Title Adaptive channel selection through collaborative sensing

Author(s) Yang, QH; Zheng, H; Zhao, J; Li, VOK

Citation Ieee International Conference On Communications, 2006, v. 8, p.3753-3758

Issued Date 2006

URL http://hdl.handle.net/10722/45936

Rights

©2006 IEEE. Personal use of this material is permitted. However,permission to reprint/republish this material for advertising orpromotional purposes or for creating new collective works forresale or redistribution to servers or lists, or to reuse anycopyrighted component of this work in other works must beobtained from the IEEE.

,Ad,.aptive Cannel Selection Trough ollaborative

SensingGuang-Hua Yang*, Haitao Zheng'. Jun Zhao+ and Victor 0. K. Li*

*Dept. of Elec. & Electronic Engineering, Univ. of Hong Kong, Hong Kong, China, {ghyang, vli}l@eee.hku.hktDept. of Computer Science, Univ. of California, Santa Barbara, CA, U.S.A, htzheng@cs.ucsb.edutWireless & Networking Group, Microsoft Research Asia, Beijing, China, junzhao@microsoft.com

Abstract- Proper channel selection is essential to exploit thebenefits of multi-channel systems by distributing conflictingtransmissions across non-interfering channels. Critical to channelselection is the channel quality metric. We propose a busy timeratio (BI'R) metric that captures channel contention and usertraffic load under a variety of network dynamics. We also proposea distributed collaborative sensing scheme to reduce sensingoverhead and energy consumptions. The proposed algorithmscan be implemented using conventional 802.11 hardware withsingle radio interface. The proposed metric can be integratedwith routing and channel selection. Experimental results showthat the proposed scheme significantly outperforms the existingchannel selection methods.

Index Terms- Multiple channel, channel selection, availablebandwidth, busy time ratio

I. INTRODUCTIONA critical problem in multi-hop wireless networks is

throughput degradation due to interference among multiplesimultaneous transmissions. Multi-channel systems were intro-duced to alleviate interference by distributing interfering linksto non-interfering channels. For dense, heavily loaded systems,the number of potentially conflicting links outnumbers thenumber of channels, and a good channel selection algorithm isessential to system performance. The problem is exacerbatedby the sporadic nature of multi-hop wireless networks, inparticular node mobility, fragile links, traffic dynamics.

Critical to a channel selection scheme is the metric tocharacterize channel quality. Prior efforts have proposed touse link signal-to-noise ratio [1], or probed packet delay [2]as channel metrics. While providing a good estimation ofchannel quality for point to point links, these metrics donot accurately characterize channel contentions. The work in[3], [4], [5] approximates contention through the number ofcompeting links or traffic volume, ignoring the impact ofheterogenous traffic pattern and heterogeneous link qualityacross links. These motivate the search for a channel metricthat can properly characterize channel utilization under avariety of traffic and topology dynamics and time-varyingchannel impairments.The metric design is also constrained by the complexity

and overhead of measurement techniques. We consider adistributed multi-hop wireless network without central man-agement We assume commonly available 802 11 devices each

This research is supported in part by the Areas of Excellence Schemeestablished under the University Grants Committee of the Hong Kong Spe ialAdministrative Region, China (Project No. AoE/E-01/99). Part of this workwas done while Guanghua Yang was a visiting student in Microsoft ResearchAsia.

equipped with a half-duplex single radio interface. For thistype of network, we propose a device-centric channel selectionapproach where each device senses channel conditions andadapts its channel usage to network and traffic dynamics. Inparticular, we propose a simple, busy time ratio (BTR) basedchannel metric, and a set of collaborative sensing techniquesthat trade off complexity with accuracy. The proposed ap-proach allows each user to collect information on the qualityof multiple channels without exhaustively accessing all thechannels, making channel sensing energy-efficient. We alsoincorporate the BTR metric into routing and channel selec-tion for throughput improvement in multi-hop transmissions.Extensive experimental results demonstrate the effectivenessof the proposed metric and sensing techniques.The rest of the paper is organized as follows. In Sec. II,

we briefly introduce related work in channel quality nea-surements. In Sec. III, we present the BTR-based channelmetric and propose several collaborative sensing techniquesto estimate BTR in IEEE 802. I1 DCF systems. In Sec. V,we propose a BTR-based adaptive channel and route selectionframework. The advantages of the proposed metric and frame-work are demonstrated by experimental results in Sec. VI.Finally, we conclude in Sec. VII.

