the receiverside. Particularly, we make use of the following independentmeasurements for the differentiation of the noise-related losses,collision-induced losses, and hidden-node losses.The 802.11 frame consists of a physical layer convergence

preamble (PLCP) and MAC payload called the physical servicedata unit (PSDU). Each PSDU is protected with a 32-bit cyclicredundancy check (CRC checksum). At the PHY level, errors inframe reception can be classified as either PHY or CRC errors.• An error occurs on the PLCP preamble or header. We callthese PHY errors.

• The PLCP is correctly decoded, but the PSDU CRC fails.We call this a CRC32 error. Note that the presence of aCRC32 error notification on a received frame implies thatno errors occurred in the PLCP.

We analyze the count of CRC32 errors for validation mea-surements, that is we consider when collisions, channel noise,and/or hidden nodes result in CRC errors.1) Collisions: First, note that in a collision, two or moretransmit stations have chosen the same PHY slot to starttransmission. We assume that a receiver station will notonly observe this as a busy slot, but that it will also detecteither a PHY error or, in the case of physical layer capturein the PLCP, a CRC error. We split the probability ofcollision

(9)

where is the probability of a collision resulting in aPHY error and the probability of a collision resultingin a CRC error. Thus collisions will be observed by theCRC estimator.

2) Noise errors: Second, consider channel noise. Typically,the PLCP is sent at a substantially lower rate than thePSDU, so we assume that channel noise never results ina PHY error, but instead in a CRC error.

3) Hidden nodes: Finally, consider the impact of hiddennodes. The receiver will see a certain number of hiddennode errors as simple collisions, when a hidden nodeand a ordinary node select the same slot, as illustrated atpoint 1 in Fig. 6. These will contribute to . However,hidden-node transmissions beginning in later slots (i.e.,after an ordinary node has already started) may result inmore complex errors. In our experiments, we use 802.11gtransmissions with a PLCP of 20 s and the 802.11bcompatible slot length of 20 s. For this setup, shown inFig. 6, we expect all of the hidden node errors that arenot simple collisions to result in CRC errors because thehidden node will not transmit until after the PLCP hasbeen transmitted.To confirm this assumption, we took two hidden nodestransmitting at 300 f/s (frames per second). Fig. 7 showsthe fraction of retry errors at the transmitter that aremapped into CRCerr frames at the receiver. We see aconsistent level of about 91%. The remaining 9% areattributed to both nodes choosing to transmit in the sameslot, thus leading to PHY errors, as we expect.


Fig. 6. Hidden node errors for an 802.11 frame (not to scale).

Fig. 7. Probability of CRC errors with a hidden node.

Thus, the CRC errors seen at the receiver satisfy:

where is the number of CRC32 errors and isthe number of busy MAC slots seen at the receiver.

VII. EXPERIMENTAL ASSESSMENTIn this section, we present experimental measurements to val-

idate and explore the practical utility of the proposed estima-tors. We argue that experimental testing is vital when assessinglink-quality estimators since issues such as complex radio prop-agation effects, real antenna behavior, front-end amplifier is-sues, etc., can all have an important impact on performance, yetare difficult to capture accurately in simulations. Experimentaltesting also highlights implementation issues, demonstrates thepracticality of operation on commodity hardware, and generallyhelps to build greater confidence in the viability of the proposedapproach. Our results are presented in terms of quantities suchas , , and . Throughputs figures are not presented dueto space constraints, but can be inferred from the fraction ofpackets not subject to some impairment.

A. Collisions Only, No Noise, No Hidden NodesWe begin by considering a simple scenario with a clean

channel and no hidden nodes. A low level of RF interferenceis confirmed by a spectrum analyzer. We vary the number of

Fig. 8. Estimates of , , and versus number of contending stations. Cleanchannel, no hidden nodes.

contending wireless stations so as to vary the collision rate.Each station generates traffic at a rate of 300 f/s, which issufficient to saturate the network, for an interval of 600 s. Ofthe traffic, 10% is sent through the PIFS queue, while the restis sent through the BE queue.Fig. 8 shows the measured estimates of , , and aver-

aged over the experiment. We can immediately make a numberof observations.• The collision probability increases with the number ofstations, as expected.

