WhiteRate: A Context-Aware Approach toWireless Rate Adaptation

Veljko Pejovic and Elizabeth M. Belding

Abstract —The increased demand for wireless connectivity emphasizes the necessity of efficient wireless communication as resourcessuch as the available spectrum and energy reserves become limiting factors for network proliferation. Recent advancements in software-defined radio enable high flexibility of the physical layer allowing fine grained transmission adjustments. Although communicationefficiency can greatly benefit from physical layer flexibility, modern wireless protocols can neither handle these new opportunitiesnor allocate resources according to the overlying application needs. In this work we develop WhiteRate, a method for physicallayer parameter adaptation that efficiently utilizes available energy and spectrum resources, while maintaining the desired qualityof communication. Our solution adjusts the modulation and coding scheme, and channel width to achieve a communication profile thatmatches application requirements. We implement WhiteRate in GNUradio and evaluate it in both indoor and outdoor environments.We demonstrate improvements on two important fronts: spectrum utilization and energy efficiency. Moreover, we show that by usingWhiteRate, both benefits can be achieved simultaneously.

Index Terms —Wireless communication, network protocols, context awareness, energy efficiency, software-defined radio.

✦

1 INTRODUCTION

W IRELESS connectivity is no longer limited to homesand offices of the western world; it is now

reaching more than a half of the planet’s population,distributed across very diverse environments. In 2010over one billion mobile phone handsets were shippedworldwide, 18% more than the previous year, and thepredictions are that the number will continue to in-crease [4]. The rise is reflected in the wireless broadbandtrend, which currently grows five times faster than fixedconnectivity [2]. The wireless revolution is happeningworldwide, and the number of Internet users in thedeveloping world has already surpassed the number ofusers in the developed world. The changes in wirelessusage are not only quantitative, but also qualitative, asthe demand pushes service provisioning to situationsthat were unforeseen when today’s wireless systemswere designed. Always-online mobile computing, con-nectivity in remote rural areas and high quality voiceand video communication are becoming common users’expectations.However, higher proliferation of wireless communica-

tion is hampered by the lack of physical resources: wire-less spectrum and electrical energy. Spectrum scarcityis increasingly evident in highly populated areas wherethe sheer density of mobile wireless devices causes de-teriorating quality of service. In another example, whenbrought to rural areas that are often characterized by

• Veljko Pejovic is affiliated with the School of Computer Science, Universityof Birmingham, UK. The work presented in this paper was done while hewas a PhD candidate at the Computer Science Department, University ofCalifornia, Santa Barbara. Elizabeth Belding is affiliated with the ComputerScience Department, University of California, Santa Barbara.E-mail: v.pejovic@cs.bham.ac.uk

erratic power supply, traditional power-hungry WiFiequipment fails to deliver reliable connectivity.While these problems are usually attributed to low in-

frastructure scalability, we argue that inefficient resourceallocation significantly contributes to poor performance.As wireless connectivity spreads into new environments,we increasingly observe a wide variety of resourceavailability and user needs. Modern wireless protocolsare unable to adapt in this large gamut of operatingsituations for two reasons: first, they can only adjust arestricted number of communication parameters over afixed physical layer and, second, they react to changesin the wireless environment without considering thecontext in which the communication takes place.To break out of the limitations of the fixed physical

layer, we use software defined radio (SDR). We embracean OFDM-based flexible physical layer, similar to theone presented in [32], which allows tight control overchannel width, and modulation and coding scheme. Weexperimentally evaluate the potential of such a systemfor energy and spectrum efficient wireless operation. Wethen develop WhiteRate, a context-aware protocol thatharnesses the physical layer flexibility to fulfill users’communication expectations in the most resource effi-cient way. WhiteRate modifies channel width and modu-lation and coding scheme (MCS) according to the packeterror rate (PER) threshold imposed by the upper protocollayers. The key observation that allows WhiteRate tooperate with very little communication overhead is thefact that PER can be used to guide both energy efficiencyand spectrum utilization, as well as to directly answer tothe application needs. Thus, WhiteRate remains highlypractical and, in terms of implementation complexity,comparable to standard rate adaptation protocols.We implement WhiteRate in GNUradio, a software-

2

defined radio framework, and USRP2 hardware andexperimentally evaluate the protocol in both indoor andoutdoor environments. Our decision to use two differenttestbeds is motivated by the foreseen applications ofWhiteRate. In one case, we see WhiteRate as a key toolfor alleviating network congestion in urban areas wherethe growing appetite of mobile devices surpasses theinfrastructure growth [3]. In our lab testbed, therefore,we investigate the communication quality under Whit-eRate as the available spectrum and the number of activeclients varies. On the other hand, we envision WhiteRateas an integral part of next-generation rural area networksoperating in white spaces. These networks are expectedto provide connectivity to remote regions where evenbasic voice and TV service is unavailable and whereenergy efficiency is of key importance. Thus, we evaluateWhiteRate’s potential for energy savings in a long dis-tance outdoor wireless testbed in Pretoria, South Africa.We show that WhiteRate operates along the tradeoff

line at which the energy savings are balanced against thegiven application loss tolerance. We compare WhiteRatewith a widely used rate adaptation solution and, wherepossible, intuitive alternatives. Through the experimentswe demonstrate that by using WhiteRate we can supportmore clients without sacrificing end user applicationperformance perception. We also show that, comparedto application-agnostic solutions, WhiteRate saves upto a half of the transmission energy per bit. Finally,WhiteRate allows on-the-fly prioritization of resourceutilization efficiency or packet delivery reliability andadaptability to varying spectrum availability.The contributions of this paper are the following:

• Theoretical and practical evaluation of the flexiblephysical layer potential for context-aware operation.

• Development and full software defined radio im-plementation of WhiteRate, a channel width andMCS adaptation protocol that maximizes energyand spectrum efficiency while satisfying the appli-cation performance requirements.

• A comprehensive evaluation of WhiteRate’s per-formance in terms of achieved application quality,energy and spectrum efficiency with static and dy-namic packet delivery requirements and spectrumavailability, and in both indoor and a long-distanceoutdoor testbed.

In this paper we build on our previous work [19],however, we revise the original work and extend it on anumber of important fronts. Specifically:

• We implement a fully functioning version of Whit-eRate that works in real time1. On the contrary,in the earlier version we ran the algorithm in asimulator over a wireless link trace.

• In our previous testbed we had a 500m long line-of-sight link. For this work we deployed a 3 km longoutdoor non line-of-sight white space link between

1. WhiteRate code is freely available athttps://github.com/vpejovic/whiterate

SDR nodes. This represents a more realistic rural-area network setup and is, to the best of our knowl-edge, the first such link reported in the literature.

• We analyze packet delivery performance with dif-ferent channel widths and MCSs over a frequencyselective long distance link in our testbed.

• We conduct a full experimental evaluation of Whit-eRate, encompassing all aspects measured in ourprevious paper, and augment it with examinationof dynamic protocol behavior.

2 RELATED WORK

Physical parameter adaptation to changing wireless con-ditions has been extensively researched and a numberof bit rate adaptation protocols have been proposed forWiFi networks [5], [7], [31]. In addition, work has beendone on adjusting the transmission power to achieve themost energy efficient operation [33], and even adjustingthe channel width of the transmission [10]. The maingoal of these algorithms is high throughput. In our workwe concentrate on multiple, possibly conflicting goals.For example, when energy efficiency is important, wesacrifice some of the delivery reliability, if we are stillable to satisfy the application performance requirements.Since traditional WiFi cards offer little flexibility when

it comes to rate adaptation, more flexible software de-fined radio platforms have been considered in recent rateadaptation work. Thus, AccuRate [26] extracts symboldistortion levels at the receiver to guide modulationadaptation, while SoftRate uses decoder confidence lev-els for the same purpose [30]. Strider [12] introducesrateless coding, where a linear combination of symbolsis transmitted, effectively increasing the distance be-tween constellation symbols until the throughput opti-mal transmission is achieved. In FARA [22] transmissionbitrate is adjusted so that each of the OFDM subcarriersgets assigned a modulation level that corresponds toSNR observed on that particular subcarrier. While theseschemes allow a high level of sophistication, they exhibitcomplexity which prevents easy implementation. SNRbased adaptation requires in-situ training and receivertriggered rate selection. A large amount of feedbackfrom the receiver needs significant time and inefficientlyutilizes communication bandwidth. Our protocol, Whit-eRate, builds upon WiFi-based solutions driven by abinary ”packet delivered or not” outcome [7]. Thus, oursolution remains highly practical and easy to implementdespite the fine grained adaptation it provides.Wireless networks are deployed in a variety of sce-

narios: personal and mobile communication, monitoring,disaster relief, to name a few. As a consequence, context,or circumstances that surround the communication, canbe considered in order to better match wireless trans-mission with the end user needs. Context-aware wirelessrate adaptation was proposed in CARS [27], a vehicularnetwork rate adaptation protocol that accounts for vehi-cle speed and distance from its neighbours. ASTRA [15]

