How Penalty Leads to Improvement:a Measurement Study of Wireless Backoff

Dmitriy Kuptsova,b, Boris Nechaeva,b, Andrey Lukyanenkob, Andrei Gurtova,c

aHIIT, FinlandbAalto University, Finland

cCenter for Wireless Communication, University of Oulu, Finland

Abstract

Despite much theoretical work, different modifications of backoff protocols in 802.11 networks lack empirical evidence demon-strating their real-life performance. To fill the gap we have set out to experiment with performance of exponential backoff byvarying its backoff factor. Despite the satisfactory results for throughput, we have witnessed poor fairness manifesting in severecapture effect. The design of standard backoff protocol allows already successful nodes to remain successful, giving little chance tothose nodes that failed to capture the channel in the beginning. With this at hand, we ask a conceptual question: Can one improvethe performance of wireless backoff by introducing a mechanism of self-penalty, when overly successful nodes are penalized withbig contention windows? Our real-life measurements using commodity hardware demonstrate that in many settings such mecha-nism not only allows to achieve better throughput, but also assures nearly perfect fairness. We further corroborate these results withsimulations and an analytical model. Finally, we present a backoff factor selection protocol which can be implemented in accesspoints to enable deployment of the penalty backoff protocol to consumer devices.

Keywords: Wireless networks, channel access, performance measurement and modeling, protocol design

1. Introduction

There are several well-known problems associated with thecommunication in wireless LANs (WLANs). In particular,since nodes in such networks use shared medium in an unli-censed radio spectrum for transmission of frames, collisionsare possible. Measurement study of large scale enterpriseWLANs [8] showed that nearly 15% of sender-receiver pairsexperience significant loss due to collisions. Another problemis fairness. The study in [11] showed that 802.11 networks havegood short-term fairness when the number of contending sta-tions is small, e.g. 2, and becomes worse for an increasingnumber of stations.

In this paper we tackle these two problems by proposingtwo novel backoff mechanisms for 802.11 networks. The pro-tocols, although different in design, have a similar inspirationand a common goal—to increase throughput and improve fair-ness. The underlying principle of both schemes is to penal-ize overly successful nodes with large contention windows andaccordingly reward unsuccessful nodes with small contentionwindows. We discover that unlike the greedy backoff schemeof standard 802.11 protocol, our protocols assure better fairnessand higher throughput.

To evaluate effectiveness of the proposed designs we, unlikemany other researchers [16, 22, 34], take a measurement ap-proach. Thus, to conduct the study we implement two existing

Email addresses: dmitriy.kuptsov@hiit.fi (Dmitriy Kuptsov),boris.nechaev@hiit.fi (Boris Nechaev), laser.ru@gmail.com(Andrey Lukyanenko), gurtov@hiit.fi (Andrei Gurtov)

and two novel backoff protocols on commodity hardware. Weconsider the details of implementation, data collection and cal-ibration techniques to be our second principle contribution.

Next, we conduct a series of real-life experiments in variousscenarios. The goal here is to explore how throughput and fair-ness of different protocols change when we vary such param-eters as backoff factor, number of clients and lossiness of thechannel. Our third principal contribution is in revealing per-formance trends and optimal configurations in real-life experi-ments, simulations and an analytic model.

To operate properly, the proposed algorithms require an ac-curate estimation of the number of active wireless stations at-tached to the access point. Our last contribution in this work isdesign and analysis of two metrics for estimating the numberof active clients. We implement these metrics in a Linux-basedwireless access point and evaluate the system performance inan office-like environment.

To preview our results, we have found that in many settingsthe penalty mechanisms improve fairness greatly. At the sametime, such improvement is also accompanied by considerableincrease in throughput. To be more specific, our experimentalwork indicated the following:

• In all the scenarios that did not include hidden termi-nals, we have witnessed that the backoff with penalty im-proves throughput by 100% when compared to the stan-dard 802.11 backoff.

• In many settings the penalty mechanisms allowed toachieve nearly perfect fairness. Even with RTS/CTS

mechanism disabled, two hidden terminals using backoffwith penalty achieved nearly perfect fairness under certainconditions.

• The penalty mechanisms reduced collision rate on averagefrom 0.3 to 0.14 in close proximity environment and from0.29 to 0.09 in sparse deployment.

• We have observed that the penalty mechanisms do not sig-nificantly increase the delays for UDP traffic, and preformmuch like standard backoff protocol.

The rest of the paper is organized as follows. In section Sec-tion 2 we review two existing and introduce two novel back-off protocols that will be studied in this paper. Section 3 dis-cusses the implementation details, experimental environment,data collection and calibration process. We devote Section 4 toour experimental findings and Section 5 to corroborating thesefindings with simulations. In Section 6 we outline the theoreti-cal analysis. Section 7 describes the protocol for estimating thenumber of active clients. In Sections 8 and Section 9 we discussthe implications of our findings and their correspondence withrelated work. We make concluding remarks in Section 10.

2. Backoff protocols

In this section we review the modifications of the backoffprotocols we have experimented with.

Standard backoff with varying backoff factors. This is theprotocol that is currently in use in all 802.11 standards. We usethis protocol as a benchmarking baseline and compare it withall other algorithms presented in this paper. The only modifica-tion of this protocol that we explore is varying backoff factorsr ∈ [1.2, 2.6]. We discuss exact changes in Section 3. Figure 1demonstrates how the standard backoff behaves upon succeed-ing (s) or failing (f ) to send a packet. In case of failure, e.g. dueto a collision, the protocol retransmits the packet and increasesi until it reaches i = 6. If all 7 retries fail, the packet is dis-carded and similarly to the result of a successful transmission,i is set to 0.

0 1 2 3 4 5 6

Figure 1: Standard backoff

Backoff with penalty. This is the first novel protocol we pro-pose in this paper. The idea behind it is to penalize the sta-tions that always successfully transmit frames by throwing themto a state with a larger contention window, and at the sametime allow the stations that are unsuccessful in transmissionsto have smaller contention windows. As seen in Figure 2, thisis achieved by putting the node which was in state 0 and had

a successful transmission into state 6. Such a modification in-creases the chances of each station to seize its portion of wire-less medium. Similarly to standard backoff, after 7 unsuccess-ful retries the mechanism retreats to state 0.

0 1 2 3 4 5 6

Figure 2: Backoff with penalty

Rollback backoff. In contrast to backoff with penalty, inour second proposed algorithm (rollback backoff) nodes alwaysstart with the state that has the largest contention window, andonly nodes that are unsuccessful are rewarded with smaller con-tention windows. The state transition diagram for this protocolis presented in Figure 3. Unlike the previous two protocols, 7failed retries set the state to 6.

0 1 2 3 4 5 6

Figure 3: Rollback backoff

Backoff with fixed contention window. This protocol is dif-ferent from all protocols described above in that it has a singlestate. In other words, the nodes have up to 7 retries (this iscommon for all protocols discussed in this paper) but after eachfailure or success the contention window remains unchanged.Though as we show later, optimal window size should dependon the number of active stations. Contention window sizes thatwe have used in our experimental work are identical to thosepresented in Table 2.

3. Implementation and data collection

In this section we describe the Linux implementation ofbackoff protocol for 802.11 networks, our experimental testbedand the way the data was collected and calibrated.