II. RELATED WORK

In this section, we briefly overview existing channel evalu-ation methods and outline the research problem.The simplest approach is to ignore channel quality and

randomly select channels [6]. Under contention fluctuationsand time-varying channel impairments, this approach is obvi-ously not optimal. The work in [2] applies channel probing,i.e., sending probing packets over the air to estimate channelbandwidth and delay. This approach may heavily stress thecommunication resources of bandwidth- or energy-constraineddevices. In addition, injecting extra traffic could result inexcessive contention and obligatory backoff, which lead tosystem throughput degradation.On the other hand on line signal based channel estimation

provides an inexpensive alternative to channel probing. Thework in [1] measures signal to interference plus noise ratio(SINR), indirectly estimating the maximum throughput a usercan get without any contention. However, it fails to accountfor the impact of user contention. The work in [3], [4]use the number of competing links N to characterize thelevel of contention. However, the estimation is subject to theassumption of homogeneous traffic which is not commonly

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observed. Other studies have shown that estimation of Nsuffers from non-negligible estimation errors [7], [8], and ahigh computational complexity [3].

The work in [5] uses the aggregated traffic served by a chan-nel to represent its quality. However, it does not consider theimpact of user-dependent channel impairments, and channelusage due to packet retransmissions. In addition, this approachassumes each user can successfully collect traffic informationfrom neighbors in close proximity, making the measurementsensitive to the reliability of message exchanging, and the levelof user cooperation.

III. Busy TIME RATIO AS CHANNEL SELECTION METRIC

Let available bandwidth Baa/il represent the maximumbandwidth a channel can provide to a new link. Intuitively,a user tends to select the channel with the highest B0 jailWe can approximate B0 V il as the difference between themaximum saturated bandwidth of the channel and the to-tal bandwidth occupied by existing links. Assuming eachdevice performs CSMA/CA [9], we can derive Baaail byestimating mean frame size (AlIFS) and eavesdropping on thenetwork allocation vector (NAV) [10]. However, the NAVinformation collected by a device only accounts for the linktransmissions within a device s transmission range. Instead,channel quality depends on the level of transmissions withina node s interference range. In general the interference rangeis much larger than the transmission range, e.g. by a factor of2. Hence, NAV-based available bandwidth under-estimateschannel usage.We propose to replace NAV with busy time ratio (BTR).

BTR is defined as the total time that the physical channel isbusy normalized by the measurement time,

BTRTotal busy time

Total measurement time (1)

Following a set of derivations similar to [10], we can deriveBav/il from BTR and AIFS by solving a set of nonlinearequations. To verify the accuracy of the proposed approach, weperform a set of experiments comparing the analytical resultsto the actual measurements. We randomly set up a set of linksin a given area, each carrying random traffic with the samepacket size but different packet rate. We analytically computeB,,,a,il based on the measured BTR at each niode. Ini Fig. 1,this derivation is compared to the actual throughput each linkobtains. We see that the estimation is fairly accurate underfixed frame size AIFS. There exists a consistent linear relationbetween Bavail and BTR for a fixed MFS.