3

Fig. 1. Spectrum analyzer output for a single linkoccupying non-adjacent OFDM subcarriers . In thisexample nine groups of subcarriers are active, while twoare inactive. We activate groups in descending order ofthe observed channel gain, so that narrower channelsalways encompass “stronger” groups and perform betterthan wider channels.

relies on machine learning techniques in order to identifythe relationship between the context, which is in ASTRAdefined by the channel type, SNR and node velocity,and wireless parameters. Our work takes a different,holistic, view on the context. We embrace energy andspectrum resources as well as the application deliveryrequirements to guide our rate adaptation.

3 FLEXIBLE RADIO COMMUNICATION

Our primary objective is to efficiently use the availableresources for wireless communication. A flexible physi-cal layer is the key enabler of efficiency. Through sophis-ticated adaptation of PHY parameters, such as modula-tion level or channel width, we can impact energy andspectrum usage, and packet delivery performance.We embrace orthogonal frequency-division multiplex-

ing (OFDM) as a foundation of the PHY layer in ourwork. OFDM has emerged as a basis for a number ofrecent research efforts [22], [32] and also is the techniqueof choice for many wireless standards such as IEEE802.11a/g (WiFi), 802.16 (WiMax) and 802.22 (WRAN inwhite spaces) due to its low sensitivity to harsh chan-nel conditions. An OFDM channel consists of multiplenarrow-band subcarriers, a high level of flexibility can beachieved if we manipulate the subcarriers individually.This allows us to explore the solution space definedby modulation and coding schemes, variable channelwidths and the available spectrum distribution. Ourapproach to channel width change is similar to Jello [32]and 802.22 [11]: with OFDM subcarrier (de)activation wechange the width and bind the available spectrum intoa single OFDM channel, even if the available spectrumresides in non-contiguous bands (Figure 1).2

As we do not advocate any specific standard protocolstack in this paper, our work also remains operating-frequency agnostic. Our practical implementation, how-ever, targets white spaces due to the vast number ofpotential channel widths and central frequencies.

2. We define a “channel” as a band over which a single link canoperate. This bandwidth is limited by transmitter’s analog-digital con-verter speed. Within the channel, OFDM subcarriers can be activatedin various, even non-contiguous. formations. Thus, we use “channelwidth” and “number of active subcarriers” interchangeably.

4 PHY PARAMETERS AND SYSTEMPERFORMANCE

In this section we examine the impact of PHY parameterson communication quality and energy and spectrumutilization. The findings are used to guide the designof our rate adaptation protocol, WhiteRate (Section 6).

4.1 Communication Performance

Packet delivery depends on both modulation and codingscheme, and the channel width. In an OFDM system wecan consider each of the subcarriers individually, andfor each of them Shannon’s capacity formula defines themaximum sustainable bitrate:

(1) Ri = W log2 (1 + SNRi),

where W represents the bandwidth occupied by a singlesubcarrier, and SNRi represents the signal-to-noise ratioat the ith subcarrier. This bitrate used in the calculationsrepresents an upper bound. In physical systems, thechoice of MCS, which is guided by the desired bit errorrate (BER), determines the actual bitrate:

(2) Ri = W log2 M ,

where M ≤ 1 + SNRi has to be a practically feasiblemodulation level. Naturally, the higher the modulationlevel, the closer the system is to its full capacity. How-ever, as the number of bits packed in a single transmittedsymbol M increases, a more subtle difference amongpossible signal values results in easier misinterpretationof the signal. The robustness of MCSs is a well studiedtopic [21], and rate adaptation protocols usually selectthe MCS that maximizes throughput, i.e. the amountof information that reaches the receiver without errors.Since modern coding schemes rely on the Viterbi algo-rithm and soft decoding, in practical implementations allsubcarriers use the same MCS to simplify the decoding3.Wider channels always result in a higher capacity; the

relationship between channel width and transmissionerrors, however, is not as clear. In [10] Chandra et al.report lower packet loss over narrower channels. Theyexplain this phenomenon by increased power-per-Hzand better resilience to delay spread of narrow channels.The same reasoning does not hold in our case. As shownin Figure 1, we keep the same power per subcarrierirrespective of the number of active subcarriers and,unlike in [10], we do not change the subcarrier width.In our problem setting, frequency-selective fading, isthe most probable cause of channel width-dependentdelivery errors. In the case of frequency-selective fading,subcarriers at different frequencies experience mutuallydifferent channel gains. If a data packet is sent over anOFDM link where the subcarriers experience differentfading, some bits are more likely to be corrupted thanothers. With subcarrier interleaving, the errors are dis-tributed evenly throughout the OFDM symbol [14].

3. Viterbi decoder occupies a significant number of logical gates on awireless NIC [14]. The operation is also the most processing intensivepart of the demodulation in case of the SDR.

4

The impact of channel width on the packet error ratedepends on the way the width change is envisioned. Wediscuss two cases: (i) when the subcarriers are activatedaccording to the channel state they observe in the best-first fashion, and (ii) when the subcarriers are activatedso that they always remain contiguous. The first case isoptimal in a sense that it does not waste transmissionpower to overcome the impact of poorly performingsubcarriers when better ones are available. However,from a practical point of view best-first comes withtwo major drawbacks. First, non-adjacent subcarriersdemand precise narrow filters that require substantialcomputing power. Second, subcarrier selection basedon the channel quality requires sweeping through thewhole frequency range so that the well performingfrequencies can be isolated. This increases the protocoldelay and the communication overhead. Therefore, wedevise a practical solution shown in Figure 1: adjacentsubcarriers are assembled in a small number of groupsand each group is individually (de)activated accordingto the average channel gain observed within a group.Consequently, channel widths are ordered in our system– a lower width always results in a better delivery rate.Note that due to multipath fading and imperfect filteringthe relationship between achieved rate and the numberof active subcarriers is not perfectly linear. Thus, inWhiteRate we rely on explicit channel probing in orderto identify the optimal channel width.

4.2 Energy Efficiency

A good understanding of the energy consumption ofwireless a network interface card (NIC) operating overflexible OFDM helps us to identify the space for im-provement of the energy efficiency of the whole system.A bit of information over an OFDM wireless channel

with k active subcarriers is transmitted with energy:

(3) ETx =

k∑

i=1

PTx,i + PTC

k∑

i=1

Ri

where PTx,i represents the transmission power over thei-th subcarrier and PTC the transceiver circuit powerof a wireless NIC [16]. The transceiver circuit power isconstant irrespective of the transmission parameters andrepresents the power needed to keep the digital circuitspowered whenever the NIC is in the transmission mode.The energy efficiency thus depends on the cumulativetransmission power

∑PTx,i and transmission bitrate∑

Ri and their relationship as we change the channelwidth and/or MCS. Changing modulation levels andcoding rate does not result in a noticeable power con-sumption change in modern WiFi NICs [6], [29], andsame is true for USRP2 power consumption (Table 1).It follows that higher MCSs are more energy efficient asthey reduce the transmission time without any powerconsumption penalty. Channel width change through

TABLE 1Power consumption breakdown for USRP2. Fixed

factors such as PTC and Pbase dominate the totalconsumption when the device is in transmission mode.Transmission power grows linearly with channel width,

while it remains unaffected if the MCS changes.