3.1. Implementation

Most, if not all, hardware manufacturers implement such lowlevel protocols as 802.11 backoff in firmware or in proprietarykernel modules. And typically, the firmware is shipped as abinary microcode that is loaded by OS drivers directly into de-vice memory. This significantly complicates any changes todefault protocol behavior. Fortunately, research community

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(a) Close proximity setting: nodes are localized in one place (b) Sparse deployment: nodes are scattered around the office

Figure 4: Experimental testbeds

and open source enthusiasts provide a great opportunity to ex-plore the internals of such software. In our implementation wehave used open source firmware [1] for Broadcom B43 wire-less cards as a basis for experimental work. The microcode iswritten in assembly language and features implementation ofstandard 802.11g Medium Access Control (MAC) mechanismsfor Broadcom/Airforce chipsets.

Without going into various peculiarities we will instead de-scribe what noticeable changes we have introduced to the firm-ware. The task was to modify the exponential backoff, as wellas random backoff, and the way it is calculated in firmware. Inoriginal firmware the contention window is calculated as fol-lows. Initial contention window CWmin, which is also theminimum one, is set to a default value of 31, and the maxi-mum is bounded with CWmax = 1023. Whenever the colli-sion is detected the client increases the window by a factor of2. The contention window is increased by shifting the valuein the register CUR CONTENTION WIN to the right. To ensurethat the value of current contention window will not exceedthe maximum allowed value 1023, bitwise AND operation withthe MAX CONTENTION WIN value is performed. We introducedseveral changes to the above scheme.

Non-standard backoff factors. Instead of using a fixedbackoff factor r = 2, we allow r ∈ R and experiment withthe values in [1.2, 1.3, . . . 2.6]. In our code CWmin is reducedfrom 31 to 15, since IEEE 802.11g uses the latter. We also re-moved CWmax = 1023, which was bounding the growth ofCW . Thus, in our implementation contention window changesaccording to CWi = CWmin ∗ri−1, where i ∈ [0, 6] is the re-transmission counter (i = 0 means that the packet is being sentfor the first time). For the experiments with fixed contentionwindow, we have set identical values to all 7 states. For exam-ple, for the experiments with 3 clients the contention windowfor all states was set to 21 [16].

Using floating point factors in original assembly code asabove would require the support for floating point multiplica-tion in hardware, which is of course missing. As a workaroundwe have done the following. First, using trial and error methodwe have found the offset in memory (note the available shared

memory in the devices is just 4KB) which was not used by anypart of the code. Second, for every r we have precomputed pos-sible contention window sizes and corresponding bit masks (wewill discuss the need for such bit masks later). The mask wascalculated as the minimum value 2x−1 which is larger than thecorresponding contention window.

Third, the client keeps track of backoff counter variablewhich is increased by 1 whenever the collision occurs or resetsit to 0 whenever the contention window returns to the minimumCWmin. Observe, that the actual minimum contention windowdoesn’t depend on r and is always equal to 15. Also, for r = 2our scheme is identical to the one used in original IEEE 802.11gprotocol.

Finally, we have changed the way random backoff value fromthe interval [0, CWi] is selected in the firmware. In the origi-nal version this was achieved using a bitwise AND operation ofthe pseudo-random number generated in SPR TSF Random reg-ister with the value of current contention window stored inCUR CONTENTION WIN. This allows to trim the 16-bit randomnumber to the number of bits in the current contention win-dow and thus obtain a random number in [0, CWmin ∗ 2i − 1].Though since in our case the values of contention windows arenot always powers of 2, the above approach wouldn’t work.In principle, it is possible to obtain the required random valuein [0, CWi] by taking (SPR TSF Random mod CWi). Sincedivision operation is absent in the microcode instruction set,we can subtract CWi from SPR TSF Random until the result isless than CWi. We implemented this algorithm, but found thatbig number of frequent subtraction operations significantly de-grades overall performance. Thus, we switched to a more ef-ficient solution. Given a CWi we do bitwise AND operationbetween SPR TSF Random and the precomputed mask equal tothe minimum value of 2x − 1 bigger than CWi. This givesus a random number H ∈ [0, 2x − 1]. If H ≤ CWi, thenwe have found the sought random backoff value. Otherwise, ifH ∈ (CWi, 2

x− 1], we repeat the above operations of taking anew 16-bit random number and applying bitwise AND with themask, until H ≤ CWi. Empirical evidence shows that the loopdoesn’t cause any noticeable performance issues.

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Non-standard state transition. To implement rollbackbackoff and backoff with penalty we have also changed theway the clients select backoff states whenever collision hap-pens. For instance, for the rollback backoff protocol instead ofstarting from state 0 and increasing the backoff counter by oneupon every collision, the clients start from state 6 (maximumcontention window) and decrement the counter on each retry.

For backoff with penalty we have also added logical check toa callback which is invoked after successful frame transmission.If the packet was successfully transmitted at the first attemptthen the state becomes 6, otherwise the state is set to 0.

3.2. Experimental environmentOur testbed comprised only commodity hardware. We have

selected inexpensive ($4 each) 802.11g wireless cards, 4 com-modity computers, a 100/10 Base-T switch and single wirelessaccess point running OpenWRT Linux, which also supported802.11g standard. The variants of our testbed are shown in Fig-ure 4.

We have dedicated a single computer to play the role of amaster node. This node was responsible for sending commandsto slave nodes to start experiments and also was participatingin receiving and sending test traffic from and to slave nodesover wireless interfaces. The other 3 machines were used asslave nodes. These nodes were provisioned with a single wiredconnection each and 5, 4 and 3 wireless cards correspondingly.

Since slave nodes had multiple interfaces, we configuredthese Linux boxes with policy based routing rules. This al-lowed us to send specific traffic through a specific interface.For instance, all control traffic such as commands and cali-bration packets (discussed below) was sent to wired interfaces,while wireless cards were performing experimental bulk trans-fers. Such setup allowed us to avoid interfering control trafficwith experimental wireless flows. Since the experiments wereconducted in an office-like environment, we have configuredthe wireless network with a channel that was least used andthus most probably not overlapping with other channels.

3.3. Data collection and calibrationOur experiments consisted of three traffic types: bulky TCP

upload and download streams, and delay sensitive UDP flows.We have generated the first traffic pattern with the transfers of64 MB files using Linux Wget utility. Here the master nodewas downloading the files from the slaves. Note that from theperspective of slave nodes (stations) this is the upload traffic.The second traffic pattern was generated using similar file sizes,but now the master node was uploading the file to the slavenodes using scp utility (hence we call it download traffic inSection 4.5). We could implement a simple custom applica-tion running on top of UDP to get more control over rate limit-ing and congestion control dynamics. Though we chose to useTCP, since it’s the most popular transport protocol for perform-ing large file transfers. We have used UDP in the experimentsinvolving measurement of delays. In particular we have usedUDP flows to emulate delay sensitive voice-over-IP sessions.We have implemented a custom application that was periodi-cally (every 10ms) sending 100 bytes packets from slave nodes

to the master node. Using a socket option the master node wasregistering timestamps of each received UDP frame. This al-lowed us to achieve the required precision in measurements.