While algorithms exist to estimate Ba ail from the observedAMRS and BTR, they are computationally complex [10] andmay heavily stress the energy constrained devices Motivatedby the consistent linear trend in Fig 1, we propose to directlyuse BTR as the channel metric. The same figure also suggeststhat \AIFS can heavily impact the available bandwidth fora given BTR. However, good estimation of -NIFS requiresexcessive sensing and reliable decoding of all packets, whichis infeasible for energy-constrained devices. As a result, weignore the impact of V FS, trading complexity with accuracy.We examine the related estimation error in Sec. VI-B, whichconfirms that the imipact is small in the simulatekd networks.

3.6 ll3

Simulated, size = 64 bytes3.2 - - Analytical, size = 64 bytes

\ 1-Simulated, size = 256 bytesQ2.8 Analytical, size = 256 bytesQ0 -v- Simulated, size = 1024 bytes;!~2.4 -v- Analytical, size = 1024 bytes

*§2.01 .6

(D1 .2

>0.8-

0.4

o0.2 0.4

BTR0.6 0.8

Fig. 1. Comparison of Bavail estimated on MIFS and BTR with thatobtained from simulations. MFS = 64, 256, 1024 bytes respectively.

While the proposed BTR metric focuses on characterizinguser contentions, it also captures variations in user link quality.A user experiencing bad connections consumes additional timeby using lower rates and extra retransmissions. In this work,we assume that each user experiences statistically uniformimpairments (path loss, shadowing and fading) across all thechannels; i.e. the impairments are frequency non-selective.Therefore, BTR directly indicates the channel usage andthe available bandwidth for each new link. For time-varyingfrequency-selective fading, we can extend BTR to account foraverage channel signal-to-noise ratio by periodically probingeach channel. Overall, BTR provides a good approximationof channel quality, taking into account the impact of usercontentions, traffic heterogeneity, transmission failures andretransmissions.

IV. COLLABORATIVE BTR MEASUREMENTIn this section, we provide a measurement scheme that

allows each user to observe BTRs of multiple channels with-out having to sense each channel individually. In particular,each device performs local measurement on the channel it iscurrently using, and utilizes a collaborative sensing scheme toaccumulate measurements on other channels.

A. Local BTR SensingEach device measures the BTR of the channel it is cur-

rently using. There are two measuring schemes that trade offcomplexity with precision.a) Physical measurementThis scheme invokes the carrier-sensing module to measurethe power level of the received signal When the power levelexceeds a pre defined threshold the channel is busy Giventhe carrier-sensing module is built-in, this scheme is simple toimplement However since sensing consumes similar poweras transmissions, this leads to excessive energy consumption.We refer to the physically measured BTR as BTRPHYb) Vrtual measurementTo reduce energy consumption due to carrier sensing, eachnode can estimate BTR by eavesdropping on MAC controlmessages. In particular, AV information ermbedded in MAC

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frame headers approximates the duration of the current trans-mission. Each device can obtain a good estimation of BTRby accumulating self-transmission time, and NAV-specifiedneighboring links' transmission time as well as protocol-specific overhead. This measurement significantly improvesenergy efficiency - during the time declared by neighbor'sNAV signals, a device can configure its radio to a low-power dozing mode [11]. We refer to the measured BTR asBTRAIAC

Fig. 2 illustrates the time-line of both measurementschemes. The busy/idle states represent physical sensingresults of Node I using physical measurement. The NAV-related blocks represent the channel busy durations estimatedat Nodes 1-6 by using virtual sensing. It should be noted thatvirtual measurement requires successful decoding of NAVsignals, and can therefore only detect transmissions withinits transmission range, rather than interference range. Thisleads to under-estimation of channel usage. Next we proposea collaboration-based approach to overcome this problem.

B. Collaborative SensingWhile virtual sensing scheme provides an energy-efficient

alternative to physical sensing, its estimation accuracy islimited by the difference between the interference range andthe transmission range. In addition, the sensing complexityand overhead scales linearly with the number of channels. Inthis section, we propose a collaborative sensing scheme wheredevices can obtain an accurate estimation of BTR of multiplechannels without physically scanning and carrier-sensing all ofthem. The scheme is motivated by the fact that users in closeproximity observe similar channel usage.