Total power Ptotal(tx amplitude = 0) 12.77 WTotal power when idle, Ptotal(idle) = Pbase 11.76 W

Transceiver circuit power PTC = Ptotal(0) − Ptotal(idle) 1.01 W

The above values are independent on the number of active subcarriers,modulation and coding scheme and the USRP interpolation rate.

Changing MCS, tx amplitude = 0.5PTx = Ptotal(max width, any mcs) − Pbase − PTC 0.45 W

Changing width, tx amplitude = 0.5PTx = Ptotal(all widths, any mcs) − Pbase − PTC [0.22 - 0.45W]

subcarrier activation leads to a linear increase in thetransmission power PTX =

∑k

i=1 PTx,i and the transmis-

sion rate R =∑k

i=1 Ri. The total power consumption,however, is a sum of PTX and a large constant factorPTC (see Table 1). It is clear that if we want to optimizethe energy consumption ETX , we have to maximizethe channel width so that the constant factor in thenumerator of equation (3) is diminished.

4.3 Spectrum Utilization

Spectrum efficient transmission utilizes the medium asclose to its physical capacity as possible. Modulationlevels and an adjustable channel width are the primaryPHY parameters that impact spectrum utilization.Modulation constellation that utilizes the I-Q space

so that the distance between constellation points isminimized leads to maximal spectrum utilization. Sub-carrier activation impact spectrum utilization, as acti-vating fewer subcarriers than what is allowed by theavailable bandwidth leads to suboptimal transmissiontimes. In addition, a subcarrier activation that leadsto high spectrum fragmentation can prevent concurrenttransmissions on other links in a network.In summary, spectrum utilization increases with

higher MCSs and higher number of active subcarriers,i.e. wider channels. The approach in our protocol is thus,to push the modulation level to the highest supportedvalue and to occupy as much bandwidth as possible.Since these parameters impact the delivery error, thelimits on their values are imposed by the packet errorrate (PER) threshold that user can tolerate.It is important to note that in a multiuser system, spec-

trum utilization is tightly connected with fairness. Here,a few wide links can starve nodes that do not have anyavailable spectrum to use. In addition, it has been shownthat low transmission rates, even at only one of the com-peting transmissions, significantly lower the throughputof all the links in the interference domain [23]. AlthoughPHY parameters change spectrum utilization in a rela-tively straightforward way, MAC schemes can result in anon-trivial relationship between the MCS, channel width

5

and spectrum utilization. In the evaluation section werely on trivial MAC implementations that allow us toisolate the impact of PHY layer parameters.

5 CONTEXT AWARENESS

Wireless systems are deployed for different purposesand even the same system can have varying prioritiesdepending on the operational situation. A rural areawireless network may need to support high qualityvoice and video communication in order to connectremote regions. However, the same network might favorenergy efficiency over the communication quality oncegrid power is unavailable and the electricity is suppliedthrough limited charge UPSs. We argue that the tradi-tional one-size-fits-all solution for PHY layer parameteradaptation can be counter-productive in certain contexts.We describe three avenues where a holistic approach isgreatly beneficial.

5.1 Loss Tolerant Applications

Multiple layers of a standard networking stack aregeared towards reliable packet delivery. However, manyapplications do not require lossless communication. Forexample, voice and video communication are often en-coded so that they can tolerate non-zero packet loss.Moreover, voice and video are usually used to transmitdata framed in a certain situation (e.g. a human con-versation about a known topic). From the endpoint per-spective, communication success is not a binary value,but can be described as more or less satisfactory de-pending on user tolerance. In some cases extra effortput into ensuring flawless packet delivery can evenhurt performance. Real-time traffic requires stringentpacket delay and jitter guarantees, whereas reliabilitycan introduce additional unpredictable delay. Finally,reliable delivery, either through transmission with morerobust MCSs (thus more redundancy and lower bitrate)or through packet retransmissions, requires extra energyand increases channel busy time.

TABLE 2Commonly used voice and video codecs and their

packet loss tolerance. Real-time multimedia oftentolerates non-zero packet loss.

VoiceCodec G.711 PLC G.726 G.729a G.723.1 G.722

Loss Tolerance 10% 5% 2% 1% 5%

VideoCodec H.262 (MPEG-2) H.264 (MPEG-4)

Loss Tolerance 0.3% - 0.9% 0.2% - 2.2%

In Table 2 we list commonly used voice and videocodecs along with the packet error rates (PERs) belowwhich the quality of the communication is consideredacceptable [1], [8], [9]. For successful voice and videocommunication over a wireless link, we need to ensure

that the packet error rate is below a certain, codec-specific, threshold, which is often significantly abovezero. Thus, if relaxing the packet delivery constraintsincreases protocol efficiency, we can allow PER to benon-zero but below the application packet loss tolerance.

5.2 Limited Energy BudgetMobile communication devices are expected to be com-pact yet powerful and as a result have very limitedbattery capacity. Power-hungry high-bandwidth datatransfers often leave these devices depleted. Rural areawireless networks deployed in remote areas seldom haveaccess to a reliable power grid and have to be pow-ered by alternative energy sources such as unpredictablewind and solar energy. These two use cases representwireless systems that are very sensitive to communica-tion protocol energy inefficiency. Fortunately, these twoinstances can highly benefit from context aware PHYlayer adaptation.In Section 4.2 we analyzed the impact of PHY pa-

rameters on energy consumption. The need for energyefficiency depends on the battery charge - a value that isoften exposed by a mobile device drivers. In a networkof self-powered wireless routers, on the other hand,battery charge is a complex function of local wind speed,solar irradiation and hardware properties. In this case,the information about the energy needs can be inferredthrough energy flow models [20]. Our protocol designedin Section 6 ensures that the energy efficiency of PHYlayer operation corresponds to the energy needs of thesystem. The actual means of determining those needs,however, is beyond the scope of this work.

5.3 Spectrum Demanding OperationWireless spectrum is unevenly utilized: urban environ-ments suffer a shortage of available bandwidth, whilein rural areas there is an abundance of unutilized chan-nels [13]. Unfortunately, current protocols usually oper-ate on a fixed central frequency and with a fixed band-width, which prevents them from using communicationresources efficiently, and high spatial and temporal corre-lation of the bandwidth demand among users often leadsto intermittent connectivity with virtually no quality ofservice guarantee.Our system adapts by adjusting the channel width and

MCS according to spectrum availability. The strategy ofadaptation depends on the medium access scheme. Forexample, with a time ordered scheme (e.g. TDMA) theprotocol should allow a high level of multiplexing byutilizing the widest available channel so that the thetransmission time is minimized and more flows can bepacked in time. In a frequency division regime our pro-tocol should enable as many multiple flows to operateconcurrently as possible, by increasing the MCS and,thus, enabling a narrower channel width per each flow.Note that the available spectrum is often fragmented;for efficient spectrum utilization our protocol allows fornon-adjacent subcarrier activation (Figure 1).

6

6 WHITERATE

Physical layer parameters impact communication perfor-mance (section 4.1), energy efficiency (section 4.2) andspectrum utilization (section 4.3). Often the impact isbeneficial from one angle and detrimental from another.The usage context defines the importance of each of theabove aspects. To provide a practical solution for PHYadaptation under various packet delivery, spectrum uti-lization and energy efficiency requirements, we designWhiteRate, a holistic approach that adapts the bitrateaccording to the usage context.

Fig. 2. Relationship among the context-aware PHYadaptation system components: The top three mod-ules (application layer, energy model and spectrum scan-ner) determine the system requirements: applicationpacket delivery, energy efficiency and spectrum utiliza-tion. In addition, the spectrum scanner marks the avail-able spectrum. The information is passed to the prioritydetermination module, which analyzes it and sets a singlePER threshold and available channel widths that guideWhiteRate. WhiteRate adjusts MCS and channel width,while monitoring the PER, making sure that it stays withinthe limits given by the priority determination module.

6.1 Cross-layer Information Flow

We consider three types of constraints imposed by thecontext in which a device operates. The first one is theapplication packet delivery requirement. In our systemwe integrate “hooks” that allow the overlaying applica-tion to set the maximum tolerated PER. As seen in Sec-tion 5, even a single type of traffic can have different losstolerances. The second constraint comes from the limitedenergy reserves of the system. In the case of mobiledevices or self-powered routers, energy efficiency oftentakes precedence over lossless communication. Again,the “hooks” are provided for the platform to provide itsenergy reserves information. This vertical flow of infor-mation enables loosening of the PHY layer restrictions.Finally, modern SDRs can easily, and with a fine time andfrequency granularity, monitor wireless spectrum. We letan independent scanning module deliver the availablespectrum information to which WhiteRate abides.