We instructed the kernels on slave machines to log on perpacket basis the information about number of retries, acknowl-edgment flags, packet size, used contention window and back-off interval. In doing so we have encountered a problem withLinux kernel which did not allow us to log this information toofrequently. To overcome the issue we have recompiled the ker-nel with an increased ring buffer size for debug messages andalso increased kernel printk rate limit. Upon receiving packettransmission status notification from firmware, the kernel reg-isters the event and logs it into ring buffer. The ring buffer isperiodically (every 0.1 seconds) read and dumped into a file.Each event was also flagged with the wireless interface ID anda timestamp. For the latter we found it easier to use time elapsedsince machine boot up rather than time since the Unix epoch.

Upon inspecting our logs, we discovered that some of themstill contained few errors of two different types. First is trans-mission error reported by the wireless card driver. Unfortu-nately we failed to identify the cause for the error. Second isthe kernel debugging error mentioned above—apparently evenafter tweaking kernel parameters, it still didn’t manage to logall events and occasionally reported that some of them weresuppressed. We estimated share of these two errors relative tothe number of benign log events and found it to be negligible,well below 1%. Hence, we decided to ignore erroneous logmessages by discarding them.

In total we used 12 wireless cards installed on three slavenodes. For majority of experiments we have used 3, 6, 9 and12 concurrently active clients. We have balanced the usage insuch a way that for any number of active clients we have em-ployed all three slave nodes in our testbed. Our eventual goalwas to study combined performance of all active clients, whichrequired merging logs recorded on different machines. Sinceclocks on the machines were not in sync, we had to find a wayto correctly align our logs. The solution was to send calibratingbeacons from the master node to all slave machines via wiredinterfaces. In principle, it would have sufficed to send a singlebeacon in the beginning of each experiment, which slave nodeswould have recorded as a reference time frame. Then subtract-ing this value from each packet’s timestamp would yield a rel-ative packet’s timestamp in the merged log file. Alas, this so-lution is not perfect, since in prolonged experiments clock driftamong different machines could cripple relative packet timingsby putting some of them unduly further into the future or thepast. To eliminate the effect of clock drift we instructed themaster node to send beacons periodically, with an interval of10 msec. This made it possible to do re-alignment on shorttimescales.

Another task where we utilized beacons is splitting logs intobins. Binning allowed us to do fine-grained analysis of variousperformance metrics and trace evolution of different relevantparameters over time. We found it convenient to perform bin-ning by tying bin size to a number of consecutive beacons. Forinstance, 10 beacons would correspond to a 100 msec bin, 100beacons - to a 1 sec bin, etc.

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Figure 6: Difference in beacons inter-arrival times for different ma-chines.

Even though the beacons were sent with a strict 10 msec in-terval, there is no guarantee that they were recorded by slavenodes with exactly the same intervals. Perfect inter-arrival timebetween beacons could be skewed by various network, NIC orOS effects. Incorrect beacon inter-arrival times could result inimperfect binning and thus undermine any analysis that relieson the assumption of constant bin size. To assess the possibleskew in beacon timestamps, we calculated beacon inter-arrivaltimes for all logs. A typical distribution is shown in Figure 5. Toour relief, inter-arrival times turned out to be sharply clusteredaround 10 msec. Though the figure still shows rare outliers.This could lead to drift in cumulative beacon intervals amongseveral machines. However, Figure 6 eliminates this concern.To make the plot we calculated differences between respectivebeacon inter-arrival times on different machines. The facts thatthe distribution is centered at zero, highly clustered and sym-metric proves that bins calculated based on beacons are stayingequally sized in the long run.

After devising start and end timestamps of each bin, we tra-verse our logs and using all packets falling into it calculate num-ber of successfully transmitted packets, number of failures andthe total number of packets in the bin. The set of these threeparameters is also calculated for each client participating in theexperiment.

The last bit of calibration that we performed before mov-ing to data analysis is truncating our logs to all-active periods.Packet sending may have started at different times on differentclients. Also, some clients may have finished sending fasterthan the others. In our analysis we were interested in thoseperiods when all clients were active, since this would give usconfidence that all of them were involved in competing for thechannel. To identify such period, we calculated the maximumtimestamp of the first full-size (1540 bytes) packet among allclients and the minimum timestamp of the last one. Then, us-ing these two values we discarded all packets before and afterthem respectively. We made sure that the remaining periodswere big enough to provide meaningful data for our analysis.

4. Experimental results

Our research agenda is to observe behavior of all protocolsdescribed in Section 2 in various environments. The goal is

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Figure 7: Close proximity setting: Throughput

to reveal meaningful trends by experimenting with differentsettings and setups. In this section we present the results forthroughput (which is measured as an aggregated throughput ofall stations), fairness, collision probability and delays obtainedfor various values of backoff factor. It is worth noting, that inour study we do not differentiate between actual packet col-lisions and packets losses due to other factors (such as signalfading). Instead, both events collectively constitute collisions.

We begin with the environment in which nodes are close toaccess point (< 1m, Figure 4(a)). Next, we present results forthe settings where the nodes were deployed in the environmentwhich is typical to many office-like deployments of 802.11 net-works. This experiment is followed by the scenario with twohidden stations. In all three above mentioned experiments thenodes were uploading a large file to the server.

We conclude our empirical evaluation with experiments that

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Table 1: Close proximity setting: Median throughput (Mb/s) comparison

XXXXXXXXXXClientsProtocol

Penalty Rollback Std, r=1.2 Std, r=2.0 Std, r=2.6 Fixed CW

3 12.57(r=1.4) 11.16(r=1.4) 6.69 8.45 9.98 7.166 14.33(r=1.7) 9.16 (r=1.7) 3.99 5.75 7.98 5.289 13.86(r=1.8) 9.63 (r=1.8) 3.4 4.69 6.34 4.4612 14.09(r=2.0) 10.22(r=1.9) 2.9 4.8 6.34 4.11

involve two additional traffic patterns. The first pattern we con-sider is when all nodes were downloading a large file. Here ourintention was to observe whether the behavior of the protocolswill change in comparison to the experiment with upload traf-fic. For the second pattern we have emulated a mixed trafficpattern with the presence of low rate voice-over-IP (VoIP) likeflow and multiple (upload) TCP streams. The idea was to ob-serve the impact of bulky streams on less demanding, but delaysensitive flows. In this experiment we were mainly interested indelay characteristics and how would suggested protocols com-pare with the behavior of standard protocol.

But before we dive into the discussion of the results, we de-fine a metric we have used to measure fairness throughout thepaper. Intuitively, fairness is the ability of all transmitting sta-tions to share the channel bandwidth roughly equally in a givenperiod of time. To formalize the above notion, we have usedJain’s fairness index [17] with a sliding normalized window ofvarying sizes. In other words, if N is the number of stationsgenerating traffic, we have computed Jain’s index for windowsw ∈ {N, 2N, 3N, . . . , xN}. Here window size is expressed innumber of packets and the lower it is the shorter term fairnessis considered. More formally, if τj is the fraction of successfultransmissions for station j in window w, then the fairness indexis defined as:

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Thus F (w) = 1 is an indication of perfect fairness, andF (w) = 1/N is an indication of total unfairness.