The detailed procedure is as follows. Each device conductsvirtual sensing on its current channel i in use, to obtain a self-observed BTRSIAC(i). Each device periodically broadcaststhis measurement to its k-hop neighbors, where

k Interference Range-Transmitssion Range-

The BTR broadcast can be embedded into regular controlmessages, communicated through a predefined coordinationchannel, or during a synchronized coordination time slot [4],[14]. Each device collects the BTR broadcasts and recordsthem in BTRIAC(.) for each channel. Utilizing self andneighbor BTR measurements, each device updates its BTRestimation as follows:a) For its current channel i in use,

BTR(i) max(BTRSAC (t) max(BTR IAC(i))) (2)b) For other channels j ( i)

BTR(j) max(BTRN ) (3)j mx AfAC A))The proposed collaborative sensing provides a fair level

of robustness against loss of BTR broadcast packets. Priorwork [5] estimates the traffic volume on each channel byaccumulating the traffic information from each individual user.Any packet loss will lead to inaccurate estimation. For theproposed approach, BTR is a local measure, and thus thecontents of the BTR broadcast each device receives is highcorrelated. The BTR estimation suffers only if the broadcast

message from the user observing the highest BTR is lost.Such redundancy provides robustness to packet loss. We willexamine this property through experimental results in Sec. VI-D.

V. BTR-BASED CHANNEL AND ROUTING ASSIGNMENTIn this section, we incorporate BTR-based channel metric

into channel and route selection for multi-channel ad hocnetworks. We start from single hop transmissions where linkpairs use BTR to negotiate a data channel. We then showthat for multi-hop transmissions, BTR can be integrated intorouting decisions to discover the best route and coordinatechannel usage along the route. For simplicity, we assume thatthe MAC protocol provides a control channel or time framefor users to exchange negotiation information.

A. Adaptive Channel Selection for Single-hop TransmissionIn fully connected networks, any two nodes observe the

same channel status, and thus the same BTR. Hence, one nodecan decide the best channel to use based on self-observations.For multi-hop networks, two ends of the transmission mayobserve different BTR on each channel, and need to negotiatethe data channel selection. We propose to combine the BTRof a link pair (node u and v) as follows:

BTR(n) = max{BTRu (n), BTR, (T)} (4)where nu is the chaninel index. Theni the channel selectionis reduced to finding the channel with the lowest combinedBTR, i.e.,

n = argmMir BTR(n). (5)Before a pair of users u and v start communications, both

perform collaborative sensing to collect BTRs, and selectthe best channel according to (5). During transmissions, theycontinue to collect BTRs and adapt their channel selection tonetwork dynamics. The decision of channel switching needsto account for not only the BTRs on other channels, butalso the impact of traffic variations by moving self-trafficto the new channel. Additional mechanisms are required toavoid concurrent switching where a few node pairs (in closeproximity) observe a channel with low BTR and switch toit concurrently. We propose a random delay based approachwhere a node pair defers its channel switch by a random timeinterval. During this period, they keep sensing the channeland if a substantial increase in BTR is detected, the switchis canceled.

Fig. 3 illustrates an example network with two channels. Aline exists between two nodes if they are within transmissionrange of each other. For illustration, we only show the fivenodes in the middle of the network. The left table showsthe BTRs of both channels observed by each node (throughcollaborative virtual sensing). When Node 1 and Node 2 wantto start a transmission, they select channel 2 following (5).