Fig. 3. Packet delivery reliability, energy and spec-trum efficiency trends with changing bitrate. A restric-tion on the PER is sufficient to control rate adaptation asoperation at the edge of tolerable PER ensures maximumenergy and spectrum efficiency. Note: the lines in thegraph are for illustrative purpose only, and do not repre-sent actual values.

In Figure 2 we illustrate the relationship betweendifferent parts of the system and the information flow.Application layer, energy model and spectrum scanner aremodules that rely on techniques from sections 5.1, 5.2,and 5.3, respectively. Priority determination enables Whit-eRate to answer to complex and perhaps contradictingdemands through a practical algorithm.The trends in energy and spectrum efficiency and

packet delivery reliability are illustrated in Figure 3.High bitrate positively impacts both energy savings andspectrum efficiency, yet can have an adverse effect onpacket delivery. Therefore, the PER threshold is set bythe priority determination module and WhiteRate en-sures that the bitrate is adjusted so that the actual errorrate is below the threshold. The threshold exact valuedepends on the context and external properties, such asthe system hardware and user preferences. In the rest ofthe paper we use a stub implementation of the modulesin the dotted boxes and concentrate on WhiteRate.

6.2 The Algorithm

We design WhiteRate as a packet-level MCS and channelwidth adaptation solution. WhiteRate keeps track of thePER and ensures that the MCS and channel width are setso that the PER stays within the specified tolerance (Al-gorithm 1). At the same time, it aggressively tries to findthe highest achievable bitrate within this loss tolerance,as in that case the energy consumption is minimized andthe spectrum utilization maximized (Algorithm 2). Toidentify the most efficient bitrate, WhiteRate periodicallyprobes channel width and MCS combinations that havelower theoretical minimum bit transmission time thanthe current combination’s average bit transmission time.Packet delivery statistics are updated in the process,and if at any point WhiteRate finds a combination thatresults in higher energy and spectrum savings whilekeeping the PER below the threshold, it switches to itand continues probing as before.

7

In the event of a complete loss of wireless link, Whit-eRate quickly adjusts to a lower bitrate setting. Once areliable MCS is pinpointed, WhiteRate continues, withthe help of channel gain estimations, to intelligentlyadjust the channel width as long as unsatisfactory end-to-end performance is observed.

Algorithm 1 WhiteRate

Require: MCS = MCS5, channel width = channel width7

Ensure: PER < PERthold

1: if c consecutive losses then2: MCS = min(MCS − 1,MCS1)3: else if lth packet then4: Evaluate PER5: if PER > PERthold − PERmargin then6: bin search reduce(channel width)7: else if PER < PERthold − PERmargin then8: bin search expand(channel width)9: end if10: else if pth packet then11: set pick probe(channel width,MCS)12: else13: pick best() {Average tx time table lookup.}14: end if

Algorithm 2 pick probe

1: Parameter: MCSi

2: if ∃MCSj | min tx time[MCSj ] < avg tx time[MCSi]then

3: MCS = MCSj

4: if PER(MCSj) > PERthold − PERmargin then5: bin search reduce(last channel width at MCSj)6: else if PER(MCSj) < PERthold − PERmargin then7: bin search expand(last channel width at MCSj)8: end if9: end if

6.3 The Algorithm - Details

We give a fictional example of the algorithm flow for asingle source-destination pair in Figure 4. The columnsrepresent available MCSs and rows represent channelwidths, which can be composed of non-adjacent sub-carriers if needed. Wider channels/higher MCSs arelabelled with a higher subscript value (widths from 1 to7, MCSs from 1 to 5). Cell shades represent average bit-transmission times, where a lighter shade means a lowertransmission time. These times are initially unknown,and are computed as the algorithm visits each of thecells, while the minimum theoretically possible transmis-sion times for MCSs (with the widest channel) are knownin advance. Depicted is also (with a black line) an ini-tially unknown region within which [MCS, channel width]combinations yield PER acceptable for the application.WhiteRate search dimension selection. In an OFDM

system that implements interleaving and operates overlimited spectrum, the choice of MCS determines theorder of the PER magnitude, while the channel widthadjustment allows fine-grained tuning. WhiteRate im-plements mechanisms that enable fast recognition of the

width1

width2

width3

width4

width5

width6

width7

Norm

alized bit-transmission tim

e

Acceptable PER boundary

100% PERCurrent position

MCS1 MCS2 MCS3 MCS4 MCS5

0

0.2

0.4

0.6

0.8

1

Minimum lossless transmission time

(a) Initial position.

width1

width2

width3

width4

width5

width6

width7

Norm

alized bit-transmission tim

e

MCS1 MCS2 MCS3 MCS4 MCS5

0

0.2

0.4

0.6

0.8

1

Minimum lossless transmission time

(b) MCS fallback.

width1

width2

width3

width4

width5

width6

width7

Norm

alized bit-transmission tim

e

MCS1 MCS2 MCS3 MCS4 MCS5

0

0.2

0.4

0.6

0.8

1

Minimum lossless transmission time

(c) Width change and probing.

width1

width2

width3

width4

width5

width6

width7

Norm

alized bit-transmission tim

e

MCS1 MCS2 MCS3 MCS4 MCS5

0

0.2

0.4

0.6

0.8

1

Minimum lossless transmission time

(d) Final position.

Fig. 4. WhiteRate protocol example. Each cell repre-sents one [channel width, MCS] combination. The goal ofWhiteRate is to identify the cell that results in minimumaverage bit-transmission time, while satisfying the PERthreshold, i.e. the brightest cell left of the black line.

8

channel state, thus allowing the algorithm to adjust theappropriate parameter.In the case of extremely bad link performance, recog-

nized by c consecutive packet losses, WhiteRate adaptsto a more robust MCS. In our example, the algo-rithm starts transmission over the highest possible MCSand the widest available channel, [MCS5, width7] (Fig-ure 4(a)). The next lower combination ([MCS4, width7])might still be lossy enough to result in c consecutivepackets dropped. Thus, the process is repeated until alink is established at [MCS3, width7] (Figure 4(b)).Well-performing MCS and channel width combina-

tion identification.When the link is established, the PERis calculated and compared to the application providedthreshold. If it is above the threshold the application cantolerate, the channel width has to be changed.WhiteRate periodically estimates the channel gain by

transmitting a known bit sequence. Afterwards, OFDMgroups (Figure 1) are ordered based on the descendingaverage gain. Thus, the narrowest channel (width1) inFigure 4 contains only the best performing groups ofsubcarriers, and we expect the channel conditions to de-teriorate as we increase the width. This ordering allowsWhiteRate to quickly, through binary search, identify achannel width that supports the PER requirements of theapplication, and prevent excessive loss. In the examplein Figure 4(c) WhiteRate finds a well performing combi-nation at [MCS3, width4].Identifying the optimal solution. The current [MCS,

channel width] pair satisfies the PER requirement butmight not be the optimal choice in terms of energyefficiency and spectrum usage. Therefore, the algorithmprobes other MCSs, but only those that can potentiallylower the transmission time and consequently improvethe efficiency. Thus, for each of the MCSs we keep infor-mation on the so-far-observed average bit transmissiontime and the minimum possible bit transmission time.WhiteRate searches for a better solution than the cur-

rent one by transmitting every pth packet on an MCSthat has a minimum bit transmission time lower thanthe currently observed average bit transmission time andthat has not experienced c consecutive losses recently.In Figure 4(c) these probes are depicted with arrows.First, WhiteRate picks a candidate MCS (MCS2) thatcould, ideally, result in better performance than thecurrent combination ([MCS3, width4]). If the candidateMCS is probed for the first time, the transmission takesplace over the widest channel ([MCS2, width7]). Forthe probed candidate [MCS, channel width] combination,WhiteRate calculates the average bit transmission timeand the packet error rate. If the new bit transmissiontime is lower than the current one (corresponding to[MCS3, width4]), and the PER satisfies the applicationrequirements, WhiteRate switches to the candidate MCSand channel width. Otherwise, the next time the sameMCS is selected for probing, WhiteRate readjusts thechannel width through binary search, and probes avalue that should result in lower PER (in our case

[MCS2, width5]). Once the combination that has theminimum of all average bit transmission times is found,WhiteRate selects it as a new ground point. In ourexample, as shown in Figure 4(d), WhiteRate settles at[MCS2, width5], which is the combination that returnsthe lowest bit transmission time among all combinationsthat satisfy the application’s packet loss requirements(the brightest cell under the PER boundary line).