4.1. Close proximity setting

We begin with the experiment in which nodes are placedclose to access point (< 1m, Figure 4(a)). Although this en-vironment can be thought as being idealized, we found thateven in such a simple setting we had several sources of inter-ference. For example, metal a rack located next to the test-bedwas most likely distorting the signal. This setting emulates reallife deployments in tight space areas, and therefore we find theresults obtained in it interesting and relevant. In the proceedingparagraphs we discuss the results for the following performancemetrics: throughput, fairness and collision probability.

The results for the experiments when rate control was en-abled are presented in Figure 7. For each backoff factor wecalculated median throughput across all throughput values for

each bin. The exception is the plot with fixed contention win-dow (Figure 7(c)) in which we plot the whole CDFs. We havealso calculated mean throughput values only to find that theyare close to median values shown in the plots, thus presentingthem here is redundant.

Furthermore, in Table 1 we highlight the above results usingmedian throughput for most interesting configurations. Notethat for penalty and rollback backoff protocols we use such val-ues of r which correspond to the maximum values of through-put. In other words, we use the peak values of r according tofigures. With this respect there are several interesting observa-tions that we can make.

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Figure 8: Close proximity setting: Fairness

First, for small values of r (e.g. 1.2) standard 802.11 back-off protocol does not show good throughput. For this setting,the fairness is comparable to that observed for standard back-off with r = 2.0 (Figure 8(d)). Second, for large values ofr (e.g. 2.6) the standard 802.11 backoff shows considerableimprovement in throughput, accompanied by a unduly bad fair-

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ness. As clearly seen in Figure 8(d), for r = 2.6 fairness isworse than for r = 2.0. In other words, larger backoff fac-tors allow fewer hosts to capture the channel, while other hostremain silent. And ff we put this in terms of user experience,it would probably mean that only few users can complete forexample download, while for rest of the users the TCP connec-tion will likely to time out (indeed, we observed such behaviorin some of our experiments). For this reason in the rest of thepaper we focus only on r = 2.0 for standard backoff protocolwhen comparing it with other protocols. Moreover, for standard802.11 backoff the throughput declines as the number of clientsincreases. In contrast, Table 1 shows that aggregate through-put for backoff with penalty and rollback backoff protocols (forN ∈ [3, 12]) doesn’t change significantly for different numberof stations. Finally, from the values presented in Table 1 wehave calculated the average improvement of rollback backoffover standard backoff (77%) and over backoff with fixed con-tention windows (96%). Similarly, the improvement for backoffwith penalty is 145% and 172% correspondingly.

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Figure 9: Close proximity setting: Collisions

For this setting we have also repeated the experiments whenMAC layer rate control was disabled. We observed that for allprotocols the median throughput was between 13 − 15Mb/s.On the other hand, fairness for all protocols was similar to thatwhen MAC layer rate control was enabled (we elaborate on fair-ness results in the proceeding paragraphs). However, we thinkthe results obtained for the settings with enabled MAC layerrate control have greater importance than the results with ratecontrol turned off. There is extensive evidence, both theoreti-cal and empirical, that different coding schemes deliver differ-ent bit error rates (BER) under the same signal-to-noise (SNR)

level. For example, packets transmitted at 1Mb/s can sustainhigher noise than packets transmitted at 54Mb/s. In real net-works SNR changes dynamically, which increases the role ofthe rate adaptation mechanism [37, 23]. For this reason in theremaining paragraphs we focus only on experiments with MAClayer rate control enabled. 1

Curiously, the increase in throughput for backoff withpenalty and rollback backoff does not harm fairness. The phe-nomena is best demonstrated in Figure 8(a) and Figure 8(b).Thus, we can observe that both protocols achieve nearly perfectshort-term fairness. On the other hand, backoff with fixed con-tention windows shows slightly worse results. We found thatour results are somewhat different from previous work foundin the literature. We see at least three reasons for the discrep-ancy. First, unlike other studies we do not rely solely on sim-ulations. Second, the CW values we use in our experimentalwork are theoretical (see Table 2), and thus, can be non-optimalin practice. Finally, it can be also that the backoff protocol withfixed contention windows does not reward the clients exposedto harsher environments (for example, in such settings the back-off with penalty can equalize the chances of successful channelaccess for all stations). Thus, in general we believe our resultsto be different due to real world effects absent in other papers.In contrast, the results for standard 802.11 backoff protocolshown in Figure 8(d) were less surprising. As it is expected,standard IEEE 802.11 protocol delivers worst performance interms of fairness.

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Finally, we turn our attention to packet collisions and theirimpact on system throughput. Since the MA layer can makeup to seven retries to deliver a packet, there are two ways tomeasure collision rate. First is to just calculate the numberof packets that required more than one retry, and second is tocount all the retries. Clearly, the first will yield smaller colli-sion rates, since some packets are delivered with more than oneretry. Here we report collision rates using the second approach,which explains why the numbers we obtained may seem higherthan those reported in related work.

Penalty and rollback backoff protocols (for optimal valuesof r) have sufficiently smaller fraction of collisions compared

1In all our experiments wireless stations were using minstrel – the de-fault rate control algorithm in Ubuntu distributions.

7

to backoff with fixed contention window and standard proto-col. As can be deduced from Figure 9 the median collision ratein optimal throughput points varies between 0.14 and 0.2 forbackoff with penalty and between 0.15 and 0.21 for rollbackbackoff. Similar ranges in Figures 9(c) and 9(d) are consider-ably larger: from 0.37− 0.4 and 0.3− 0.4 respectively. Lowercollision rate has two positive implications. First, it has a di-rect connection to increase in throughput; second, it improvesenergy efficiency of the protocols as stations spend less time intransmitting the same amount of information.

4.2. Validation with non-modified firmware

To ensure that the results we obtained for standard IEEE802.11 protocol in Figure 7(d) are valid, we performed an ad-ditional sanity check. We connected three off-the-shelf lap-tops2 (which had original hardware, proprietary firmware andnon-modified drivers) to an access point which was physicallyplaced 1 meter away (thus, the distance was similar to our ex-perimental setting). The access point was operating on an non-busy channel and itself did not contain any custom software.On the laptops we installed an identical HTTP server, whichwas ready to serve a 100 MB file. Lastly, using Wget util-ity we performed concurrent downloads from all three laptopsand measured the download completion times. We repeated theexperiment for 20 times to make the results statistically repre-sentative. From the collected data we were able to calculatethe average upload speeds which we present as a CDF in Fig-ure 10. We found that the median throughput in this experi-ment (8.69Mb/s) matches closely the results of our experimen-tal setup with three stations running the standard IEEE 802.11protocol. Therefore, we concluded that the observed through-put was normal for TCP flows over standard IEEE 802.11 withMAC layer rate control enabled and is not an artifact of ourexperimental setup.

4.3. Sparse deployment

The deployment we have dealt with so far in our experimen-tal work considers the nodes scattered around the access pointno further than one meter. Although this setting allowed usto test the utility of the proposed backoff protocols, to affirmourselves, we have conducted a set of additional experimentswhere nodes are placed away from each other by as much as30 meters. Such deployments appear in many home and officeenvironments. Therefore, the results we obtain in this setting al-low us to judge the applicability of the penalty-based protocolsin realistic settings. Note, in this experiment we had severalother operational wireless networks, and several networks withpartially overlapping channels. Schematically the deploymentlooks as shown in Figure 4(b).