B. Route and Channel Selection in Multi-hop NetworksThe proposed BTR metric can be integrated with route

selection for multi-hop transmissions. We use a simple routingand channel usage strategy to illustrate the usage of BTR. Weassume that the nodes on each route use the same channel to

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Observation Interval -- I_

,

I

Node I NAV F random gNode1 ~~~~Sbackoff

--j -I - - - - --j-2

S D IRTSII II I I

FS DATA F |new random Defers, but kee backoff counterS backoff NAV upon receiving RTS _ _ on RTS

Node 2 NA S NA pon receiving RTS r NAVNAV K N~V uIonCreceiving Ron RTS NVo T

D SF F N o

NV F |random Defers, but keeps backoff counter F remaining R F DATAI I NAV I NAVRTSNode 3 NV S |backoff; Aupon receiving RTS |S |backoff RT S DAT NAVl o RS onRT

NAV NAV upon receiving RTS A N NAV on RTS

II IS SC SI

F II

Node 5 NAV upon receiving RTS NAV upon receiving RTS C_ SiNode 6 -~~~4~~ I I ~NAVupon receivingCTS NAV D TNode 6 T)A4lA A pnrciigCS| ,|NAV UnOn receiving RTS NV| D IRTSII.

11I on RTS I [ IFS

(Not to scale)

Fig. 2. BTR measurement in IEEE 802.11 DCF.

Channel selection for transmission 1-2

Selection metric CH1 CH2 DecisionBTRmx 0.2 0.3 CH1BTRT, 0.5 0.4 CH2

max(BTRTX, BTRRx) 0.5 0.4 CH2The channel with max(BTRTX, BTYRx) is the

selection. So CH2 is selected

5

Path and channel selection for transmission 1-5

Path1: 1-2-5 Path2: 1-3-53W Hop CH1 CH2 Hop CH1 CH2

1-2 0.5 0.4 1-3 0.5 0.72-5 0.6 0.4 3-5 0.5 0.7

1 2-5 0.6 0.4 1-3--5 0.5 0.7

The hop with the max (BTRTx, BTRRx) is thebottleneck. So pathl with CH2 is preferred4

Fig. 3. An example of BTR-based channel and route selection.

TABLE IMAIN SYSTEM PARAMETERS.

Description alueBasic rate 2 AVlbpsData rate I1 VIbpsSlot time 20 usSIFS 10 usDIFS 50 us

PHY header 144 bits + 48 bitsMAC header 224 bits

RTS 160 bits + PHY headerCTS 112 bits + PHY headerACK 112 bits + PHY headerDATA Data length + MAC header + PHY header

Carrier sensing range 500 mTransmitting range 250 mCBR packet length Uniformly distributed in [32, 1024] bytesCBR packet rate Uniformly distributed in [1, 50] packets/sec

Active link number n Uniformly distributed in 30

avoid frequent channel switches. Using the example in Fig. 3,we illustrate the procedures to select the routing path and thechannel to use on each hop for a transmission from Node 1

to Node 5. In this case, there are two candidate routing paths.Each path selects the channel that minimizes the path BTR,defined as the maximum BTR of all the hops on the path.The bottom right table in the same figure illustrates the BTRof different hops. In this case, path 2 5 with channel

2 has the minimum path BTR, and thus is selected as theroute.

VI. PERFORMANCE EVALUATIONIn this section, we conduct experimental simulations to

evaluate the performance of the proposed channel selectionschemes. We start from a fully connected network and com-

pare the performance of different channel metrics. We then

consider general multi-hop networks, and examine the effec-tiveness of the collaborative virtual sensing, and the robustnessof different metrics to broadcast errors. Finally, we comparethe performance of different metrics in multi-hop networks.

uted MAC (HD-MAC) [14]. In HD-MAC, the transmissionsare based on time-frame, which is composed of a short-periodcoordination time slot and a long-period data transmissiontime slot. In the coordination time slot, users switch to a pre-defined coordination channel to exchange BTR informationand negotiate the data channel to be used in the following datatransmission time slot. Then in the data transmission time slot,users switch to the selected data channel for data transmission.Related main MAC layer parameters and the traffic parametersused in the simulation are summarized in Table I.

We examine the performance of different channel metrics,

including the number of co,ntending links N, aggregatedthroughput E Thru, BTR and the estimated available band-width B0 VOil. All nodes have the same transmission range of

250 m and interference range of 500 m. For simplicity we

assume that each user experiences the same average signal to

noise ratio on each channel.