• The noise loss probability , estimated from measure-ments on subsequent fragments, is negligible, as expected.

• The hidden-node loss probability is consistently low, asexpected.

Although a simple test scenario, it is nevertheless encouragingthat these initial tests indicate correct operation of the estima-tors. In particular, the ability to distinguish collision losses fromnoise and hidden node effects. We confirm this in the followingsections by varying the level of noise and hidden node lossesover a wide range of operating conditions.

B. Channel Noise Only, No Collisions, No Hidden NodesTo explore the impact of channel noise, we begin this section

by considering a setup with one transmitting and one receivingstation, and thus no collisions or hidden nodes (more complexsetups with noise, collisions, and hidden nodes are considered inlater sections). The physical rate for transmission is fixed to 12Mb/s and sending rate at 300 f/s, which saturates the transmitqueue. The link is adjusted to have low SNR, and thus a highnoise error rate, according to the test bed setup described inSection VI-B. Recall that noise losses are measured via the lossrate for subsequent fragments. Fig. 9(a) plots the measured lossrate for first and second fragments on normal traffic and PIFStraffic. It can be seen that the loss rates are all similar, as ex-pected in the absence of collisions and hidden nodes. This dataalso helps to confirm that the loss rate measured on second frag-ments is a good indicator of the noise loss rate experienced byother types of traffic.As further validation of correct operation of the estimator,

we classify the loss percentage of transmitted/received frames,respectively:• , i.e., the loss rate for first fragmenttransmissions;


Fig. 9. Measured loss rates for low SNR link, no collisions, no hidden nodes.is loss rate for first fragment transmissions, loss rate for second

fragments (an estimate of ), the error rate measured at the receiverfor first fragments, the rate for second fragments. (a) Measured loss rateof first and second fragments and PIFS traffic. (b) Cross validation of measurednoise loss rate.

• , i.e., the loss rate for second andsubsequent fragments;

• , i.e., the rate of CRC errorsat the receiver for first fragments ;

• , i.e., the rate of CRC errorsat the receiver for subsequent fragments .

The measurement is our proposed estimator for , theframe loss rate due to noise errors. Note that the and

measurements are obtained by an entirely independentestimator (operating at the receiver) from the andmeasurements (operating at the transmitter). As expected,Fig. 9(b) shows that the two estimators report very similarstatistics for first and subsequent fragments, as the only errorspresent are noise errors.4

C. Hidden Nodes Only, No Collisions, No Noise

We now consider estimation of hidden node losses, againstarting with a simple setup in this section in order to help gainclear insight into performance, but with more complex situa-tions considered in later sections.Fig. 10 reports the experimental results for a setup with only

one transmitter and one receiver (and so no collisions) and with4For this validation, the receiver used fragment and retry bits to distinguish

first and subsequent fragments. These bits may have been corrupted. Interest-ingly, despite uncertainty in these bits, the estimates are satisfactory.

Fig. 10. Hidden nodes, clean channel, no collisions. is loss rate for firstfragment transmissions, loss rate for second fragments (an estimate of), the error rate measured at the receiver for first fragments,

the rate for second fragments.

one hidden node, the offered load at the transmitter and hiddennode being 300 f/s. As before, measurements at the transmitterare validated against independent measurements taken at the re-ceiver. It can be seen that while the first fragment in a burst ex-periences a high error rate, the second fragment has a very lowerror rate. That is, as we expect, hidden node errors are limitedto the first fragment sent in a burst, while second fragments areprotected from these errors. It is interesting to observe that inthis experiment the channel characteristics were slowly varying,as can be seen from the peak in loss rate after around 30 s.Note that the transmitter and receiver estimators report dif-

ferent error rates. This can be explained as follows: While mea-surements indicate that the number of CRC errors measured atthe receiver is roughly the same as the number of retries mea-sured at the transmitter, the number of busy slots is measuredto be higher at the receiver because the hidden node’s transmis-sions can be heard at the receiver. This test also confirms the dif-ferent nature of the transmitter and receiver environments andthe need for a local estimator at the transmitter to take into ac-count the point-to-point link-quality characteristics.