There are several interesting observation we can make here.One such observation relates to throughput results. In general,

2In this experiment two laptops had a preinstalled unmodified Ubuntu dis-tribution and used minstrel for wireless rate control; the third laptop wasoperating under MAC OS and used a proprietary wireless rate control algo-rithm.

the curves we obtain for sparse environment resemble those wepresented in the previous section. This is best demonstrated byFigure 11. Similarly to the experiments in close proximity en-vironment, we have calculated the average improvement of thepenalty based protocols over the standard 802.11 backoff pro-tocol. The improvement for the rollback backoff was > 70%in comparison to standard backoff. The result for the backoffwith penalty was more modest: > 55%. Secondly, fairnessof backoff with penalty and rollback backoff was again muchbetter than in the other two protocols. Thus, in Figure 12 weshow fairness results for 12 stations only because they are al-most identical to those obtained in the close proximity setting.

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Figure 12: Sparse deployment: Fairness. 12 stations

4.4. Hidden stations

A hidden terminal is a serious problem for wireless networks.Briefly, it can be described as a scenario when two stations areplaced far enough (or have an obstacle in between) so that theycan’t detect electromagnetic radiation from each other. Clearly,this breaks down the operation of DCF protocol and as suchis a highly unwanted network condition. And it is exactly thereason why and how we have designed our next experiment.Regarding the goal of the experiment, here we wanted to ob-serve whether the penalty and reward mechanisms, built-in inour backoff protocols, can solve the problem without additionalmechanisms such as RTS/CTS.

To model such configuration we have placed two stations sothat they could detect signals from the access point only but notfrom each other. To confirm this, we have configured one hid-den station in the access point mode and tried to detect beaconsfrom it on the other hidden terminal. We repeated the test in re-verse direction to ensure that the property holds for the secondhidden terminal. Another desirable property of this setting wasthe difference in signal strengths produced by the two stations.Thus, it was our intention to have significant difference in sig-nal strengths for two stations connected to the access point. Toachieve this we have done the following: first, we have placedone station inside a metal rack (see Figure 4(a)); and second,we have wrapped antennas of both stations with metal foil.

Figure 14 illustrates fairness with Jain’s index w = 100.There is little to no fairness for standard backoff protocol,which happens due to a single station capturing the whole chan-nel, irrespectively of r. There is almost no fairness for rollback

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Figure 11: Sparse deployment: Throughput

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backoff when r ≤ 1.4, after which it starts growing for highervalues of r, and eventually reaches perfect fairness. For back-off with penalty the picture is similar. It is hard to simultane-ously achieve good throughput and fairness in this scenario. Wefurther discuss this issue in Section 8. However, both modifi-cations of the protocol show similar throughput results as thestandard protocol, which is good.

4.5. Download traffic pattern

In most wireless networks (home and public WI-FI, non-mesh networks) volume of download traffic is typically higherthan volume of upload traffic. For this reason, in this sectionwe investigate how the backoff protocols behave in a situationwhen download traffic pattern prevails. In this experiment the

access point was following standard backoff protocol and sta-tions were running both modified and non-modified versionsof the protocols. Our intention was to understand the effect ofdifferent backoff protocols (deployed on the stations) on TCPperformance. In contrast to upload scenario discussed in pre-vious sections, in this case data packets are sent by the accesspoint to the stations. Thus backoff protocols implemented onthe stations manifest themselves in control over sending TCPacknowledgment packets. These packets are typically muchsmaller than data packets and due to TCP’s ”ACK every sec-ond” policy are approximately twice as rare.

We present the results for throughput in Figure 15. Backoffwith penalty and reverse backoff do show the highest through-put for optimal r (in this experiment N = 7, therefor opti-mal r, according to Table 2, is 1.5 for rollback backoff and 1.7for backoff with penalty correspondingly). Although, these re-sults are comparable to those observed for the standard backoff(r = 2.0). To our surprise, even in such configuration backoffwith penalty and rollback backoff, according to Figure 16, bothachieve far superior fairness than the standard protocol.

This experiment clearly demonstrates the benefit of the twomodified protocols for the networks with prevailing downloadtraffic.

4.6. Delay sensitive trafficOur final experiment was designed to understand the impact

of our protocols on delay sensitive traffic under the presenceof bulky TCP flows. We started concurrent TCP streams on9 clients and at the same time a single low rate UDP flow onanother client. In this setting the UDP flow, limited to 80Kb/s,was emulating a VoIP-like call. Such rate is roughly equivalentto sending 100 byte packets every 10ms.

To generate UDP traffic we have implemented a simpleclient-server application in C language. The client was peri-odically sending UDP packets and the server timestamping thereceived packets using the socket option. While there were sev-eral sources of noise in the measurement, including clock skewon the server and imperfections of usleep(usec) function, thesewere rather insignificant so we ignored them (we have mea-sured these imperfections with synthetic tests to observe the er-ror).

In Figure 17 we show the results for mean delay of UDPpackets. We can observe here that results for all three proto-

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cols are pretty much identical and there is little deviation fromdesired delay of 10ms for small r. We can also notice that thedelays tend to increase as r gets large. We believe this is due tothe backoff intervals being undesirably large, which increasesthe overall waiting of the queued packets. For all protocols ex-cept standard, this can become a concern when the number ofstations attached to access point is large, e.g.. N > 20 (note, rgrows with N ). We should bear in mind however that for stan-dard backoff the situation will be even worse for large N : 1023slots – the maximum possible CW for standard backoff protocol– will be too small for large number of stations and collisions inthis case can cause more significant delays than the large CWin rollback and penalty backoff protocols for comparable N .Overall, we think that this problem can be tackled with bettercapacity planning.

5. Simulations

To corroborate the results obtained in our experiments, weperformed simulations of penalty and standard backoff proto-cols in NS-3 [2] simulation framework. We simulated two maindeployment modes described in Section 4: the close proxim-ity setting and the setting with hidden stations. Our simulationsetup included 12 stations attached to an access point, with eachstation performing TCP upload to a machine attached to a wirednetwork. Each simulation lasted for 600 seconds, during whichwe collected the necessary data. In our simulations all stationsused 802.11g technology with enabled minstrel rate controlmechanism, and RTS/CTS disabled. We also ensured that thequeues are large enough to prevent packet discards due to bufferoverflow, which we later confirmed by analyzing the collectednetwork traces.

The analysis of the data confirmed our empirical results. Forexample, Figure 21(a) and Figure 21(b) show throughput andfairness results for the simulations with rate control enabled.Clearly, the trend we saw in the real life experiments of closeproximity settings is visible here as well. To compare the col-lision rate, we measured the number of retransmitted packets.We found this figure obtained from simulations to be around13% and 4% for the standard and penalty backoff protocols re-spectively. These numbers are smaller than the collision rateswe saw in real experiments, which is certainly to be expected

since simulation provides an idealization of many real mech-anisms such as timers, queues, etc. Nevertheless, the overalltrend clearly persists.

We also repeated the experiment with two hidden stations.We configured the simulation parameters so as to ensure thatthe two uploading wireless stations don’t receive each other’ssignal, but can communicate with the access point. Here, we ex-perimented with penalty backoff with r = 1.2 and r = 1.7, andstandard backoff with r = 2.0. The results we obtain resemblethose we report in Section 4.4. Figure 21(c) demonstrates dis-tributions of throughput, with median throughput being clearlybetter for penalty backoff. Fairness index (with w = 50) forstandard backoff turned out to be 0.72, and 0.69 and 0.93 forpenalty backoff with r = 1.2 and r = 1.7 respectively. Thesesimulation results confirm the validity of our real-life experi-ments.