We use the decsion correctness to measure the accuracy"of the channel metrics. Decision correctness is defined as

A. Simulaon Setup

We extend NS-2 [12] with CMU wireless extensions [13] toinclude a multi-channel MAC protocol, heterogeneous distrib-

dccisiorn correctnessNumber of correct selectionsTotal nUTber of selectionts

A channel selection decision is "correct" if it leads to a larger

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. . I1_,

CTI

[NAV on

|CTSI

I (node 6 is hidden to node 1)|

Time

A multi-channel systemwith CH1 and CH2

ID CH1 CH,1 0.5 0.42 0.2 0.33 0.5 0.74 0.5 0.7 CH5 0.6 0.4

.A

14--busy-W*--idle 004 bus 04 idle 104 bus 04 idle*.busv--O,-. idle 0,4 -busy-*.4-idle-PH

overall system throughput than any other selection.

B. Comparison of Channel Selection MetricsWe start from a fully connected network where users

observe the same contention on each channel. We randomlydeploy users with traffic in a lOOTrm x lOOTrm area. In eachinstance, a pair of nodes start communications and negotiate achannel to use, while a random number of CBR flows with ran-dom traffic exist on all the channels. The characteristics of theCBR flows are also listed in Table I. Table II summarizes theresults using different metrics, averaged over 3000 instances,and assuming two channels in the system. For comparison,the decision correctness, the system throughput and the linkthroughput of the newly joined link are listed in Table II.We also include a normalized throughput measure (usingthe contending link number-based metric as the baseline) toillustrate the relative difference of different metrics.The results show that the Bavao -based channel selection

yields the best performance, especially for the new link (67%improvement compared to N-based scheme). BTR-basedmetric leads to a slightly lower throughput (10% degradation)but outperforms the other two metrics, especially metric N.Performance degradation of the metric EThru is due toignoring of channel resource consumption from user con-tention and packet retransmissions. The decision errors ofmetric BTR are mainly due to ignoring NIFS. The resultsindicates that ignoring the effects of MFS leads to moderateperformance degradation. Overall, we see that BTR providesan accurate but low cost channel quality ranker. Note that evenfor B, ail decision errors exist. This is mainly due to errorsin MIFS estimation, and to neglecting MIFS variations dueto the newly joined traffics.

C. Effectiveness of Collaborative BTR Virtual SensingThe proposed collaborative virtual sensing requires informa-

tion from k-hop neighbors to estimate BTR. In this section,we examine the effectiveness of collaborative virtual sensingwhen neighbors up to 2 hops away are involved. We randomlydeploy 10 node pairs in a 500rrn x 500Tn area, each carryinga UDP flow with CBR traffic of 512 byteslpacket and30 packets sec. The results are averaged over 3000 indepen-dent deployments. Fig. 4 compares the self-observed physicalBTR BTRPHY, self-observed virtual BTR BTRA AC, andthe BTR estimated based on 2 hop collaborative virtual sens-ing. From the figure, we find that the self-observed BTRA ACis in general lower than the self-observed BTRpHy, dueto the difference in transmission and interference ranges. Inaddition, collaborative virtual sensing leads to a good estimiateof the BTIRPHY-

D Robustness to Broadcast ErrorsResults in the previous section show that when broadcast is

error-free, E Thru metric performs only slightly worse thanBTR metric. Next, we examine the robustness of the twometrics against broadcasting errors in multi-hop environments.Broadcast message transmission is conducted between anytwo nodes within transmission range. Broadcasting error rate2BER) is used to indicat the error level. It is defined as the

0 0.5 120 BTR

200 Max (2hop BTRMAC)

100

00 0.5 1

BTR

0.9

08

0.7-

0.6

.05 .