6.4 Discussion

We presented an example WhiteRate run in the casewhen a link is established and WhiteRate has not yetexplored any of the channel width, MCS combinations.Otherwise, in case that the PER is satisfied at the currentcombination, the adaptation process reverts to combi-nation probing; in case the current PER is above theapplication threshold, the process reverts to channelwidth/MCS fallback. Note, however, that WhiteRate canadapt successfully only if the channel conditions arestable for sufficient enough time for the protocol tocalculate PER and identify at least one well performingcombination.In the worst case protocol convergence requires MCS

identification, which can be done in O(M) , where M

is the number of MCSs, and channel width adjustment,which can be done in O(log(W )) where W is the num-ber of available channel widths as the binary searchis employed. The total complexity, O(M) + O(log(W ))is significantly lower that the complexity of exhaustivesearch (O(MW )). To quickly bootstrap a link to a nearoptimal MCS, channel width combination a history ofrates can be kept.Compared to protocols that are oblivious to frequency

selective fading, WhiteRate introduces very little over-head. Subcarrier quality evaluation is based on the PNsequence that is a standard part of the transmission, as itis needed for OFDM frame acquisition [24]. The receiverpiggybacks the subcarrier group ordering to the sendervia a packet acknowledgement. For N subcarrier groupsthat overhead is only Nlog2N bits; for example, if we useseven groups we introduce only a three byte overheadin the acknowledgement.The WhiteRate algorithm bears some resemblance to

SampleRate [7] in its PER guided rate adaptation. Similarto the practical implementation of SampleRate from theMadWiFi driver, WhiteRate maintains a separate algo-rithm table for different packet sizes, and implicitly takesthe relationship between BER and PER into account.Unlike SampleRate, WhiteRate changes both MCS andchannel width and explicitly caters to application needs.Additionally, WhiteRate can ensure lossless communica-tion if the PER threshold is set to zero. If the channelwidth is fixed and application hints not present, Whit-eRate essentially behaves as SampleRate.Finally, it is important to realize that WhiteRate aims

for context-aware link adaptation. Network performancein case of multiple interfering links running WhiteR-ate depends on many other factors, such as the MAC

9

scheme, and is beyond the scope of this paper. Intu-itively, however, WhiteRate’s tendency to narrow thechannel in search for reliable transmission leads to lowercontention among links.

7 GNURADIO IMPLEMENTATION

We implement WhiteRate in GNUradio - an open sourcesoftware defined radio framework. The key componentsof our implementation are a MAC layer that takes care oftransmission scheduling and delivery tracking, the rateselection algorithm, a flexible OFDM subcarrier activa-tion system that allows on the fly channel width andmodulation and coding scheme (MCS) selection, forwarderror correction coder, and channel gain estimator. Weuse the publicly available flexible OFDM system fromJello [32] and channel coding implementation from Soft-Rate [30], and integrate them with the other componentswe implemented.Our initial implementation [19], did not allow runtime

rate adaptation due to the following problems: a) GNU-radio/USRP processing latency prevents the ACK-basedstop-and-wait transmission needed to keep track of thepacket error rate [17], and b) channel width/MCS adap-tation requires that GNUradio flow graph is stopped,reconfigured and reconnected, incurring a non-negligibletime delay. In our improved implementation we solvethe first problem by using an Ethernet back-channel totransmit packet acknowledgements as well as controldata, such as channel width change signals and channelgain updates. This cuts the minimum stop-and-wait timeto about a half of its original value4. To solve the secondproblem, we move all GNUradio graph adaptation codefrom Python to C++ and cut the channel width/MCSadaptation time to nearly 100ms. This value, along withthe halved ACK processing time, allows us to implementWhiteRate as a fully operational real-time protocol.SampleRate is a widely accepted rate adaptation pro-

tocol often used as a benchmark for other rate adaptationprotocols [5], [30]. We implement SampleRate and inour experiments compare WhiteRate and SampleRate.As SampleRate is width agnostic, we use it with themaximum channel width, unless otherwise stated.

8 EXPERIMENT SETUP

WhiteRate is geared towards improving two aspects ofwireless communication: energy efficiency and spectrumutilization. We envision and focus on two scenarioswhere these aspects are crucial for overall networkperformance. The first scenario is a rural area wirelessnetwork connecting distant locations via white spacelinks powered by alternative energy sources. Studies ofexisting rural area wireless networks show that criticalbattery resources, and voice and video communication

4. We found out that the actual value highly depends on the distancebetween the nodes, processing PC configuration and the over-the-airbandwidth. In our experiments we support a 500kHz channel with200ms inter-packet time.

are the key descriptors of such networks [28]. The secondscenario is a wireless network of numerous personaldevices operating in the same wireless spectrum. Thisscenario is common in densely populated urban ar-eas [3]. We build two testbeds so that we can examineWhiteRate’s performance in both scenarios.Our first testbed is located in suburban Pretoria, South

Africa. It consists of a single 3 km outdoor link betweentwo PCs(Linux)/USRP2s with whitespace-enabled WBXcards and directional antennas. On the sender side weuse a 1W amplifier, while on the receiver we do not haveany amplification. A direct view between the nodes isobstructed by a hill, but transmission over white spacesallows a robust non-line of sight link. With its distance,outdoor surroundings, susceptibility to weather condi-tions and terrain configuration, this link encompassesall the phenomena we expect in a rural area network.Therfore, we use this testbed for experiments that pro-vide insights about channel behavior. In addition, weWhiteRate as a key protocol for enabling energy savingsin self-powered rural area networks, thus we use thistestbed for all experiments related to energy efficiency.We deploy an indoor testbed of four

PCs(Linux)/USRP2 nodes in a building at UC SantaBarbara campus. The nodes are located in the sameroom, and the distance between any two nodes isapproximately three meters. This testbed allows usto examine WhiteRate spectrum saving potential in aspectrum-scarce setting. In addition, in this testbed wecan achieve robust links over a much wider range oftransmission amplitude values, thus we use it for amore complete analysis of WhiteRate’s correctness interms of achieved delivery ratio, throughput and bitrate.Our transmission uses white space frequencies (cen-

tered at 694MHz for the Santa Barbara testbed, and at530MHz for the testbed in Pretoria) and a 512 FFT with462 possibly active subcarriers, divided in 7 groups of 66subcarriers. We use 1/2 convolution coding and BPSK,QPSK, 8-QAM, 16-QAM, 64-QAM MCSs. The defaultchannel is 500kHz wide for the outdoor and 300kHzwide for the indoor testbed.

9 EXPERIMENTAL EVALUATION

We have two goals in the evaluation. In the first part(9.1) of the evaluation we experimentally justify twoimportant protocol design decisions: prioritizing MCSover channel width for rate adaptation and relying onchannel gain stability for width determination. In thesecond part (9.2), we thoroughly analyze WhiteRate’sability to efficiently utilize resources while ensuringsatisfaction of the application requirements.Unlike the preliminary evaluation from our earlier

work [19], the experiments presented in this paper areconducted with WhiteRate operating in real-time, over alonger NLOS outdoor link or a multi-link indoor setup.Moreover, in this work we evaluate WhiteRate underboth static as well as dynamic application requirements.

10

0

0.2

0.4

0.6

0.8

1

50 100 150 200 250 300 350 400 450 500

Pac

ket e

rror

rat

e

Number of active subcarriers (channel width)

BPSK8-QAM

16-QAM64-QAM

Fig. 5. Packet error rate under different channelwidths and MCSs. Narrower channels, composed ofbetter performing subcarriers, deliver more packets. How-ever, the difference between the PER at the widest andthe narrowest channel is not as drastic as the differenceamong the PERs achieved at different MCSs.