6. Analytic model

In this section we derive an analytic model describing be-havior of wireless networks with rollback and penalty backoffprotocols. We consider the model important for two reasons.First is to solidify and corroborate our empirical results—as wewill see later in this section the proposed model agrees wellwith our experimental findings. Second, the model can be usedto choose an optimal value of r for a given number of activestations N , importance of which is discussed in Section 8.

Relationship between expected contention window and r. Us-ing many lines of research [32, 4, 34] on analysis of 802.11networks as a starting point we find the expected value of con-tention window for rollback backoff protocol:

E[CW ] =(CWmin − 1)(1− pc)(pkc − rk)

2(1− pkc )(pc − r), (2)

where r is the backoff factor, pc is the probability of collision(to be defined in Equation 7), k = 7 is the maximum number ofretransmissions and CWmin = 16 is the minimum contentionwindow.

To model backoff with penalty we used an observation thataccording to Figure 2 the station, besides backoff process it-self, can be in two states. The first state (S0) is the state in

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Figure 21: Simulation results

which the station fails with probability pc to transmit at the firstattempt and thus follows the standard backoff protocol. Thesecond state (S1) corresponds to the case when the station suc-ceeds to transmit a frame at the first attempt with probability1 − pc and is thus forced to use the largest contention win-dow for transmission of the next frame. We can model thisprocess with a two-state Markov chain (Figure 22), which es-sentially encodes the states the backoff with penalty protocolcan be in when attempting to transmit a new frame (not to re-transmit a failed frame). The stationary probability distributionof the chain is {1/(2 − pc), (1 − pc)/(2 − pc)}. We use thisfact to further define Equation 3 which represents the expectedcontention window for backoff with penalty.

S0 S1 0

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Figure 22: Markov chain for backoff with penalty

E[CW ] =1

2− pc

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2

((pkc r

k − 1)

(pcr − 1)− rk−1

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The optimal expected contention windows. By defining theprobability of transmission attempt pt as:

pt = 2/(E[CW ] + 1) (4)

similarly to [10, 16] we can further define other probabilities:pn, ps, pc. The probability that none of N competing hosts willsend a packet pn is:

pn = (1− pt)N (5)

The probability ps that the host will successfully transmit apacket depends on the probability that the host will attempt to

Table 2: Optimal values of r for backoff with penalty and rollbackbackoff protocols

N CWfixed3 E[CW ] With penalty, ropt Rollback, ropt

2 12.4 14.9 1.18 1.113 21.3 27.3 1.35 1.254 30.1 40.1 1.45 1.315 38.9 55.2 1.53 1.386 47.6 71.2 1.65 1.457 56.4 88.8 1.67 1.58 65.1 107.8 1.73 1.559 73.8 128.5 1.78 1.65

10 82.87 150.8 1.85 1.6711 91.2 174.8 1.88 1.6912 100 200.5 1.95 1.75

send a packet and none of other N − 1 hosts will do so. It iseasy to see that such probability can be defined as follows:

ps =

(N

1

)pt(1− pt)(N−1) (6)

Lastly, the probability pc that at least two hosts will send apacket simultaneously, i.e. collision will occur, can be found bysubtracting both ps and pn from the total probability:

pc = 1− ps − pn =

N∑i=2

(N

i

)pit(1− pt)(N−i) (7)

With these at hand, throughput can be defined as a functionof probability pt:

F (pt) =E[Bytes]

E[Time]=

Spspsts + pctc + pntn

(8)

The values ts, tc and tn respectively represent: The time tosend a packet of length S

ts = DIFS + TX(K) + SIFS +RX(ACK)

3These values are identical to those that were used in prior work

11

The time to detect a collision

tc = DIFS + TX(K) + SIFS

The time during which the network is idle

tn = Slot duration

The function F (pt) is strictly concave in the interval pt ∈[0, 1], and thus by finding its first derivative and equating it tozero we can obtain the value of E[CW ] which yields the max-imum throughput:

Npt − 1

(1− pt)N=tn − tctc

(9)

Numerical results. To calculate the theoretical results we usets, tc and tn which correspond to 802.11g standard working atits maximum rate: ts = 3.22 · 10−4s, tc = 2.92 · 10−4s andtn = 9 · 10−6s. To compute ts and tc we have used packet sizeS = 1540 bytes. We have used Newton’s method to solve theEquation 9 numerically for several values of N . Next, we haveused the obtained values of E[CW ] and the similar method tofind the roots of Equation 2 and Equation 3. The computedresults for the first 12 N are shown in Table 2 and for the first50 N in Figure 18. These results represent the optimal valuesof r for the above values of ts, tc, tn and S.

Next, we compare the empirical results obtained in experi-ments with the model seeded with parameters of packet size, av-erage transmission time and time to detect a collision extractedfrom the empirical results. To obtain an adequate comparisonof the empirical results with the model one needs to have a goodestimate of the mean packet size and the time to detect a colli-sion tc. Observe that tc itself depends on both the packet sizeand the rate used to transmit the packet. First, we have used ourdata sets to compute the mean packet sizes. It appeared that forall experiments involving differentN and r this mean was closeto 1000 bytes. Second, across all the data we have calculatedthe average rate, and using 1000 bytes as the packets size wehave found that the average tc was close to 366µs for backoffwith penalty and 345µs for rollback backoff. And finally, usingthese values as parameters for the model, we have computed thenumerical results and compared those against the experimentaldata (for optimal r). The results of the comparison can be seenin Figure 19 and Figure 20. The empirical curves strongly re-semble the theoretical curves, especially for the case of backoffwith penalty.

7. Backoff factor selection protocol

In this section we present the design, implementation andevaluation of the protocol which allows access points to esti-mate the number of active wireless stations to adapt the backofffactors accordingly in a dynamic way.

Algorithms. We chose two different ways to calculate thenumber of active wireless stations within the access point’svicinity. The first approach is a simple threshold-based adap-tion algorithm. In this algorithm access point counts as active

only those associated stations that were transmitting at least τunits of time within the update interval T .

Nactive =∑∀i

I(τi ≥ τ)

where I(·) is an indicator function mapping logical expressionto 1 if it is true, and 0 otherwise and τi is the fraction of timea station i was using channel during interval T . This algorithmis really easy to implement yet it has its drawbacks: possibleerrors in estimations if selected τ is too small or too large.

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The second algorithm is ratio-based estimation of number ofactive stations. Similarly to previous algorithm, here we let τito be the fraction of time during which the station i was occu-pying the channel within the interval T and define x =

∑∀i τi

Nassoc

to be the fair share channel access time for all associated sta-tions. With this information at hand, we formally can define thenumber of active stations as follows

Nactive =∑∀i

I(τi ≥ x) + b∑∀j τjI(τj < x)

xc

where I(·) is an indicator function. Because this algorithm canbe prone to frequent oscillations in estimates of N (e.g. if xis taken exactly equal to fair share), we introduce a coefficientε ∈ (0, 1] and instead of x we use ε · x (we use ε = 0.8 in ourexperiments). This allows the access point not to update thestations with new N if the difference between between channeluse is marginal. Finally, we note that to calculate τi for eachstation, an access point for every packet received from user istores the size of the frame (Pi,k) and the bit rate (Ri,k) used totransmit kth frame. And when time T elapses for each wirelessstation access point recalculates τi =

∑∀k Pi,k/Ri,k.