0.4-

0.3

0.2-Self BTRPHY

0.1 - Self BTRMACMax (2hop BTRMAC)

00 0.5

BTR

Fig. 4. Approximation of BTRPHY with 2-hop neighbors' BTRMAC.Sub-figures on the left from top to botton are the histograms of self-observed BTRPHY, self-observed BTRVIAC and max(2hop BTRIAC)respectively; the sub-figure on the right plots the corresponding CDFs.

]uu

80

700)a) 600)

a)

50-I0

c: 40uo 30

20

10 _

0 _0

BTRAggregated Throughput

0.2 0.4 0.6 0.8Broadcast Error Rate (*100%)

Fig. 5. Decision correctness of BTR and E Thru based schemes underdifferent broadcast error rates.

ratio of the number of failed broadcast message transmissionsand the total number of broadcasting messages. For example,a broadcast message sent by one node to its 10 neighborsis counted as 10 broadcast messages, and if 3 of the 10transmissions fail, then the BER is 0.3.We randomly deploy users in a 500m x 500m area, and

measure the decision correctness under different BER. Re-sults in Fig. 5 show that the decision correctness degradeswith BER, but BTR- based metric offers higher robustness.This is due to the correlation in neighboring users' BTR mea-surements - since BTR measurements taking into account thecontributions of contending users in the neighborhood, usersin close proximity are likely to report similar BTR on eachchannel. Such correlations can be utilized by collaborativesensing to mitigate the effect of broadcast errors. In contrast,correct estimation of Thru requires reliable reception ofbroadcasts from neighbors.

It should be noted that users can also measure BTRby physically observing each of the channels sequentially.This eliminates the dependency on message exchanging andneighbor cooperation, at the costs of a higher device power

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1

TABLE IIPERFORMANCE COMPARISON OF DI1FFERENT CHANNEI METRICS.

Absolute Thru.of New Link402 Kbps568 Kbps611 Kbps671 Kbps

Normalized Thru.of New Link

100.0%141.3%152.0%166.9%

measure BTR across multiple channels with minimum costand robustness to packet losses. Experimental results showthat the proposed scheme achieves significant performanceimprovements. The proposed algorithms can be implementedusing conventional 802.11 hardware with single half-duplexradio interface.

REFERENCES

120

10

BTRAggregated Throughput

0.2 0.4 0.6 0.8Broadcast Error Rate (*100%)

Fig. 6. Delay comparison of BTR and E Thru under different broadcasterror rates.

consumption and measurement overhead. The other metricslike N and E Thru always rely on the cooperation ofneighboring users.

E. Route and Channel Selection in Multi-hop NetworksIn this part, we examine the performance of E Thra and

BTR -based schemes in multi-hop networks. We randomlydeploy 20 node pairs in a 1000m x lOOOm square area, usingone of the two channels to communicate. The maximum hopdistance required for any two distinct nodes to communicate is2. Assuming each node pair carries exponential on/off traffic,we measure the average end-to-end delay under differentBERs. If no neighbor information is successfully received,the latest valid self-observed result is used instead for channelselection. Simulation results in Fig. 6 show that the proposedBTR-based scheme significantly outperforms the E Thru-based scheme, particularly under high broadcast errors.

VII. CONCLUSIONIn this paper we propose a new BTR-based channel

quality metric and a distributed scheme to collaboratively

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[9] IEEE 802.11 Working Group, "Wireless LAN Medium Access Control(MAC) and Physical Layer (PHY) Specifications," Jun. 1999.

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This full textpaper was peer reviewed at the direction ofIEEE Communications Society subject matter expertsfor publication in the IEEE ICC 2006proceedings.

Metric

E Thrut.BTRBavail

DecisionCorrectness

71.20c92.5%t93. 1o94.3%7

AbsoluteSys. Thru.3.85 M..bps4.01 MIbps4.05 Mfbps4.11 Mfbps

NormalizedSys. Thru.100.0%o104.2%105.26106.8%7

170

160

F 150E

r 1400)

>130-

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