D. Collisions and Hidden Nodes, No NoiseHaving validated the individual components of the estimator

in basic scenarios, we now consider more complex situationswith a mix of link impairments. In this section, we consider alink with both collision losses and hidden node interference. Inthe experiments, the offered load at all stations is 300 f/s.First, we again use a setup with a pair of stations that behave

as hidden nodes transmitting to one AP. Fig. 11(a) plots esti-mates of , , and locally measured on one of the hiddennode stations. It can be seen that is estimated at a high value,as expected due to the severe hidden node interference in thisexample. The noise loss rate is correctly estimated as beingclose to zero. The collision loss rate is correctly estimated ata value very close to that measured with two contending stationsand no noise or hidden nodes (marked as ( , )in the figure, with the value taken from the measurements inFig. 8). This is an encouraging result as it clearly demonstratesthe ability of the proposed estimation approach to effectivelydistinguish the different sources of frame loss, even under com-plex conditions.Fig. 11(b) plots similar measurements, but now with a pair of

stations that behave as hidden nodes plus one station that can


Fig. 11. Estimator values for , , and in the presence of collisions,hidden nodes and high SNR (low noise). (a) Hidden node and one transmit-ting station. (b) Hidden node and two transmitting stations.

be heard by all the other stations, for a total of three contendingstations with saturated traffic. Again, the noise loss rate iscorrectly estimated as being close to zero, and the collision lossrate is correctly estimated as being close to that with three sta-tions and no hidden nodes (marked on plot, with value takenfrom Fig. 8). The hidden node loss rate is estimated at a highvalue, albeit somewhat lower than in the previous example (60%versus 80%). This is caused by the third station transmissions,which are overheard by both hidden nodes, thus decreasing thenumber of hidden node transmissions and hence the hidden nodeprobability.

E. Collisions, Hidden Nodes, and NoiseFinally, we consider a link suffering from all three loss-in-

ducing impairments: collisions, noise, and hidden node interfer-ence. The scenario is illustrated in Fig. 12. We have three con-tending stations (stations 1, 2, and H), a pair of which behavesas hidden nodes (stations 1 and H), and with a noisy channelbetween station 1 and its receiving station. Each station sendssaturated traffic. Measurements gathered on station 1 are sum-marized in Fig. 13. It can be seen that the collision loss rateis estimated at a value very close to that measured with threecontending stations and no noise or hidden nodes (marked as“ ( , )” in the figure with the value taken fromthe measurements in Fig. 8). That is, the estimator is able tosuccessfully distinguish collision-related losses from noise andhidden-node-related losses. It can also be seen from the figurethat there is a high level of errors caused by noise and hidden

Fig. 12. Topology for hidden node with noise and contending stations.

Fig. 13. Estimation with collisions, noise, and hidden nodes.

node interference, with loss rates of approximately 65% and75%, respectively, providing a demanding test of our estimator.

F. Performance of RTS/CTS With Hidden NodesIn this paper, we use 802.11’s packet fragmentation func-

tionality to mitigate hidden node effects. Of course, it is morecommon to consider the use of the RTS/CTS handshaking forthis purpose and in principle the behavior should be similar.However, in practice we found a number of basic difficultieswith the use of RTS/CTS for this purpose.First, consider an experiment with seven stations transmit-

ting traffic at 300 f/s without noise and hidden node interfer-ence. In Fig. 14(a), we plot the probability of collision with andwithout RTS/CTS (labeled as and , respec-tively). The RTS/CTS collision probability is estimated from thenumber of missed CTS frames. To confirm the absence of noiseinterference, we have also plotted the overall probability of error(labeled ), which also takes into account the number ofmissed ACK over sent Data frame. Thus in this basic case, wesee that RTS/CTS reliably estimates the probability of collision.Now, consider a scenario with a hidden node. As a baseline,

we collect data when two transmitting stations are within oneanother’s carrier sense region. As expected, we see a low col-lision probability of around 7%; see Fig. 14(b) (line labeled