9.1 WhiteRate design

WhiteRate needs to quickly identify a sustainable bitrate.To this end, our protocol switches to a lower MCS after cconsecutively-dropped packets in a row (Section 6). Analternative approach is to prioritize the channel widthand change it once consecutive losses are experienced.If MCS has more impact on packet loss than channelwidth, our approach leads to fast convergence and is thepreferred alternative. The first goal of our experimentsis to evaluate this claim.In the South African testbed we transmit a sequence of

500 packets over each [channel width, MCS] combinationand record the PER. We want to examine the changein combination performance while the channel is stable.Therefore we measure PER as we cycle through all thecombinations starting a transmission immediately aftera previous one ends. Subcarriers are allocated accordingto the gain averaged over a group of subcarriers, asexplained in Section 4.1. In Figure 5 we show the errorrate (y-axis) for different channel widths (x-axis) andMCSs (different point types). We observe that the choiceof MCS impacts PER more than the choice of the channelwidth. A single MCS experiences no more than between0% and 70% loss for all channel widths (the 16-QAMcase), while channel widths can experience everythingfrom 0% to 100% loss. Therefore, our decision to useMCS for coarse grain rate adaptation is experimentallyvalidated.WhiteRate uses channel gain measurements to rank

subcarrier groups and activate them in descending order.If the channel gain ordering among groups varies overtime, the protocol needs to repeat the measurements pe-riodically. In order to examine the channel gain stability,and its role in width determination, we measure the gainchange over time in our outdoor testbed. To estimate thechannel gain, we prepend each packet with a knownBPSK modulated preamble and compare the receivedcopy with the original one. We then average the gainover all subcarriers in a group.

-55

-50

-45

-40

-35

-30

-25

-20

-15

0 20 40 60 80 100 120

Gai

n [d

B]

Time [s]

500kHz channel2MHz channel6MHz channel

Fig. 6. Channel gain observed at each of the sevensubcarrier groups over time and with different totalchannel widths. Each line represents gain of one group,different colors correspond to different widths. The gaindoes not change over time and the ordering of groupsremains constant.

Figure 6 shows, with a single line, the gain for each ofthe seven subcarrier groups. We repeat the experimentover three different bandwidths: 500kHz, 2MHz and5Mhz. In Figure 6, lines of a different colour correspondto different bandwidths used in the measurements. In-terpacket time is 200ms and a total of 600 packets issent at each bandwidth. We observe that the channelgain is fairly constant over time. Even more important,the relative ordering of groups remains the same. Thetestbed with its long distance NLOS link may result ina non-negligible multipath, that would manifest as thefrequency selective fading. What we observe though, isthat fading varies over subcarrier groups, but remainsconstant over time. A closer examination reveals thatgroups closer to the edge of the channel are in a deeperfade. We suspect that rather than multipath, imperfectfiltering and amplification cause this frequency selectiv-ity. Irrespective of the cause, the results point out thatonce channel gain sampling is executed, there is no needfor frequent gain re-estimation. In the rest of this sectionwe report on experiments with flows that last fewer thantwo minutes, thus we perform gain measurements onlyonce at the beginning of the flow.

9.2 WhiteRate performance

Because WhiteRate is designed to be a rate adaptationprotocol for diverse environments, we evaluate it overa range of channel conditions: indoor, outdoor, longdistance and short distance links. We quantify the en-ergy and spectrum savings it delivers and examine itsadaptability to dynamic system requirements.

Packet loss, Throughput and BitrateTo understand how rate adaptation copes with changinglink quality, we vary the transmission amplitude andmeasure the packet loss, throughput and bitrate. Theresults are shown in Figure 7. Each data point representsan average over five runs of an experiment that consistsof 500 packets sent at channel width-MCS combinations

11

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2

Pac

ket e

rror

rat

e

Transmission amplitude

PER threshold

SampleRateWhiteRate

(a) Packet error rate.

0

50

100

150

200

250

300

350

400

450

500

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2

Ave

rage

thro

ughp

ut [K

bps]

Transmission amplitude

SampleRateWhiteRate

(b) Throughput.

50

100

150

200

250

300

350

400

450

500

550

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2

Ave

rage

bitr

ate

[Kbp

s]

Transmission amplitude

SampleRateWhiteRate

(c) Bitrate.

Fig. 7. Rate adaptation under varying transmission amplitudes. WhiteRate keeps packet errors below the giventhreshold (left), without sacrificing throughput (center). Transmission bitrate (right) is higher for SampleRate, but at thecost of higher PER. All data points are averages of five runs, with error bars marking two standard deviations.

picked by the protocol under study, either WhiteRateor SampleRate5. This set of experiments is run in theindoor testbed, as it allows a wider range of transmissionamplitude values at which the link can be maintained,thus enabling comprehensive evaluation.WhiteRate abides by the application PER require-

ments. In Figure 7(a) we see that the PER threshold re-mains satisfied with all but very low transmission ampli-tudes. To achieve the desired packet delivery WhiteRatelowers the bitrate (Figure 7(c)). SampleRate, on the otherhand, does not explicitly take application requirementsinto account. The resulting PER is variable and abovethe threshold value.Despite being constrained by the PER threshold, Whit-

eRate manages to keep the throughput on a par withSampleRate, a protocol specifically designed with maxi-mum throughput in mind. We ran SampleRate with thehighest channel width, thus the highest bitrates for everyMCS. The highest throughput, however, is found at the[MCS, channel width] that balances the bitrate, and linkconditions (PER). This provides a strong case for channelwidth adjustment even if we consider rate adaptation ina traditional sense of throughput maximization.

Energy Savings

We quantify the energy savings that WhiteRate deliversunder varying levels of PER tolerance. We calculatethe energy per bit by multiplying the time needed totransmit a bit of information by the platform powerconsumption. Time to transmit a bit of information is cal-culated directly from the MCS and channel width used.The power consumption depends on the transmissiondevice and differs drastically between an experimentalSDR platform such as USRP2 and a commodity devicesuch as a WiFi NIC. For system evaluation purposeswe use power consumption figures of a USRP2 devicefrom [18]. We discard the base power, since it is MCSand channel width independent, an order of magnitudehigher than the base power of commodity devices and

5. In our previous, trace-based evaluation we analyzed channelwidth, MCS combinations that are optimal in terms of PER or resultin zero loss [19]. Since we cannot guarantee the exact same channel inevery run in the real-time experiment, here we omit Optimal selections.

Fig. 8. Energy efficiency with varying PER threshold.

incurs bias towardWhiteRate or any protocol that lowersthe transmission time.The results of experiments over our long-distance out-

door link are shown in Figure 8. At each error tolerancelevel we sent five hundred packets; each data point inthe figure is an average value of five runs, with errorbars representing two standard deviations. Delivery re-quirements have a strong impact on energy efficiency:the energy per bit can be cut to a third of its initialvalue if the delivery requirements are relaxed to thePER threshold of 20%. Even a relatively tight error raterequirement (5%) yields two-fold energy savings. As aconsequence, the priority determination module describedin Section 6 can strictly favor energy efficient operationby adjusting the required PER level.