Implementation. We implemented both algorithms inhostapd damon - a piece of user-space software available inLinux that implements wireless access point functionality. Wealso introduced a new 802.11 management frame which the ac-cess point broadcasts every time it finds new value for the num-ber of active stations. In turn, wireless clients use the value (N )conveyed in this frame to select proper current backoff factor.

12

Table 3: Comparative analysis of backoff protocols

XXXXXXXXXXPropertyProtocol

Standard Fixed CW Rollback With penalty

Short-term fairness Poor Close to perfect Nearly perfect Nearly perfectThroughput Medium Medium High High

Hidden stations (fairness) Little - good for r ≥ 1.6 good for r ≥ 1.6Energy efficiency/Collisions Low/High Low/High High/Low High/Low

Finally, we also modified the b43 Linux kernel driver for Broad-com cards which we used in our experimental work (describedin previous sections) to enable dynamical adaptation of backofffactors on wireless cards.

Evaluation. To evaluate the performance of the proposedalgorithms we use the deployment in which wireless stationswhere scattered around the office (see Figure 4(b)). The idea ofthe experiment is to vary the number of stations transmitting si-multaneously and let the access point specify the backoff factorwhich the wireless stations need to use. In this setting we useall 12 stations. Thus, we instruct 6 stations to transmit a 40KBUDP stream every other 30 seconds during 30 seconds inter-val. The other 6 stations transmit 64MB file using TCP proto-col. We repeat the experiment 20 times for each of the follow-ing settings: (i) all stations and access point where configuredwith standard backoff protocol, (ii) all stations configured withpenalty backoff and access point selects backoff factor usingthreshold-based algorithm, and (iii) all stations configured withpenalty backoff and access point selects backoff factors usingratio-based algorithm. We show the distribution of experimentdurations for all settings in Figure 23. Clearly, the ratio-basedalgorithm shows better performance than threshold-based al-gorithm and far better than standard backoff protocol which isexpected.

8. Discussion

In this section we summarize the key observations we havemade during our experimental work. These observations arehighlighted in Table 3 and explained in the proceeding para-graphs.

Does the penalty mechanism work: short-term fairness as anindicator. It is a well-known fact that 802.11 standard backoffprotocol lacks short-term fairness: nodes in a disadvantageousposition fail to compete for the channel as they advance theircontention window and hence decrease own odds for occupy-ing the shared medium. Using this fact as a starting point wehave introduced two modifications (which have game theoreticroots) to the standard backoff protocol: a penalty and incen-tive mechanisms. Two have a common goal – improve fair-ness – but tackle it in different ways. In the first one, success-ful nodes are penalized with larger contention windows to givea possibility for unsuccessful nodes to compete for the chan-nel. Inversely, in the second protocol, unsuccessful nodes arerewarded with smaller contention windows. Our experiments

demonstrate that these two changes can improve fairness of802.11 networks greatly. On the other hand, unlike other stud-ies [14, 16], our empirical evidence indicates that simply usingbackoff with fixed contention windows does not guarantee goodshort-term fairness.

Improved throughput. In many settings we have tried, stan-dard 802.11 protocol does achieve decent throughput. However,such performance is stipulated by capturing the channel by asmall fraction of stations. In other words, to some extent, theincrease in throughput (when 802.11 standard backoff is used)is a result of decrease in fairness.

On the other hand, when backoff factor r is selected properly,backoff with penalty shows amazing results due to drasticallydecreased collisions. In particular, even in lossy environmentthe average improvement reached almost 100% in comparisonto standard 802.11 protocol. These results are closely followedby the results of rollback backoff protocol. And unlike stan-dard backoff protocol, such improvement was not penalizedwith lack of fairness.

Impact on energy consumption. The literature devoted to theproblems of energy efficiency in wireless networks is so exten-sive that we outline few out of many techniques. First, one wayto reduce energy consumption by wireless devices is to accu-rately schedule data transmission and sleeping intervals [36].The other body of work discusses techniques that allow to re-duce the overall amount of packets, e.g. using compression andpacket aggregation [3, 27].

Although not specifically designed for being energy efficient,the two protocols proposed in this paper implicitly achieve thesimilar goal. We have witnessed that for instance backoff withpenalty reduces the total number of collisions from 30% to14%. As a result the overall number of lost packets is also re-duced (bear in mind that in 802.11 there are 7 retries beforethe frame is discarded). Although we do not have a quanti-tative estimation of the amount of saved energy, and have notexplored the relationship between collisions and energy con-sumption, we believe that the proposed protocols have a goodpotential for energy saving.

Can we deal with hidden terminals? Hidden terminals is awell understood problem in wireless networks. And as we havedemonstrated in Section 4.4, standard 802.11 backoff protocollacks fairness in such settings (at least when the number of sta-tions is 2). Potentially, such mechanism as RTS/CTS allows toavoid the problem but at the cost of extra overhead.

Our goal, on the other head, was to understand whether

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penalty and reward mechanisms are sufficient to tackle the hid-den terminal problem. We have found that even though forr ≤ 1.5 backoff with penalty shows little fairness, already forr > 1.5 the protocol passes the threshold level of 0.7, which canbe considered as good fairness, good enough to give all nodespossibility to transmit packets.

Poor fairness of the protocol with penalty when r is smallcan be a result of almost equal values of expected backoff timeand time needed to successfully send a packet. This can alsomean that it is very likely that one of the stations will start send-ing a packet while the other one still hasn’t finished doing so.Of the two colliding packets the access point will receive theone sent by the station with higher transmitting power, result-ing in the other station consistently having a smaller fractionof successfully transmitted packets. Ideally, for the scheme towork time needed to transmit a packet should be considerablysmaller than the expected backoff time. Indeed, we have wit-nessed that backoff with penalty achieves good fairness alreadywhen r = 1.6 (which can be selected as minimum for real de-ployments as nodes will not loose much in terms of throughput).

Adapting the backoff factor and estimation of active stations.In standard 802.11 backoff protocol throughput grows with theincrease of r (which is however severely penalized with reducedfairness) and the trend holds for all N we have experimentedwith. Though as our results suggest, relation between through-put and r is different for backoff with penalty protocol, wherean optimal value of r has to be selected for eachN . Indeed, set-ting r too large whenN is small will result in a large number ofwasted slots; choosing a too small r when N is large – in largenumber of collisions. In Section 6 we have demonstrated that rshould be a function of N .

In practice, however, counting the number of active stationsin a distributed way can be a challenging task. For instance, inIdle Sense access protocol [16] the authors suggested to countthe number of idle slots between any two transmissions. Andit is these idle slots that are used as an indication to increaseor decrease the window size. There is, however, little experi-mental evidence that this approach will work well in real-worlddeployments. For example, when hidden terminals are present,the nodes can easily miscount the number of ”occupied” slots.As an alternative, the nodes can use the information providedor relayed by an access point. In this work, we demonstratehow access points can estimate the number of active stationsand broadcast this information to attached nodes. In a meshnetwork deployment there has to be some other distributed al-gorithm to find the number of active neighbors in the vicinity ofa particular node. Unfortunately, we are unaware of any priorwork that considered estimating number of active stations inmesh and ad-hoc wireless networks.