). Now, we move the transmitters so that they arehidden from one another. In the absence of RTS/CTS, we mea-sure a high error probability of around 82% (labeled ),which is mainly caused by hidden node errors. If we enableRTS/CTS, the error probability drops, but not to the expected


Fig. 14. Estimating with RTS/CTS under various conditions. (a) Without hidden nodes. (b) With a hidden node and time slot equal to 20 s.

value of 7%. Instead, we have a residual error of about 52%[line labeled in Fig. 14(b)]. That is, in presence of hiddennodes, the RTS/CTS estimator is still subject to considerablehidden node interference.In order to understand this behavior, we note that the hidden

node will defer its transmission if it overhears the CTS from thereceiver before sending its frame. We can calculate when thisoccurs. Our tests used an 802.11g PHY. Station 1 sends an RTSframe (duration 48 s), the receiver waits for a SIFS (duration16 s), and finally a CTS frame is sent by the receiver. Thus,the hidden station would need to leave the medium idle for atleast 64 s in order to receive the CTS frame. This is muchlonger than the PHY slot duration of 20 s for mixed mode11b/g. Indeed, if the backoff counter of the hidden node is lessthan 3 when the other station begins its RTS transmission, thenthe hidden node will make a transmission that corrupts the CTSframe.

VIII. ESTIMATING EXPOSED NODE AND CAPTURE EFFECTS

A. Exposed NodesAn exposed node is a sender station that senses the channel

to be busy when, in fact, the channel at the receiver is idle, andthus a successful transmission could have been made. A typicalscenario is illustrated in Fig. 4. Here, and send datato , while sends data to . Sender overhearsthe data transmissions by and and senses the channelto be busy. This is incorrect, however, since the physical separa-tion between and and means that transmissionsby would in fact be received correctly at even when

and are transmitting. therefore defers its backoffcountdown unnecessarily and its throughput suffers.We implemented the topology in Fig. 4 in our testbed.

and send 300 f/s traffic to access point , whileuses the same channel to send at a low rate of 20 f/s traffic to

, and station 300 f/s to . The channel is cleanwith no noise losses. In addition to measuring , , and asbefore, we now also measure the total number of MAC slotsand the number of slots that are detected idle. The value of

is a measure of the proportion of slots that the MACdetects to be busy via carrier sense. The collision probabilityprovides a measure of the proportion of slots that are actuallybusy (in the sense that a transmission in that MAC slot would

result in a collision). The difference between andthen provides a measure of how exposed a node is.Our measurements for this situation are shown in Fig. 15(a).

We show the collision probability estimated using our tech-nique and a fixed value measured without an exposed node (la-beled “ ”). It can be seen that these probabili-ties are low and close together. In this situation, measurementsindicate that senses the channel to be busy around 10% toooften, i.e., . This suggests that may freeze itsbackoff counter unnecessarily for 10% of MAC slots.Fig. 15(b)–(d) show the corresponding measurements as the

number of stations associated with is increased. It can beseen that, as expected, increases in line with measurementsin Fig. 8 without exposed nodes. The exposed node probability

is consistently measured as lying between 5% and 10%,although the relative impact of decreases as the number ofstations increases.To further explore our ability to sense exposed node effects,