Energy Efficiency and Application PerformanceWe showed that WhiteRate increases energy efficiencywhen the PER threshold is loosened. However, higherpacket error rate leads to lower end-to-end communica-tion quality. We evaluate WhiteRate’s ability to balancethe energy savings and the communication quality. Weare especially interested in voice-over-IP as it is thekey application in rural areas where communicationalternatives are non-existent [25].In our South African testbed we transmit a stream of

500 packets that are modelled to resemble G.711 encodedspeech and vary the application PER requirements from1% to 20%. We measure the actual loss rate and theenergy consumption; the results are averaged over five

12

1

1.5

2

2.5

3

3.5

4

4.5

5

0 2 4 6 8 10 12 14

MO

S

Energy per bit [µJ]

1%2%

5%10%

20%

WhiteRateSampleRate

BPSK16-QAM

Fig. 9. Tradeoff between voice quality and energy sav-ings. The plot shows WhiteRate runs with different PERrequirements, and SampleRate, BPSK and 16-QAM withseven different channel widths. With a single parameter –PER threshold – WhiteRate allows fine grained selectionbetween energy efficiency and VoIP call quality.

runs. Perceived quality of voice communication is re-lated to loss rate through the e-model. The model reportsthe Mean Opinion Score (MOS) of voice quality rangingfrom 1 (poor) to 5 (excellent), with anything above3.5 being considered satisfactory. In Figure 9 we showcalculated MOS and energy per bit for WhiteRate as wellas for SampleRate (tested with seven different channelwidths) and fixed MCS, channel width combinations(BPSK and 16-QAM).The results in Figure 9 show the energy saving benefits

of loosening the packet delivery requirements. Relaxingthe threshold from 1% to 5% PER yields 48% energyper bit savings with minimal impact on MOS. In thesame figure we plot the performance of SampleRate thatwe run independently under seven different channelwidths (from 66 to 462 active subcarriers). SampleRateis application-agnostic and results in unpredictable MOSvalues. We also plot the results of the operation on afixed MCS (we plot 1/2 BPSK and 1/2 16-QAM) underchanging channel width (each point represents resultsfrom a single channel width). Fixed MCS gives littlefreedom in picking the desired point on the tradeoff line.With a single MCS we can achieve either good voicequality or high energy savings, but cannot balance thetwo. Even with tight PER requirements (2%), WhiteRatehalves the energy needs of BPSK, while significantlyimproving the sound quality delivered by 16-QAM, frompoor (MOS 2) to good (MOS 4). An important advantageof WhiteRate over other protocols is that the savings canbe adjusted to any point on the line depending on thepriorities: application performance or energy savings.

Spectrum Savings

WhiteRate is a cross layer protocol whose goal is tosatisfy the application level PER requirement throughphysical layer adaptation. Spectrum efficiency – the in-formation rate over a unit of bandwidth – should beanalyzed from the application’s point of view. In practi-cal terms, improvement in efficiency can be observed as

0

1

2

3

4

5

6

7

8

FDMA TDMA

Sim

ulta

neou

s V

oIP

cal

ls

Access scheme

0.1% PER threshold5% PER threshold

10% PER threshold20% PER threshold

Fig. 10. Number of supported VoIP calls withchanging PER requirements. We analyze two differentmedium access schemes: TDMA and FDMA. In bothcases, WhiteRate harnesses the varying requirements toenable a higher number of simultaneous calls.

a larger number of simultaneous conversations that canbe supported with the fixed amount of bandwidth overa fixed time interval.To quantify WhiteRate’s spectrum efficiency under

changing PER threshold we adopt two access schemes,TDMA and FDMA. In the indoor testbed we run a single500-packet VoIP flow and measure its resource usage un-der each of the schemes. The result is then averaged overfive runs and used to calculate the maximum number ofidentical G.711 VoIP flows that can be simultaneouslysupported. In the TDMA case, we use the full bandfor all transmissions and try to pack as many flows aspossible in time, while in the FDMA case each flow hasits own band of spectrum.In Figure 10 we show the number of supported VoIP

calls for PER thresholds ranging from 0.1% to 20%. Forboth the TDMA and FDMA schemes, the number of sup-ported increases with the higher loss tolerance. As a sideconsequence, the call quality drops, yet, as long as thePER remains above 10%, the resulting MOS is above 3.5,i.e. satisfactory. In our example, three times more callscan be supported with 10% than with 0.1% loss tolerance.In highly populated urban areas spectrum availability isoften critically low. With WhiteRate network adminis-trators can easily balance between the individual user’scommunication quality and the number of supportedclients in the network.

Dynamic Behavior

The PER threshold is set according to the system require-ments, which can vary over time. For example, a solarpowered wireless router does not need to be energy ef-ficient during long stretches of sunny weather. Similarly,in a cognitive radio network presence of primary usersdictates the spectrum availability. Thus, our protocolneeds to adapt to changing operating conditions.In the indoor testbed, we first examine how WhiteRate

reacts to changing PER threshold. We start a packet flowand set the PER tolerance to 5%. After 400 packets aretransmitted, we change the threshold to 20% and, 300packets later we change it to 10%. In Figure 11 we plot

13

0

10

20

30

40

50

0 200 400 600 800 1000

Pac

ket l

oss

[%]

Packet number

PER thold 5% PER thold 20% PER thold 10%

WhiteRateThird degree polynomial curve

Fig. 11. WhiteRate adapts to changing PER threshold.The figure shows average packet loss ratio for every20 packets. The first forty packets experience high lossuntil the algorithm finds a well performing [MCS, channelwidth]. The figure also shows a third order polynomialcurve fitted according to the data points.

PER for each group of 20 transmitted packets over time.When threshold is raisedWhiteRate’s aggressive probingquickly identifies a MCS, channel width combinationthat yields higher throughput. Once the threshold istightened, WhiteRate immediately, at the subsequenttransmission, adjusts MCS, channel width to values thatresult in acceptable loss. In the general case, WhiteRateadapts to a lower threshold faster than to a higherthreshold. A rigorous initial threshold usually requiresWhiteRate to explore a substantial part of the MCS,channel width space, thus the algorithm already knowswhat level of service is afforded by each of the combi-nations. In the reverse case, WhiteRate needs to explorethe combinations before any decision can be made.We next analyze how WhiteRate adapts to dynamic

spectrum availability. A packet flow is started with40kHz of available spectrum. After 300 transmitted pack-ets the maximum width is extended to 300kHz, and after300 additional packets it is decreased to 80kHz. It isimportant to note that we do not change any physicallayer parameters of the link. Rather, we restrict theOFDM subcarrier groups that WhiteRate can activate.We show the measured bitrate and throughput from a

single experiment in Figure 12. WhiteRate immediatelyadapts to the restricted spectrum availability: if it alreadyoperated on a channel width that is lower than thenew boundary, no change is necessary; otherwise, it mo-mentarily deactivates the conflicting subcarrier groups.When more bandwidth becomes available, WhiteRatecan gradually utilize it. Hence, the throughput in Fig-ure 12(b) shows gradual increase. This is a consequenceof WhiteRate’s conservative width occupation strategy:groups of subcarriers are activated one by one as longas the PER threshold is satisfied.In the reported experiment WhiteRate performs a

consecutive width occupation approach for the followingreasons: it simplifies the protocol implementation; itsatisfies the packet delivery requirement, which we seeas the most important requirement of the system; andfinally, we do not know channel gains over the whole

0

100

200

300

400

500

0 100 200 300 400 500 600 700 800 900

Bitr

ate

[Kbp

s]

Packet number

Width 40kHz Width 300kHz Width 80kHz

WhiteRateThird degree polynomial curve

(a) Bitrate.

0

100

200

300

400

500

0 100 200 300 400 500 600 700 800 900

Thr

ough

put [

Kbp

s]

Packet number

Width 40kHz Width 300kHz Width 80kHz

(b) Throughput.

Fig. 12. WhiteRate adapts to changing spectrumavailability. We modify available bandwidth from 40kHzto 300kHz and then to 80kHz.

new channel so it makes sense to be cautious aboutactivating subcarriers. However, we also envision analternative approach for dynamic and bandwidth criticalapplications: once the channel is extended a packet issent over the whole width so that channel gain infor-mation is harvested; then, subcarrier groups are orderedand a binary search is performed to quickly identify thewidest channel that supports the given PER threshold.