Deployment. We foresee at least two possible deploymentpaths. First, our protocol can be deployed on the nodes and co-exist in a way similar to 802.11b and 802.11g protocols: If allassociated stations support modified version of backoff proto-col, then they all use it. Otherwise, if at least one legacy host en-ters the network, all nodes fallback to unmodified backoff pro-tocol. Second, the protocols we discuss can be readily deployedin such networks where all nodes are guaranteed to follow it.

For instance, in mesh networks backbone nodes can use modi-fied backoff protocol to communicate between each other, anduse unmodified backoff for communication with legacy clients(obviously using a different wireless channel).

A tussle with greedy and unfair: can penalty mechanismscoexist with greedy protocols such as standard 802.11? An in-teresting situation arises when, for instance, the backoff withpenalty is deployed in the environment where greedy protocolsprevail. Intuitively, the more greedy standard backoff can easilycapture the channel, because it doesn’t feature self-punishmentfor successful transmissions. On the other hand, the nodes thatfollow backoff with penalty will remain fair between each other,but will inadequately self-penalize and as a result will havesmaller throughput.

We think that the study of this tussle problem deserves acloser look and can be formulated separately. But to outline,we consider it possible to find a protocol from the family ofpenalty-based backoffs which potentially can tackle the prob-lem. We leave this for future work.

9. Related work

The literature on wireless communication is so extensive thatin the proceeding paragraphs we can only summarize a few ofthe many lines of research related to what we discuss in thispaper.

Modeling and simulation of IEEE 802.11 protocol. Thisbody of work is arguably the largest and mainly focuses on ana-lyzing and modeling the performance of standard 802.11 proto-col. Thorough review of the key results from just most notablepapers would require a book on its own, therefore, we merelymention such work in this area. For example, the following pa-pers: [4, 11, 32, 34, 21, 28, 29, 9, 25, 12] can be considered asa good starting point for deeper understanding of performanceissues of 802.11 wireless networks. Whereas, we use theoret-ical achievements found in this literature as the basis for ourown analytical models. However, our study different from theprior works in at least two ways. First, we do not analyze stan-dard IEEE 802.11 backoff protocol as such, and instead we usemathematical tools found in the papers to investigate our owndesigns. Second, we do not rely on simulations, but observethe behavior of protocols in deployments with real devices andenvironments.

Optimizations not related to backoff protocol. The literaturein this area is rife with various approaches and here we outlinejust two research directions. For example, recently researchersfocused on efficient network coding schemes to reduce colli-sions and increase overall system throughput [15, 13, 19, 18,20]. Other researchers explore the possibility to utilize radiospectrum more efficiently. Examples are designs that use mul-tiple input-output antennas [24, 26]. Although these mecha-nisms are orthogonal to our approach, they illustrate one way ofcoping with collisions in wireless networks. We consider theseapproaches to be supplementary to our work, i.e. can coexist.

Modifications to backoff protocol. There exists a consid-erable number of work that attempt to improve throughput

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and fairness of wireless networks with non-standard backoffschemes. For example, the work described in [31, 30, 16, 22]uses non-standard contention windows. Unlike these studies,which suggest to either remove the exponential backoff com-pletely and use fixed contention windows or non-standard back-off factors, we take a radical step and propose to use non-standard state transitions to penalize certain stations. The workin [7] is the closest to ours. Similarly to us, the authors suggestto use non-standard state transitions and penalize too success-ful stations to improve fairness and throughput. However, thereare several key differences the approaches. First, unlike ourwork, in [7] the authors use only two states and hence two con-tention windows, meaning that the backoff protocols are ratherdifferent. Second, in [7] the authors apply their design to mul-tipacket reception wireless networks; whereas in our designs,although we experiment with the clients using a single channelfor transmitting and receiving packets, we do not limit ourselvesto any underlying physical layer technology. Finally, althoughthe conclusions we derive are similar, in our work we make ourjudgment based on empirical study and do not rely on simula-tions.

Estimating number of active stations. There is a numberof proposals on estimating number of active stations in 802.11wireless networks. Bianchi et.al. [5] were the first to proposeusing busy slots to achieve this goal. Cali et.al. [6] investigatethis direction further, and derive a metric which estimates thenumber of active stations based on the observed number of idleslots. Even though these metrics can be readily used in ourdesign, their accuracy in case of uneven demands for channelresources is an open question. Neither it is clear how well thesemetrics will perform in real-world deployments with hidden ter-minals. The other body of work considers using a centralizedcontroller [35] in enterprise wireless networks which collectsthe information about used channel time and available band-width. This information can be used to tune wireless networkparameters. These approaches do not bear into our situationdirectly, but can be used, for example, in large scale deploy-ments of our designs. Finally, we are aware of only one priorwork [14] where the authors investigate the techniques to es-timate number of active stations empirically. Thus, our workoffers a considerable step towards better understanding

Experimental work. We find it surprising that there exists lit-tle empirical investigation of modified backoff protocols. Assuch, we have found that only in [14] the authors reported somepractical implementation and evaluation of backoff protocol us-ing proprietary hardware and firmware and a small number ofwireless stations. And few research works [33] consider imple-mentation of MAC protocols in general on commodity hard-ware. Thus, our work is another step towards better under-standing of real-life performance of modified MAC protocolson commodity hardware.

10. Conclusion

The main goal of this paper is to evaluate performance ofvarious 802.11 backoff protocols in real-life experiments. Thefour protocols we considered were standard backoff, backoff

with fixed contention window, backoff with penalty and roll-back backoff. The first step in our work was to implement theprotocols on suitable devices and design the experimental setup.We described in details the difficulties we had encountered dur-ing implementation of backoff protocols on commodity wire-less cards, since we believe that these details may help otherresearchers who will be experimenting with the same or similardevices.

We put considerable effort in designing our experiments andmaking sure that our data is consistent. Our study includes sev-eral scenarios: in lossy and normal environment, with uploadand download traffic pattern, with hidden stations and delaysensitive applications, with TCP and UDP flows. In the experi-ments we observed the consequences of varying backoff factorfrom 1.2 to 2.6 and number of stations from 3 to 12. The col-lected data was carefully scrutinized and calibrated to avoid anyerrors.

The three main performance metrics we have monitored wereaggregated throughput, short-term fairness and collision proba-bility. In our experiments standard 802.11 backoff protocol andbackoff with fixed contention window demonstrated poor fair-ness. We have concluded that unfair behavior is built-in to thestandard 802.11 backoff protocol, where unsuccessful stationsare forced to remain unsuccessful, while successful stations areable capture the shared medium for long periods and remainsuccessful.

To mitigate the problem with fairness, we have introducedself-penalty mechanism in backoff with penalty and rollbackbackoff protocols. Our empirical evidence, simulation resultsand an analytic model reveal that these two protocols achievenearly perfect fairness and throughput up to 100% higher thanin standard backoff. In addition, the penalty mechanism al-lowed to decrease collision rate by more than half and to reducedelays in delay sensitive traffic.

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