recall that exposed node effects are intimately related to thechoice of carrier sense threshold used. In this scenario, thecarrier sense mechanism is too sensitive, and senses thechannel busy too often. This effect is illustrated in Fig. 16,which plots the estimated versus choice of carrier sensethreshold for in the setup of Fig. 4. As expected, it canbe seen that the exposed node probability has the highestvalue for carrier sense thresholds in the range 90 to 80 dBm.At around 75 dBm, the value of decreases as the impactof disappears (confirmed by inspection of packet traces).Finally, moving the carrier sense threshold up to 55 dBm, theeffect of also disappears, and is no longer exposed(again, confirmed by detailed packet traces). Also shown inFig. 16 is the measured collision probability . It can be seenthat this slightly increases as the carrier sense threshold isincreased, which is to be expected as the backoff countdown of

is becoming of shorter duration. The benefits of using asuitable carrier sense threshold are illustrated in Fig. 17, whichplots the ’s estimated MAC delay, which is the mean timebetween a packet arriving at the head of the interface queue andbeing successfully transmitted. The MAC delay is halved whenthe carrier sense threshold is increased to 55 dBm instead of85 dBm.A full carrier sense tuning algorithm would naturally be more

complex and is beyond the scope of the present paper. However,


Fig. 15. Collision and exposed node probability versus number of stations associated with . Network topology as in Fig. 5. (a) Three stations (two exposed).(b) Four stations (two exposed). (c) Five stations (two exposed). (d) Six stations (two exposed).

Fig. 16. Exposed node probability versus carrier sense threshold.

this example does demonstrate the value and feasibility of beingable to make this type of measurement.

B. Physical Layer Capture

Physical layer capture occurs when colliding transmissionshave different received signal power. It may then happen thatthe transmission with highest power is successfully decodedeven though it collides with another transmission. To assess theability of our estimator to measure this effect, we configured ourtestbed as shown in Fig. 5. Station sends data packets to

Fig. 17. MAC delay versus carrier sense threshold.

at a low rate of 20 f/s. In addition, we have four other con-tending stations transmitting data to at 300 f/s, but withlower received signal power than .Fig. 18(a) illustrates the impact of physical layer capture.

It can be seen that benefits from a lower-than-expectedprobability of collision. In particular, while with a total of fivecontending stations we expect a of around 19% (based onmeasurements without capture), the measured collision rate at

is only around 8%. The difference of 11% is a direct mea-sure of the capture effect advantage experienced by . Tohelp validate the accuracy of this measurement, we took thesame measurements with the carrier sense threshold increased


Fig. 18. Demonstrating capture effect estimation. Results are shown for twodifferent values of carrier sense threshold to confirm the absence of exposednode effects in these tests. Network setup is as in Fig. 5. (a) dBm.(b) dBm.

Fig. 19. Measurements of capture effect versus transmit power.

to 60 dBm—this change will not affect capture, but wouldeventually highlight the presence of exposed node effects in oursetup (see previous section). In Fig. 18(b), we find that the esti-mates of and are almost unchanged, confirming the ab-sence of exposed nodes in these tests.We now further explore our ability to measure the impact of

the capture effect. Note that decreasing the transmission powerat should reduce the capture effect. We confirm this ex-perimentally in Fig. 19, which presents measurements of and

versus the transmit power at . As expected, we see thatthe capture probability is greatest at the highest transmit

power of 20 dBm, and that decreases to zero as the transmitpower is reduced to 0 dBm. Observe that, as might be expected,

remains roughly constant as the transmit power isvaried, with a value around the expected probability of colli-sion for five saturated stations.Note that by reducing the transmit power a , we gain

multiple benefits: Power consumption is reduced, as is radio in-terference with adjacent WLANs, and the capture effect is re-moved, restoring fairness between contending stations. The ef-fect on fairness of tuning the transmit power can be analyzed inmore detail by looking at the probability of collision for eachnode in the network. We carried out tests with transmit-ting at a low rate of 20 f/s plus four other stations with satu-rated traffic. Table III summarizes the experimental measure-ments obtained. We see that decreasing the transmit power at

increases its the probability of collision. Meanwhile, theother nodes maintain a roughly constant collision probability, thus improving fairness in the network. Note that is not

identical at all stations due to remaining capture effects at sta-tions other than (power asymmetries arise due to antennatolerances, differences in physical location, etc.). Adjustment ofthe transmit power at all stations could restore fairness.