10 CONCLUSION

In this work we developed WhiteRate - a channel widthand modulation and coding scheme adaptation protocolthat adjusts a flexible physical layer according to theuser needs for reliable communication and availableresources such as energy and spectrum. WhiteRate isdesigned to answer to a wide variety of operating con-ditions, as demonstrated in extensive evaluation of theprotocol on both indoor as well as outdoor white spacelinks. The experimental results show that by abidingto relaxed delivery constraints, WhiteRate uses nearlya half of the energy and a third of the spectrum tosuccessfully deliver a VoIP call. In addition, compared toa standard SampleRate adaptation protocol, WhiteRateexplicitly takes communication reliability into accountand enables high quality communication in variouschannel conditions. Even more importantly, WhiteRateenables operation that balances resource utilization andcommunication quality based on the resource availabilityand user needs. Through WhiteRate, bitrate adaptationgoes beyond throughput maximization and becomes avaluable tool for wireless resource management.In this work context awareness was considered in

terms of energy reserves, spectrum availability and

14

packet error rate. Packet delay, device location and otheraspects might be more important in certain scenarios.Additionally, we exploited two major physical layerparameters: MCS and channel width. However, the SDR-enabled flexibility allows virtually endless possibilitiesfor PHY layer adaptation. From per-subcarrier trans-mission power to the length of OFDM cyclic prefix, anumber of parameters that impact resource utilizationcan be modified. Exploration of this high dimensionalityspace promises to take us closer to a truly amorphouswireless system that serves applications in the mostefficient, context-aware manner.

11 ACKNOWLEDGEMENTS

This work was funded in part through and NSF NetSEaward CNS-1064821 and a gift from Google.

REFERENCES

[1] http://www.voiptroubleshooter.com/diagnosis/emodel.html.[2] ITU World Telecommunication/ICT Indicators Database, December

2010.[3] Mobile Broadband: The Benefits of Additional Spectrum. Tech.

report, FCC, October 2010.[4] IDC Worldwide Mobile Phone 20112015 Forecast and Analysis , March

2011.[5] P. A. K. Acharya, A. Sharma, E. Belding, K. Almeroth, and

K. Papagiannaki. Congestion-Aware Rate Adaptation in WirelessNetworks: A Measurement-Driven Approach. In IEEE SECON’08,San Francisco, CA, June 2008.

[6] Atheros. Power Consumption and Energy Efficiency Compar-isons of WLAN Products, 2003.

[7] J. Bicket. Bit-rate Selection in Wireless Networks. Master Thesis,MIT, 2005.

[8] F. Boulos, D. S. Hands, B. Parrein, and P. Le Callet. Perceptual Ef-fects of Packet Loss on H.264/AVC Encoded Videos. In VPQM’09,Scottsdale, AZ, January 2009.

[9] L. N. Cai, D. Chui, M. McCutcheon, M. R. Ito, and G. W. Neufeld.Transport of MPEG-2 Video in a Routed IP Network. In IDMS’99, Toulouse, France, October 1999.

[10] R. Chandra, R. Mahajan, T. Moscibroda, R. Raghavendra, andP. Bahl. A Case for Adapting Channel Width in Wireless Net-works. In SIGCOMM’08, Oct. 2008.

[11] C. Cordeiro, K. Challapali, D. Birru, B. Manor, and S. Diego. IEEE802.22: An Introduction to the First Wireless Standard based onCognitive Radios. Journal of Communications, 1(1):38–47, 2006.

[12] A. Gudipati and S. Katti. Strider: Automatic Rate Adaptationand Collision Handling. In SIGCOMM’11, Toronto, ON, Canada,August 2011.

[13] K. Harrison, S. M. Mishra, and A. Sahai. How Much White-SpaceCapacity is There? In DySpan’10, Singapore, April 2010.

[14] J. Heiskala and J. Terry. OFDM Wireless LANs: A Theoretical andPractical Guide. SAMS, Indianapolis, IN, 2001.

[15] H. Liu, J. He, P. Cui, J. Camp, and D. Rajan. ASTRA: Applicationof Sequential Training to Rate Adaptation. In SECON’12, Seoul,Korea, June 2012.

[16] G. W. Miao, N. Himayat, and G. Y. Li. Energy-Efficient LinkAdaptation in Frequency-Selective Channels. IEEE Transactionson Communications, 58(2):545–554, 2010.

[17] G. Nychis, T. Hottelier, Z. Yang, S. Seshan, and P. Steenkiste.Enabling MAC Protocol Implementations on Software-DefinedRadios. In NSDI’09, Boston, MA, April 2009.

[18] V. Pejovic and E. Belding. Energy-Efficient Communication inNext Generation Rural-Area Wireless Networks. In CoRoNet’10,Chicago, IL, September 2010.

[19] V. Pejovic and E. Belding. A Context-aware Approach to WirelessTransmission Adaptation. In SECON11, Salt Lake City, UT, June2011.

[20] V. Pejovic, E. Belding, and M. Marina. An Energy-Flow Modelfor Self-Powered Routers and its Application for Energy-AwareRouting. In NSDR’09, Big Sky. MT, October 2009.

[21] J. G. Proakis. Digital Communications. McGraw-Hill, 2000.[22] H. Rahul, F. Edalat, D. Katabi, and C. Sodini. Frequency-aware

Rate Adaptation and MAC Protocols. In MobiCom’09, Beijing,China, Sept. 2009.

[23] M. Rodrig, C. Reis, R. Mahajan, D. Wetherall, and J. Zahorjan.Measurement-based Characterization of 802.11 in a Hotspot Set-ting. In EWIND, Philadelphia, PA, August 2005.

[24] T. Schmidl and D. Cox. Robust Frequency and Timing Syn-chronization for OFDM. IEEE Transactions on Communication,45(12):1613 – 1621, December 1997.

[25] S. Sen, S. Kole, and B. Raman. Rural Telephony: A Socio-EconomicCase Study. In ICTD’06, Bangalore, India, May 2006.

[26] S. Sen, N. Santhapuri, R. R. Choudhury, and S. Nelakuditi. Ac-curate: Constellation aware rate estimation in wireless networks.In NSDI10, San Jose, CA, April 2010.

[27] P. Shankar, T. Nadeem, J. Rosca, and L. Iftode. CARS: ContextAware Rate Selection for Vehicular Networks. In ICNP08, Or-lando, FL, October 2008.

[28] S. Surana, R. Patra, S. Nedevschi, M. Ramos, L. Subramanian,Y. Ben-David, and E. Brewer. Beyond Pilots: Keeping RuralWireless Networks Alive. In NSDI’08, San Francisco, CA, April2008.

[29] Ubiquiti Networks. XR5 Power Consumption Study. www.ubnt.net.[30] M. Vutukuru, H. Balakrishnan, and K. Jamieson. Cross-layer

Wireless Bit Rate Adaptation. In SIGCOMM, Barcelona, Spain,August 2009.

[31] S. Wong, H. Yang, S. Lu, and V. Bharghavan. Robust RateAdaptation for 802.11 Wireless Networks. In MobiCom’06, LosAngeles, CA, September 2006.

[32] L. Yang, W. Hou, L. Cao, B. Y. Zhao, and H. Zheng. SupportingDemanding Wireless Applications with Frequency-agile Radios.In NSDI’10, San Jose, CA, April 2010.

[33] M. Zafer and E. Modiano. A Calculus Approach to MinimumEnergy Transmission Policies with Quality of Service Guarantees.In INFOCOM’05, Miami, FL, April 2005.

Veljko Pejovic received a BS from the Schoolof Electrical Engineering, Belgrade, Serbia anda PhD/MS from the Department of ComputerScience, University of California Santa Barbara.Currently he is a postdoctoral Research Fellowat the School of Computer Science, Universityof Birmingham, United Kingdom. His researchis focused on adaptive and resource-efficientwireless technologies and their impact on thesociety. He is broadly interested in wireless net-works, mobile sensing systems, machine learn-

ing, technologies for development, technology and society. More detailsavailable at www.cs.bham.ac.uk/∼pejovicv.

Elizabeth M. Belding is a Professor in the De-partment of Computer Science at the Universityof California, Santa Barbara, and is currentlythe departments Vice Chair. Elizabeths researchfocuses on mobile networking, specifically mul-timedia, monitoring, advanced service support,and solutions for developing and underdevel-oped regions. Elizabeth is the author of over 100technical papers and has served on over 60 pro-gram committees for networking conferences.She is currently on the steering committee of the

ACM Networked Systems in Developing Regions (NSDR) Workshop,the editorial board of the IEEE Pervasive Magazine, and is servingas a guest editor for the IEEE Pervasive Magazine special issue onPervasive Information and Communication Technologies for DevelopingRegions. Elizabeth is the recipient of an NSF CAREER Award, and a2002 MIT Technology Review 100 award, awarded to the worlds topyoung investigators. She is an ACM Distinguished Scientist.