IX. CONCLUSION

In this paper, we propose a powerful MAC/PHY cross-layerapproach to understanding transmission opportunities in 802.11WLANs. Our approach provides separate quantitative measuresof the severity of each type of impairment for the transmission,which may be incorporated into routing metrics or other higherlayer statistics. Our estimator runs locally at the transmitter, andit can be used to minimize the effect of transmission impair-ments by tuning transmission rate, carrier sense, transmissionpower, and so on. We thus make available new measures thatwe expect to be of direct use for rate adaptation, channel allo-cation, etc. We explicitly demonstrate how the measurementscan be applied in carrier sense tuning and power control andshow possible benefits. Since we take advantage of the nativecharacteristics of the 802.11 protocol—without requiring anymodification to the standard or other stations—our approachis suited to implementation on commodity hardware, and wedemonstrate both a prototype implementation and experimentalmeasurements. As the approach can be applied at a single trans-mitter to improve performance, there is a clear path to incre-mental deployment.Our approach can be applied not only during run time at a

node to optimize and select the rate, channel, carrier sense andtransmission power, but also offline to verify the presence/ab-sence of impairments. This is a fundamental issue for networkoperators while troubleshooting and for researchers when estab-lishing the realism and status of test beds.

ACKNOWLEDGMENT

The authors gratefully acknowledge the help of R. Gass atIntel.

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Domenico Giustiniano received the M.Sc. degree in electronic engineeringfrom the University of Palermo, Palermo, Italy, in 2003, and the Ph.D. intelecommunication engineering from the University of Roma “Tor Vergata,”Rome, Italy, in March 2008.He spent 2007 as a visiting Ph.D. student with Hamilton Institute, Maynooth,

Ireland. He was a Post-Doctoral Researcher with the Internet Group of Tele-fonica Research, Barcelona, Spain, from 2008 to 2010. He is currently a Post-Doctoral Researcher with the Wireless Communication Group of Disney Re-search, Zurich, Switzerland. His interests lie in the area of wireless systems,including signal processing, channel quality, and localization.

David Malone received the B.A. (mod), M.Sc., and Ph.D. degrees in mathe-matics from Trinity College Dublin, Dublin, Ireland.During his time as a post-graduate, he became a member of the FreeBSD

development team. He is a Research Fellow with Hamilton Institute, NationalUniversity of Ireland Maynooth, Maynooth, Ireland. He is a coauthor ofIPv6 Network Administration (Sebastopol, CA: O’Reilly, 2005). His interestsinclude mathematics of networks, network measurement, IPv6, and systemsadministration.

Doug J. Leith received the Ph.D. degree in engineering from the University ofGlasgow, Glasgow, U.K., in 1989.In 2001, he moved to the National University of Ireland Maynooth,

Maynooth, Ireland, to assume the position of SFI Principal Investigator and toestablish the Hamilton Institute (www.hamilton.ie), of which he is Director.His current research interests include the analysis and design of networkcongestion control and distributed resource allocation in wireless networks.

Konstantina Papagiannaki (M’03) received the first degree in electricaland computer engineering from the National Technical University of Athens(NTUA), Athens, Greece, in October 1998, and the Ph.D. degree from theComputer Science Department, University College London (UCL), London,U.K., in March 2003She has been a Researcher with Intel Labs since January 2004: from 2004

until the end of 2006 in Cambridge, U.K., and since 2007 in Pittsburgh, PA.From the beginning of 2000 through 2003, she was a member of the IP Group atSprint Advanced Technology Labs, Burlingame, CA. She currently holds an ad-junct faculty position with the Computer Science Department, Carnegie MellonUniversity, Pittsburgh, PA.Dr. Papagiannaki received the ACM SIGCOMM Rising Star Award in 2008

and the Distinguished Dissertations Award 2003 from UCL.

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Measuring Transmission Opportunities in 802.11 Links